fix setup

Former-commit-id: 7d3e7db46a5f8672dd57fa5fcc03822e175047f9
fix llava rlhf
2024-04-28 03:49:13 +08:00 · 2024-04-28 03:01:49 +08:00 · 2024-04-28 01:50:30 +08:00 · 2024-04-26 23:39:19 +08:00 · 2024-04-26 23:18:00 +08:00 · 2024-04-26 20:09:14 +08:00
98 changed files with 2540 additions and 962 deletions
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
 [![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
 [![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
-[![Citation](https://img.shields.io/badge/citation-28-green)](#projects-using-llama-factory)
+[![Citation](https://img.shields.io/badge/citation-34-green)](#projects-using-llama-factory)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
 [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@@ -43,10 +43,10 @@ Choose your path:

 ## Features

- **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO.
+- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
+- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO and ORPO.
 - **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
- **Advanced algorithms**: GaLore, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning.
+- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning.
 - **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
 - **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
 - **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
@@ -68,14 +68,24 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/

 ## Changelog

+[24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See `examples/lora_single_gpu/sft_mllm.sh` for usage.
+
+[24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
+
+[24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See `examples/extras/mod` for usage.
+
+[24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)**. See `examples/extras/badam` for usage.
+
+[24/04/16] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves **117%** speed and **50%** memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison).
+
+<details><summary>Full Changelog</summary>
+
 [24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See `examples/lora_single_gpu` for usage.

 [24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv!

 [24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See `examples/extras/fsdp_qlora` for usage.

-<details><summary>Full Changelog</summary>
-
 [24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See `examples/extras/loraplus` for usage.

 [24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. See `examples/extras/galore` for usage.
@@ -102,7 +112,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/

 [23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models.

-[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
+[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.

 [23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings.

@@ -126,32 +136,38 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/

 ## Supported Models

-| Model                                                    | Model size                  | Default module    | Template  |
-| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
-| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                      | W_pack            | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [ChatGLM3](https://huggingface.co/THUDM/chatglm3-6b)     | 6B                          | query_key_value   | chatglm3  |
-| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B                  | q_proj,v_proj     | deepseek  |
-| [Falcon](https://huggingface.co/tiiuae)                  | 7B/40B/180B                 | query_key_value   | falcon    |
-| [Gemma](https://huggingface.co/google)                   | 2B/7B                       | q_proj,v_proj     | gemma     |
-| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                      | wqkv              | intern2   |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2    |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B                     | q_proj,v_proj     | mistral   |
-| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                       | att_proj          | olmo      |
-| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                   | q_proj,v_proj     | -         |
-| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B             | c_attn            | qwen      |
-| [Qwen1.5 (MoE)](https://huggingface.co/Qwen)             | 0.5B/1.8B/4B/7B/14B/32B/72B | q_proj,v_proj     | qwen      |
-| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                   | q_proj,v_proj     | -         |
-| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                  | q_proj,v_proj     | xverse    |
-| [Yi](https://huggingface.co/01-ai)                       | 6B/9B/34B                   | q_proj,v_proj     | yi        |
-| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                 | q_proj,v_proj     | yuan      |
+| Model                                                    | Model size                       | Default module    | Template  |
+| -------------------------------------------------------- | -------------------------------- | ----------------- | --------- |
+| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | W_pack            | baichuan2 |
+| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | query_key_value   | chatglm3  |
+| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | q_proj,v_proj     | cohere    |
+| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B                       | q_proj,v_proj     | deepseek  |
+| [Falcon](https://huggingface.co/tiiuae)                  | 7B/40B/180B                      | query_key_value   | falcon    |
+| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | q_proj,v_proj     | gemma     |
+| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | wqkv              | intern2   |
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | q_proj,v_proj     | -         |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | q_proj,v_proj     | llama2    |
+| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | q_proj,v_proj     | llama3    |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | q_proj,v_proj     | vicuna    |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | q_proj,v_proj     | mistral   |
+| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | q_proj,v_proj     | -         |
+| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | q_proj,v_proj     | -         |
+| [Phi-3](https://huggingface.co/microsoft)                | 3.8B                             | qkv_proj          | phi       |
+| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | c_attn            | qwen      |
+| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj     | qwen      |
+| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | q_proj,v_proj     | -         |
+| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | q_proj,v_proj     | xverse    |
+| [Yi](https://huggingface.co/01-ai)                       | 6B/9B/34B                        | q_proj,v_proj     | yi        |
+| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | q_proj,v_proj     | yuan      |

 > [!NOTE]
-> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules.
+> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules for better convergence.
 >
-> For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "chat" models.
+> For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "instruct/chat" models.
+>
+> Remember to use the **SAME** template in training and inference.

 Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported.

@@ -222,6 +238,7 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
 - [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
 - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
+- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
 - [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
 - [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
 - [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
@@ -241,6 +258,7 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
 - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
+- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
 - [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)

 </details>
@@ -275,16 +293,15 @@ huggingface-cli login

 \* *estimated*

-| Method | Bits |   7B  |  13B  |  30B  |   70B  |   8x7B |
-| ------ | ---- | ----- | ----- | ----- | ------ | ------ |
-| Full   | AMP  | 120GB | 240GB | 600GB | 1200GB |  900GB |
-| Full   |  16  |  60GB | 120GB | 300GB |  600GB |  400GB |
-| GaLore |  16  |  16GB |  32GB |  64GB |  160GB |  120GB |
-| Freeze |  16  |  20GB |  40GB |  80GB |  200GB |  160GB |
-| LoRA   |  16  |  16GB |  32GB |  64GB |  160GB |  120GB |
-| QLoRA  |   8  |  10GB |  20GB |  40GB |   80GB |   60GB |
-| QLoRA  |   4  |   6GB |  12GB |  24GB |   48GB |   30GB |
-| QLoRA  |   2  |   4GB |   8GB |  16GB |   24GB |   18GB |
+| Method            | Bits |   7B  |  13B  |  30B  |   70B  |  110B  |  8x7B |  8x22B |
+| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
+| Full              | AMP  | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
+| Full              |  16  |  60GB | 120GB | 300GB |  600GB |  900GB | 400GB | 1200GB |
+| Freeze            |  16  |  20GB |  40GB |  80GB |  200GB |  360GB | 160GB |  400GB |
+| LoRA/GaLore/BAdam |  16  |  16GB |  32GB |  64GB |  160GB |  240GB | 120GB |  320GB |
+| QLoRA             |   8  |  10GB |  20GB |  40GB |   80GB |  140GB |  60GB |  160GB |
+| QLoRA             |   4  |   6GB |  12GB |  24GB |   48GB |   72GB |  30GB |   96GB |
+| QLoRA             |   2  |   4GB |   8GB |  16GB |   24GB |   48GB |  18GB |   48GB |

 ## Getting Started

@@ -305,7 +322,7 @@ cd LLaMA-Factory
 pip install -e .[metrics]
 ```

-Extra dependencies available: deepspeed, metrics, unsloth, galore, vllm, bitsandbytes, gptq, awq, aqlm, qwen, modelscope, quality
+Extra dependencies available: deepspeed, metrics, galore, badam, vllm, bitsandbytes, gptq, awq, aqlm, qwen, modelscope, quality

 <details><summary>For Windows users</summary>

@@ -319,7 +336,7 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec

 </details>

-### LLaMA Board GUI
+### Train with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))

 > [!IMPORTANT]
 > LLaMA Board GUI only supports training on a single GPU, please use [CLI](#command-line-interface) for distributed training.
@@ -328,9 +345,20 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec

 ```bash
 export CUDA_VISIBLE_DEVICES=0 # `set CUDA_VISIBLE_DEVICES=0` for Windows
+export GRADIO_SERVER_PORT=7860 # `set GRADIO_SERVER_PORT=7860` for Windows
 python src/train_web.py # or python -m llmtuner.webui.interface
 ```

+<details><summary>For Alibaba Cloud users</summary>
+
+If you encountered display problems in LLaMA Board on Alibaba Cloud, try using the following command to set environment variables before starting LLaMA Board:
+
+```bash
+export GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/
+```
+
+</details>
+
 #### Use Docker

 ```bash
@@ -360,7 +388,7 @@ docker compose -f ./docker-compose.yml up -d

 </details>

-### Command Line Interface
+### Train with Command Line Interface

 See [examples/README.md](examples/README.md) for usage.

@@ -370,13 +398,13 @@ Use `python src/train_bash.py -h` to display arguments description.

 ```bash
 CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 python src/api_demo.py \
-    --model_name_or_path mistralai/Mistral-7B-Instruct-v0.2 \
-    --template mistral \
+    --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
+    --template llama3 \
    --infer_backend vllm \
    --vllm_enforce_eager
 ```

-### Use ModelScope Hub
+### Download from ModelScope Hub

 If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.

@@ -384,7 +412,7 @@ If you have trouble with downloading models and datasets from Hugging Face, you
 export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
 ```

-Train the model by specifying a model ID of the ModelScope Hub as the `--model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `modelscope/Llama-2-7b-ms`.
+Train the model by specifying a model ID of the ModelScope Hub as the `--model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.

 ## Projects using LLaMA Factory

@@ -413,8 +441,14 @@ If you have a project that should be incorporated, please contact via email or c
 1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
 1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
 1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
+1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
 1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
 1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
+1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
+1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
+1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
+1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
+1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
 1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
 1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
 1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
@@ -427,7 +461,7 @@ If you have a project that should be incorporated, please contact via email or c

 This repository is licensed under the [Apache-2.0 License](LICENSE).

-Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
+Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2/LLaVA-1.5](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)

 ## Citation

--- a/README_zh.md
+++ b/README_zh.md
@@ -5,13 +5,13 @@
 [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
 [![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
 [![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
-[![Citation](https://img.shields.io/badge/citation-28-green)](#使用了-llama-factory-的项目)
+[![Citation](https://img.shields.io/badge/citation-34-green)](#使用了-llama-factory-的项目)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
 [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
 [![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
 [![Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
 [![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)

 👋 加入我们的[微信群](assets/wechat.jpg)。

@@ -23,7 +23,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd

 选择你的打开方式：

- **Colab**：https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
+- **Colab**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
 - **本地机器**：请见[如何使用](#如何使用)

 ## 目录
@@ -43,10 +43,10 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd

 ## 项目特色

- **多种模型**：LLaMA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、指令监督微调、奖励模型训练、PPO 训练、DPO 训练和 ORPO 训练。
+- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
+- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练和 ORPO 训练。
 - **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
- **先进算法**：GaLore、DoRA、LongLoRA、LLaMA Pro、LoRA+、LoftQ 和 Agent 微调。
+- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
 - **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
 - **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
 - **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
@@ -68,14 +68,24 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd

 ## 更新日志

+[24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 `examples/lora_single_gpu/sft_mllm.sh`。
+
+[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 **[Colab 笔记本](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型，详情请见 [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) 和 [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese)。
+
+[24/04/21] 我们基于 [AstraMindAI 的仓库](https://github.com/astramind-ai/Mixture-of-depths)支持了 **[混合深度训练](https://arxiv.org/abs/2404.02258)**。详细用法请参照 `examples/extras/mod`。
+
+[24/04/16] 我们支持了 **[BAdam](https://arxiv.org/abs/2404.02827)**。详细用法请参照 `examples/extras/badam`。
+
+[24/04/16] 我们支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的长序列训练（24GB 可训练 Llama-2-7B-56k）。该方法相比 FlashAttention-2 提供了 **117%** 的训练速度和 **50%** 的显存节约。更多数据请见[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
+
+<details><summary>展开日志</summary>
+
 [24/03/31] 我们支持了 **[ORPO](https://arxiv.org/abs/2403.07691)**。详细用法请参照 `examples/lora_single_gpu`。

 [24/03/21] 我们的论文 "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" 可在 arXiv 上查看！

 [24/03/20] 我们支持了能在 2x24GB GPU 上微调 70B 模型的 **FSDP+QLoRA**。详细用法请参照 `examples/extras/fsdp_qlora`。

-<details><summary>展开日志</summary>
-
 [24/03/13] 我们支持了 **[LoRA+](https://arxiv.org/abs/2402.12354)**。详细用法请参照 `examples/extras/loraplus`。

 [24/03/07] 我们支持了梯度低秩投影（**[GaLore](https://arxiv.org/abs/2403.03507)**）算法。详细用法请参照 `examples/extras/galore`。
@@ -102,7 +112,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd

 [23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。

-[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn` 参数以启用 FlashAttention-2。
+[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn fa2` 参数以启用 FlashAttention-2。

 [23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。

@@ -126,32 +136,38 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd

 ## 模型

-| 模型名                                                   | 模型大小                     | 默认模块           | Template  |
-| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
-| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                      | W_pack            | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [ChatGLM3](https://huggingface.co/THUDM/chatglm3-6b)     | 6B                          | query_key_value   | chatglm3  |
-| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B                  | q_proj,v_proj     | deepseek  |
-| [Falcon](https://huggingface.co/tiiuae)                  | 7B/40B/180B                 | query_key_value   | falcon    |
-| [Gemma](https://huggingface.co/google)                   | 2B/7B                       | q_proj,v_proj     | gemma     |
-| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                      | wqkv              | intern2   |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2    |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B                     | q_proj,v_proj     | mistral   |
-| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                       | att_proj          | olmo      |
-| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                   | q_proj,v_proj     | -         |
-| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B             | c_attn            | qwen      |
-| [Qwen1.5 (MoE)](https://huggingface.co/Qwen)             | 0.5B/1.8B/4B/7B/14B/32B/72B | q_proj,v_proj     | qwen      |
-| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                   | q_proj,v_proj     | -         |
-| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                  | q_proj,v_proj     | xverse    |
-| [Yi](https://huggingface.co/01-ai)                       | 6B/9B/34B                   | q_proj,v_proj     | yi        |
-| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                 | q_proj,v_proj     | yuan      |
+| 模型名                                                   | 模型大小                          | 默认模块           | Template  |
+| -------------------------------------------------------- | -------------------------------- | ----------------- | --------- |
+| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | W_pack            | baichuan2 |
+| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | query_key_value   | -         |
+| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | query_key_value   | chatglm3  |
+| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | q_proj,v_proj     | cohere    |
+| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B                       | q_proj,v_proj     | deepseek  |
+| [Falcon](https://huggingface.co/tiiuae)                  | 7B/40B/180B                      | query_key_value   | falcon    |
+| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | q_proj,v_proj     | gemma     |
+| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | wqkv              | intern2   |
+| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | q_proj,v_proj     | -         |
+| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | q_proj,v_proj     | llama2    |
+| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | q_proj,v_proj     | llama3    |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | q_proj,v_proj     | vicuna    |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | q_proj,v_proj     | mistral   |
+| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | q_proj,v_proj     | -         |
+| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | q_proj,v_proj     | -         |
+| [Phi-3](https://huggingface.co/microsoft)                | 3.8B                             | qkv_proj          | phi       |
+| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | c_attn            | qwen      |
+| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj     | qwen      |
+| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | q_proj,v_proj     | -         |
+| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | q_proj,v_proj     | xverse    |
+| [Yi](https://huggingface.co/01-ai)                       | 6B/9B/34B                        | q_proj,v_proj     | yi        |
+| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | q_proj,v_proj     | yuan      |

 > [!NOTE]
-> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块。
+> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块以得到更好的效果。
 >
-> 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Chat）模型请务必使用**对应的模板**。
+> 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Instruct/Chat）模型请务必使用**对应的模板**。
+>
+> 请务必在训练和推理时使用**完全一致**的模板。

 项目所支持模型的完整列表请参阅 [constants.py](src/llmtuner/extras/constants.py)。

@@ -222,6 +238,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
 - [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
 - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
+- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
 - [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
 - [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
 - [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
@@ -241,6 +258,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
+- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
 - [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)

 </details>
@@ -275,16 +293,15 @@ huggingface-cli login

 \* *估算值*

-| 训练方法 | 精度 |   7B  |  13B  |  30B  |   70B  |   8x7B |
-| ------- | ---- | ----- | ----- | ----- | ------ | ------ |
-| 全参数   | AMP  | 120GB | 240GB | 600GB | 1200GB |  900GB |
-| 全参数   |  16  |  60GB | 120GB | 300GB |  600GB |  400GB |
-| GaLore  |  16  |  16GB |  32GB |  64GB |  160GB |  120GB |
-| 部分参数 |  16  |  20GB |  40GB |  80GB |  200GB |  160GB |
-| LoRA    |  16  |  16GB |  32GB |  64GB |  160GB |  120GB |
-| QLoRA   |   8  |  10GB |  20GB |  40GB |   80GB |   60GB |
-| QLoRA   |   4  |   6GB |  12GB |  24GB |   48GB |   30GB |
-| QLoRA   |   2  |   4GB |   8GB |  16GB |   24GB |   18GB |
+| 方法               | 精度 |   7B  |  13B  |  30B  |   70B  |  110B  |  8x7B |  8x22B |
+| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
+| Full              | AMP  | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
+| Full              |  16  |  60GB | 120GB | 300GB |  600GB |  900GB | 400GB | 1200GB |
+| Freeze            |  16  |  20GB |  40GB |  80GB |  200GB |  360GB | 160GB |  400GB |
+| LoRA/GaLore/BAdam |  16  |  16GB |  32GB |  64GB |  160GB |  240GB | 120GB |  320GB |
+| QLoRA             |   8  |  10GB |  20GB |  40GB |   80GB |  140GB |  60GB |  160GB |
+| QLoRA             |   4  |   6GB |  12GB |  24GB |   48GB |   72GB |  30GB |   96GB |
+| QLoRA             |   2  |   4GB |   8GB |  16GB |   24GB |   48GB |  18GB |   48GB |

 ## 如何使用

@@ -305,7 +322,7 @@ cd LLaMA-Factory
 pip install -e .[metrics]
 ```

-可选的额外依赖项：deepspeed、metrics、unsloth、galore、vllm、bitsandbytes、gptq、awq、aqlm、qwen、modelscope、quality
+可选的额外依赖项：deepspeed、metrics、galore、badam、vllm、bitsandbytes、gptq、awq、aqlm、qwen、modelscope、quality

 <details><summary>Windows 用户指南</summary>

@@ -319,18 +336,29 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl

 </details>

-### LLaMA Board 可视化界面
+### 利用 LLaMA Board 可视化界面训练（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）

 > [!IMPORTANT]
-> LLaMA Board 可视化界面目前仅支持单 GPU 训练，请使用[命令行接口](#命令行接口)来进行分布式训练。
+> LLaMA Board 可视化界面目前仅支持单 GPU 训练，请使用[命令行接口](#命令行接口)来进行多 GPU 分布式训练。

 #### 使用本地环境

 ```bash
 export CUDA_VISIBLE_DEVICES=0 # Windows 使用 `set CUDA_VISIBLE_DEVICES=0`
+export GRADIO_SERVER_PORT=7860 # Windows 使用 `set GRADIO_SERVER_PORT=7860`
 python src/train_web.py # 或 python -m llmtuner.webui.interface
 ```

+<details><summary>阿里云用户指南</summary>
+
+如果您在阿里云上使用 LLaMA Board 时遇到显示问题，请尝试在启动前使用以下命令设置环境变量：
+
+```bash
+export GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/
+```
+
+</details>
+
 #### 使用 Docker

 ```bash
@@ -360,23 +388,23 @@ docker compose -f ./docker-compose.yml up -d

 </details>

-### 命令行接口
+### 利用命令行接口训练

 使用方法请参考 [examples/README_zh.md](examples/README_zh.md)。

-使用 `python src/train_bash.py -h` 查看参数文档。
+您可以执行 `python src/train_bash.py -h` 来查看参数文档。

-### 使用 OpenAI 风格 API 和 vLLM 部署
+### 利用 vLLM 部署 OpenAI API

 ```bash
 CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 python src/api_demo.py \
-    --model_name_or_path mistralai/Mistral-7B-Instruct-v0.2 \
-    --template mistral \
+    --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
+    --template llama3 \
    --infer_backend vllm \
    --vllm_enforce_eager
 ```

-### 使用魔搭社区
+### 从魔搭社区下载

 如果您在 Hugging Face 模型和数据集的下载中遇到了问题，可以通过下述方法使用魔搭社区。

@@ -384,11 +412,11 @@ CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 python src/api_demo.py \
 export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
 ```

-将 `--model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `modelscope/Llama-2-7b-ms`。
+将 `--model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `LLM-Research/Meta-Llama-3-8B-Instruct`。

 ## 使用了 LLaMA Factory 的项目

-如果您有项目希望添加至上述列表，请通过邮件联系或者创建一个 PR。
+如果您有项目希望添加至下述列表，请通过邮件联系或者创建一个 PR。

 <details><summary>点击显示</summary>

@@ -413,8 +441,14 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
 1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
 1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
 1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
+1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
 1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
 1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
+1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
+1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
+1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
+1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
+1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
 1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
 1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
 1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
@@ -427,7 +461,7 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`

 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。

-使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
+使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2/LLaVA-1.5](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)

 ## 引用

--- a/data/README.md
+++ b/data/README.md
@@ -18,7 +18,8 @@ If you are using a custom dataset, please provide your dataset definition in the
    "history": "the column name in the dataset containing the histories. (default: None)",
    "messages": "the column name in the dataset containing the messages. (default: conversations)",
    "system": "the column name in the dataset containing the system prompts. (default: None)",
-    "tools": "the column name in the dataset containing the tool description. (default: None)"
+    "tools": "the column name in the dataset containing the tool description. (default: None)",
+    "images": "the column name in the dataset containing the image inputs. (default: None)"
  },
  "tags (optional, used for the sharegpt format)": {
    "role_tag": "the key in the message represents the identity. (default: from)",
--- a/data/README_zh.md
+++ b/data/README_zh.md
@@ -18,7 +18,8 @@
    "history": "数据集代表历史对话的表头名称（默认：None）",
    "messages": "数据集代表消息列表的表头名称（默认：conversations）",
    "system": "数据集代表系统提示的表头名称（默认：None）",
-    "tools": "数据集代表工具描述的表头名称（默认：None）"
+    "tools": "数据集代表工具描述的表头名称（默认：None）",
+    "images": "数据集代表图像输入的表头名称（默认：None）"
  },
  "tags（可选，用于 sharegpt 格式）": {
    "role_tag": "消息中代表发送者身份的键名（默认：from）",
--- a/data/alpaca_data_zh_51k.json.REMOVED.git-id
+++ b/data/alpaca_data_zh_51k.json.REMOVED.git-id
@@ -1 +1 @@
-34c723573fbc2d7601f6d9c882ccf5aa4f9bcc4b
+a97cf9475291591843976554878568e046d8a46d
--- a/data/belle_multiturn/belle_multiturn.py
+++ b/data/belle_multiturn/belle_multiturn.py
@@ -1,5 +1,6 @@
-import os
 import json
+import os
+
 import datasets


@@ -22,31 +23,19 @@ _URL = "{}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0


 class BelleMultiturn(datasets.GeneratorBasedBuilder):
-
    VERSION = datasets.Version("0.0.0")

    def _info(self):
-        features = datasets.Features({
-            "conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
-        })
+        features = datasets.Features(
+            {"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
+        )
        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        file_path = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": file_path
-                }
-            )
-        ]
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]

    def _generate_examples(self, filepath: str):
        with open(filepath, "r", encoding="utf-8") as f:
@@ -58,7 +47,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):

                assist_idx = prompt.rfind("Assistant:")
                human_idx = prompt.rfind("Human:")
-                query = prompt[human_idx+6:assist_idx].strip()
+                query = prompt[human_idx + 6 : assist_idx].strip()
                prompt = prompt[:human_idx].strip()
                conversations.insert(0, {"from": "gpt", "value": response})
                conversations.insert(0, {"from": "human", "value": query})
@@ -67,8 +56,8 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
                    assist_idx = prompt.rfind("Assistant:")
                    human_idx = prompt.rfind("Human:")
                    if human_idx != -1:
-                        old_query = prompt[human_idx+6:assist_idx].strip()
-                        old_resp = prompt[assist_idx+10:].strip()
+                        old_query = prompt[human_idx + 6 : assist_idx].strip()
+                        old_resp = prompt[assist_idx + 10 :].strip()
                        conversations.insert(0, {"from": "gpt", "value": old_resp})
                        conversations.insert(0, {"from": "human", "value": old_query})
                    else:
--- a/data/example_dataset/example_dataset.py
+++ b/data/example_dataset/example_dataset.py
@@ -1,7 +1,8 @@
 import json
-import datasets
 from typing import Any, Dict, Generator, List, Tuple

+import datasets
+

 _DESCRIPTION = "An example of dataset."
 _CITATION = ""
@@ -11,34 +12,24 @@ _URL = "examples.json"


 class ExampleDataset(datasets.GeneratorBasedBuilder):
-
    VERSION = datasets.Version("0.0.0")

    def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features({
-            "instruction": datasets.Value("string"),
-            "input": datasets.Value("string"),
-            "output": datasets.Value("string"),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
-        })
+        features = datasets.Features(
+            {
+                "instruction": datasets.Value("string"),
+                "input": datasets.Value("string"),
+                "output": datasets.Value("string"),
+                "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+            }
+        )
        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        file_path = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": file_path
-                }
-            )
-        ]
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]

    def _generate_examples(self, filepath: str) -> Generator[Tuple[int, Dict[str, Any]], None, None]:
        example_dataset = json.load(open(filepath, "r", encoding="utf-8"))
--- a/data/hh_rlhf_en/hh_rlhf_en.py
+++ b/data/hh_rlhf_en/hh_rlhf_en.py
@@ -1,8 +1,10 @@
-import os
 import json
-import datasets
+import os
 from typing import List

+import datasets
+
+
 _HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
 _DESCRIPTION = "Human preference data about helpfulness and harmlessness."
 _CITATION = ""
@@ -14,50 +16,37 @@ _URLS = {
        _URL + "harmless-base/train.jsonl.gz",
        _URL + "helpful-base/train.jsonl.gz",
        _URL + "helpful-online/train.jsonl.gz",
-        _URL + "helpful-rejection-sampled/train.jsonl.gz"
+        _URL + "helpful-rejection-sampled/train.jsonl.gz",
    ],
    "test": [
        _URL + "harmless-base/test.jsonl.gz",
        _URL + "helpful-base/test.jsonl.gz",
        _URL + "helpful-online/test.jsonl.gz",
-        _URL + "helpful-rejection-sampled/test.jsonl.gz"
-    ]
+        _URL + "helpful-rejection-sampled/test.jsonl.gz",
+    ],
 }


 class HhRlhfEn(datasets.GeneratorBasedBuilder):
-
    VERSION = datasets.Version("0.0.0")

    def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features({
-            "instruction": datasets.Value("string"),
-            "output": datasets.Sequence(datasets.Value("string")),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
-        })
+        features = datasets.Features(
+            {
+                "instruction": datasets.Value("string"),
+                "output": datasets.Sequence(datasets.Value("string")),
+                "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+            }
+        )
        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
        file_path = dl_manager.download_and_extract(_URLS)
        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": file_path["train"]
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": file_path["test"]
-                }
-            )
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_path["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": file_path["test"]}),
        ]

    def _generate_examples(self, filepaths: List[str]):
@@ -70,12 +59,12 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
                    rejected = data["rejected"]

                    assist_idx = rejected.rfind("\n\nAssistant: ")
-                    r_reject = rejected[assist_idx+13:].strip()
+                    r_reject = rejected[assist_idx + 13 :].strip()
                    assist_idx = chosen.rfind("\n\nAssistant: ")
-                    r_accept = chosen[assist_idx+13:].strip()
+                    r_accept = chosen[assist_idx + 13 :].strip()

                    human_idx = chosen.rfind("\n\nHuman: ")
-                    query = chosen[human_idx+9:assist_idx].strip()
+                    query = chosen[human_idx + 9 : assist_idx].strip()
                    prompt = chosen[:human_idx]
                    history = []

@@ -83,16 +72,12 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
                        assist_idx = prompt.rfind("\n\nAssistant: ")
                        human_idx = prompt.rfind("\n\nHuman: ")
                        if human_idx != -1:
-                            old_query = prompt[human_idx+9:assist_idx].strip()
-                            old_resp = prompt[assist_idx+13:].strip()
+                            old_query = prompt[human_idx + 9 : assist_idx].strip()
+                            old_resp = prompt[assist_idx + 13 :].strip()
                            history.insert(0, (old_query, old_resp))
                        else:
                            break
                        prompt = prompt[:human_idx]

-                    yield key, {
-                        "instruction": query,
-                        "output": [r_accept, r_reject],
-                        "history": history
-                    }
+                    yield key, {"instruction": query, "output": [r_accept, r_reject], "history": history}
                    key += 1
--- a/data/ultra_chat/ultra_chat.py
+++ b/data/ultra_chat/ultra_chat.py
@@ -1,8 +1,10 @@
-import os
 import json
-import datasets
+import os
 from typing import List

+import datasets
+
+
 _HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")

 _DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data."
@@ -24,31 +26,19 @@ _BASE_DATA_URL = "{}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jso


 class UltraChat(datasets.GeneratorBasedBuilder):
-
    VERSION = datasets.Version("0.0.0")

    def _info(self):
-        features = datasets.Features({
-            "conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
-        })
+        features = datasets.Features(
+            {"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
+        )
        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)] # multiple shards
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": file_paths
-                }
-            )
-        ]
+        file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)]  # multiple shards
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_paths})]

    def _generate_examples(self, filepaths: List[str]):
        for filepath in filepaths:
@@ -56,7 +46,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
                for row in f:
                    try:
                        data = json.loads(row)
-                    except:
+                    except Exception:
                        continue
                    key: int = data["id"]
                    content: List[str] = data["data"]
@@ -64,8 +54,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
                        content.pop(-1)
                    if len(content) < 2:
                        continue
-                    conversations = [{
-                        "from": "human" if i % 2 == 0 else "gpt",
-                        "value": content[i]
-                    } for i in range(len(content))]
+                    conversations = [
+                        {"from": "human" if i % 2 == 0 else "gpt", "value": content[i]} for i in range(len(content))
+                    ]
                    yield key, {"conversations": conversations}
--- a/examples/README.md
+++ b/examples/README.md
@@ -3,41 +3,48 @@ We provide diverse examples about fine-tuning LLMs.
 ```
 examples/
 ├── lora_single_gpu/
-│   ├── pretrain.sh: Do pre-training
-│   ├── sft.sh: Do supervised fine-tuning
-│   ├── reward.sh: Do reward modeling
-│   ├── ppo.sh: Do PPO training
-│   ├── dpo.sh: Do DPO training
-│   ├── orpo.sh: Do ORPO training
+│   ├── pretrain.sh: Do continuous pre-training using LoRA
+│   ├── sft.sh: Do supervised fine-tuning using LoRA
+│   ├── reward.sh: Do reward modeling using LoRA
+│   ├── ppo.sh: Do PPO training using LoRA
+│   ├── dpo.sh: Do DPO training using LoRA
+│   ├── orpo.sh: Do ORPO training using LoRA
+│   ├── sft_mllm.sh: Do supervised fine-tuning on multimodal data using LoRA
 │   ├── prepare.sh: Save tokenized dataset
-│   └── predict.sh: Do batch predict
+│   └── predict.sh: Do batch predict and compute BLEU and ROUGE scores after LoRA tuning
 ├── qlora_single_gpu/
-│   ├── bitsandbytes.sh: Fine-tune 4/8-bit BNB models
-│   ├── gptq.sh: Fine-tune 4/8-bit GPTQ models
-│   ├── awq.sh: Fine-tune 4-bit AWQ models
-│   └── aqlm.sh: Fine-tune 2-bit AQLM models
+│   ├── bitsandbytes.sh: Fine-tune 4/8-bit BNB models using QLoRA
+│   ├── gptq.sh: Fine-tune 4/8-bit GPTQ models using QLoRA
+│   ├── awq.sh: Fine-tune 4-bit AWQ models using QLoRA
+│   └── aqlm.sh: Fine-tune 2-bit AQLM models using QLoRA
 ├── lora_multi_gpu/
-│   ├── single_node.sh: Fine-tune model with Accelerate on single node
-│   └── multi_node.sh: Fine-tune model with Accelerate on multiple nodes
+│   ├── single_node.sh: Fine-tune model with Accelerate on single node using LoRA
+│   ├── multi_node.sh: Fine-tune model with Accelerate on multiple nodes using LoRA
+│   └── ds_zero3.sh: Fine-tune model with DeepSpeed ZeRO-3 using LoRA (weight sharding)
 ├── full_multi_gpu/
-│   ├── single_node.sh: Fine-tune model with DeepSpeed on single node
-│   └── multi_node.sh: Fine-tune model with DeepSpeed on multiple nodes
+│   ├── single_node.sh: Full fine-tune model with DeepSpeed on single node
+│   ├── multi_node.sh: Full fine-tune model with DeepSpeed on multiple nodes
+│   └── predict.sh: Do parallel batch predict and compute BLEU and ROUGE scores after full tuning
 ├── merge_lora/
 │   ├── merge.sh: Merge LoRA weights into the pre-trained models
-│   └── quantize.sh: Quantize fine-tuned model with AutoGPTQ
+│   └── quantize.sh: Quantize the fine-tuned model with AutoGPTQ
 ├── inference/
-│   ├── cli_demo.sh: Launch a command line interface
-│   ├── api_demo.sh: Launch an OpenAI-style API
-│   ├── web_demo.sh: Launch a web interface
-│   └── evaluate.sh: Evaluate model on the MMLU benchmark
+│   ├── cli_demo.sh: Chat with fine-tuned model in the CLI with LoRA adapters
+│   ├── api_demo.sh: Chat with fine-tuned model in an OpenAI-style API with LoRA adapters
+│   ├── web_demo.sh: Chat with fine-tuned model in the Web browser with LoRA adapters
+│   └── evaluate.sh: Evaluate model on the MMLU/CMMLU/C-Eval benchmarks with LoRA adapters
 └── extras/
    ├── galore/
    │   └── sft.sh: Fine-tune model with GaLore
+    ├── badam/
+    │   └── sft.sh: Fine-tune model with BAdam
    ├── loraplus/
-    │   └── sft.sh: Fine-tune model with LoRA+
+    │   └── sft.sh: Fine-tune model using LoRA+
+    ├── mod/
+    │   └── sft.sh: Fine-tune model using Mixture-of-Depths
    ├── llama_pro/
    │   ├── expand.sh: Expand layers in the model
-    │   └── sft.sh: Fine-tune expanded model
+    │   └── sft.sh: Fine-tune the expanded model
    └── fsdp_qlora/
-        └── sft.sh: Fine-tune quantized model with FSDP
+        └── sft.sh: Fine-tune quantized model with FSDP+QLoRA
 ```
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@@ -1,43 +1,50 @@
-我们提供了多样化的示例脚本。
+我们提供了多样化的大模型微调示例脚本。

 ```
 examples/
 ├── lora_single_gpu/
-│   ├── pretrain.sh: 进行预训练
-│   ├── sft.sh: 进行指令监督微调
-│   ├── reward.sh: 进行奖励模型训练
-│   ├── ppo.sh: 进行 PPO 训练
-│   ├── dpo.sh: 进行 DPO 训练
-│   ├── orpo.sh: 进行 ORPO 训练
+│   ├── pretrain.sh: 基于 LoRA 进行增量预训练
+│   ├── sft.sh: 基于 LoRA 进行指令监督微调
+│   ├── reward.sh: 基于 LoRA 进行奖励模型训练
+│   ├── ppo.sh: 基于 LoRA 进行 PPO 训练
+│   ├── dpo.sh: 基于 LoRA 进行 DPO 训练
+│   ├── orpo.sh: 基于 LoRA 进行 ORPO 训练
+│   ├── sft_mllm.sh: 基于 LoRA 进行多模态指令监督微调
 │   ├── prepare.sh: 保存预处理后的数据集
-│   └── predict.sh: 进行批量预测
+│   └── predict.sh: 基于 LoRA 进行批量预测并计算 BLEU 和 ROUGE 分数
 ├── qlora_single_gpu/
-│   ├── bitsandbytes.sh: 微调 4/8 比特 BNB 模型
-│   ├── gptq.sh: 微调 4/8 比特 GPTQ 模型
-│   ├── awq.sh: 微调 4 比特 AWQ 模型
-│   └── aqlm.sh: 微调 2 比特 AQLM 模型
+│   ├── bitsandbytes.sh: 基于 QLoRA 微调 4/8 比特 BNB 模型
+│   ├── gptq.sh: 基于 QLoRA 微调 4/8 比特 GPTQ 模型
+│   ├── awq.sh: 基于 QLoRA 微调 4 比特 AWQ 模型
+│   └── aqlm.sh: 基于 QLoRA 微调 2 比特 AQLM 模型
 ├── lora_multi_gpu/
-│   ├── single_node.sh: 使用 Accelerate 进行单节点训练
-│   └── multi_node.sh: 使用 Accelerate 进行多节点训练
+│   ├── single_node.sh: 使用 Accelerate 进行单节点 LoRA 训练
+│   ├── multi_node.sh: 使用 Accelerate 进行多节点 LoRA 训练
+│   └── ds_zero3.sh: 使用 DeepSpeed ZeRO-3 进行 LoRA 训练（拆分权重）
 ├── full_multi_gpu/
-│   ├── single_node.sh: 使用 DeepSpeed 进行单节点训练
-│   └── multi_node.sh: 使用 DeepSpeed 进行多节点训练
+│   ├── single_node.sh: 使用 DeepSpeed 进行单节点全量训练
+│   ├── multi_node.sh: 使用 DeepSpeed 进行多节点全量训练
+│   └── predict.sh: 基于全量训练进行多卡批量预测并计算 BLEU 和 ROUGE 分数
 ├── merge_lora/
 │   ├── merge.sh: 将 LoRA 权重合并到预训练模型中
-│   └── quantize.sh: 使用 AutoGPTQ 量化模型
+│   └── quantize.sh: 使用 AutoGPTQ 量化微调后的模型
 ├── inference/
-│   ├── cli_demo.sh: 启动命令行推理接口
-│   ├── api_demo.sh: 启动 OpenAI 风格 API
-│   ├── web_demo.sh: 启动浏览器推理接口
-│   └── evaluate.sh: 在 MMLU 数据集上评测模型
+│   ├── cli_demo.sh: 启动 LoRA 模型的命令行推理接口
+│   ├── api_demo.sh: 启动 LoRA 模型的 OpenAI 风格 API
+│   ├── web_demo.sh: 启动 LoRA 模型的浏览器推理接口
+│   └── evaluate.sh: 在 MMLU/CMMLU/C-Eval 数据集上评测 LoRA 模型
 └── extras/
    ├── galore/
    │   └── sft.sh: 使用 GaLore 训练模型
+    ├── badam/
+    │   └── sft.sh: 使用 BAdam 训练模型
    ├── loraplus/
    │   └── sft.sh: 使用 LoRA+ 训练模型
+    ├── mod/
+    │   └── sft.sh: 使用深度混合训练模型
    ├── llama_pro/
    │   ├── expand.sh: 扩展模型中的层
    │   └── sft.sh: 训练扩展后的模型
    └── fsdp_qlora/
-        └── sft.sh: 使用 FSDP 微调量化模型
+        └── sft.sh: 使用 FSDP+QLoRA 微调量化模型
 ```
--- a/examples/accelerate/master_config.yaml
+++ b/examples/accelerate/master_config.yaml
@@ -9,7 +9,7 @@ main_process_port: 29555
 main_training_function: main
 mixed_precision: fp16
 num_machines: 2 # the number of nodes
-num_processes: 16 # the number of GPUs in all nodes
+num_processes: 8 # the number of GPUs in all nodes
 rdzv_backend: static
 same_network: true
 tpu_env: []
--- a/examples/accelerate/slave_config.yaml
+++ b/examples/accelerate/slave_config.yaml
@@ -9,7 +9,7 @@ main_process_port: 29555
 main_training_function: main
 mixed_precision: fp16
 num_machines: 2 # the number of nodes
-num_processes: 16 # the number of GPUs in all nodes
+num_processes: 8 # the number of GPUs in all nodes
 rdzv_backend: static
 same_network: true
 tpu_env: []
--- a/examples/extras/badam/sft.sh
+++ b/examples/extras/badam/sft.sh
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
+    --stage sft \
+    --do_train \
+    --model_name_or_path meta-llama/Llama-2-7b-hf \
+    --dataset alpaca_gpt4_en,glaive_toolcall \
+    --dataset_dir ../../../data \
+    --template default \
+    --finetuning_type full \
+    --use_badam \
+    --badam_switch_mode descending \
+    --badam_switch_block_every 50 \
+    --badam_verbose 2 \
+    --output_dir ../../../saves/LLaMA2-7B/badam/sft \
+    --overwrite_cache \
+    --overwrite_output_dir \
+    --cutoff_len 1024 \
+    --preprocessing_num_workers 16 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --warmup_steps 20 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --evaluation_strategy steps \
+    --load_best_model_at_end \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --max_samples 3000 \
+    --val_size 0.1 \
+    --plot_loss \
+    --pure_bf16
--- a/examples/extras/fsdp_qlora/sft.sh
+++ b/examples/extras/fsdp_qlora/sft.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+# DO NOT use GPTQ/AWQ model in FSDP+QLoRA

 pip install "transformers>=4.39.1"
 pip install "accelerate>=0.28.0"
--- a/examples/extras/galore/sft.sh
+++ b/examples/extras/galore/sft.sh
@@ -12,6 +12,7 @@ CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
    --galore_layerwise \
    --galore_target mlp,self_attn \
    --galore_rank 128 \
+    --galore_scale 2.0 \
    --output_dir ../../../saves/LLaMA2-7B/galore/sft \
    --overwrite_cache \
    --overwrite_output_dir \
--- a/examples/extras/loraplus/sft.sh
+++ b/examples/extras/loraplus/sft.sh
@@ -9,6 +9,7 @@ CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
    --template default \
    --finetuning_type lora \
    --lora_target q_proj,v_proj \
+    --loraplus_lr_ratio 16.0 \
    --output_dir ../../saves/LLaMA2-7B/loraplus/sft \
    --overwrite_cache \
    --overwrite_output_dir \
@@ -29,5 +30,4 @@ CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
    --max_samples 3000 \
    --val_size 0.1 \
    --plot_loss \
-    --fp16 \
-    --loraplus_lr_ratio 16.0
+    --fp16
--- a/examples/extras/mod/sft.sh
+++ b/examples/extras/mod/sft.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
+    --stage sft \
+    --do_train \
+    --model_name_or_path meta-llama/Llama-2-7b-hf \
+    --dataset alpaca_gpt4_en,glaive_toolcall \
+    --dataset_dir ../../../data \
+    --template default \
+    --finetuning_type full \
+    --mixture_of_depths convert \
+    --output_dir ../../../saves/LLaMA2-7B/mod/sft \
+    --overwrite_cache \
+    --overwrite_output_dir \
+    --cutoff_len 1024 \
+    --preprocessing_num_workers 16 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --optim paged_adamw_8bit \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --warmup_steps 20 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --evaluation_strategy steps \
+    --load_best_model_at_end \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --max_samples 3000 \
+    --val_size 0.1 \
+    --plot_loss \
+    --pure_bf16
--- a/examples/full_multi_gpu/predict.sh
+++ b/examples/full_multi_gpu/predict.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
+    --config_file ../accelerate/single_config.yaml \
+    ../../src/train_bash.py \
+    --stage sft \
+    --do_predict \
+    --model_name_or_path ../../saves/LLaMA2-7B/full/sft \
+    --dataset alpaca_gpt4_en,glaive_toolcall \
+    --dataset_dir ../../data \
+    --template default \
+    --finetuning_type full \
+    --output_dir ../../saves/LLaMA2-7B/full/predict \
+    --overwrite_cache \
+    --overwrite_output_dir \
+    --cutoff_len 1024 \
+    --preprocessing_num_workers 16 \
+    --per_device_eval_batch_size 1 \
+    --max_samples 20 \
+    --predict_with_generate
--- a/examples/inference/evaluate.sh
+++ b/examples/inference/evaluate.sh
@@ -3,7 +3,7 @@
 CUDA_VISIBLE_DEVICES=0 python ../../src/evaluate.py \
    --model_name_or_path meta-llama/Llama-2-7b-hf \
    --adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
-    --template vanilla \
+    --template fewshot \
    --finetuning_type lora \
    --task mmlu \
    --split test \
--- a/examples/inference/web_demo.sh
+++ b/examples/inference/web_demo.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+# add `--visual_inputs True` to load MLLM

 CUDA_VISIBLE_DEVICES=0 python ../../src/web_demo.py \
    --model_name_or_path meta-llama/Llama-2-7b-hf \
--- a/examples/lora_multi_gpu/ds_zero3.sh
+++ b/examples/lora_multi_gpu/ds_zero3.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+deepspeed --num_gpus 4 ../../src/train_bash.py \
+    --deepspeed ../deepspeed/ds_z3_config.json \
+    --stage sft \
+    --do_train \
+    --model_name_or_path meta-llama/Llama-2-7b-hf \
+    --dataset alpaca_gpt4_en,glaive_toolcall \
+    --dataset_dir ../../data \
+    --template default \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --output_dir ../../saves/LLaMA2-7B/lora/sft \
+    --overwrite_cache \
+    --overwrite_output_dir \
+    --cutoff_len 1024 \
+    --preprocessing_num_workers 16 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 2 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --warmup_steps 20 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --evaluation_strategy steps \
+    --learning_rate 5e-5 \
+    --num_train_epochs 3.0 \
+    --max_samples 3000 \
+    --val_size 0.1 \
+    --ddp_timeout 180000000 \
+    --plot_loss \
+    --fp16
--- a/examples/lora_multi_gpu/multi_node.sh
+++ b/examples/lora_multi_gpu/multi_node.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+# also launch it on slave machine using slave_config.yaml

 CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
    --config_file ../accelerate/master_config.yaml \
--- a/examples/lora_multi_gpu/single_node.sh
+++ b/examples/lora_multi_gpu/single_node.sh
@@ -1,6 +1,6 @@
 #!/bin/bash

-CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch \
+CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
    --config_file ../accelerate/single_config.yaml \
    ../../src/train_bash.py \
    --stage sft \
--- a/examples/lora_single_gpu/sft_mllm.sh
+++ b/examples/lora_single_gpu/sft_mllm.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
+    --stage sft \
+    --do_train \
+    --model_name_or_path llava-hf/llava-1.5-7b-hf \
+    --visual_inputs \
+    --dataset mllm_demo \
+    --dataset_dir ../../data \
+    --template vicuna \
+    --finetuning_type lora \
+    --lora_target q_proj,v_proj \
+    --output_dir ../../saves/LLaMA2-7B/lora/sft_mllm \
+    --overwrite_cache \
+    --overwrite_output_dir \
+    --cutoff_len 1024 \
+    --preprocessing_num_workers 16 \
+    --per_device_train_batch_size 1 \
+    --per_device_eval_batch_size 1 \
+    --gradient_accumulation_steps 8 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --warmup_steps 20 \
+    --save_steps 100 \
+    --eval_steps 100 \
+    --evaluation_strategy steps \
+    --load_best_model_at_end \
+    --learning_rate 5e-5 \
+    --num_train_epochs 100.0 \
+    --max_samples 3000 \
+    --val_size 0.1 \
+    --plot_loss \
+    --fp16
--- a/examples/merge_lora/merge.sh
+++ b/examples/merge_lora/merge.sh
@@ -1,11 +1,12 @@
 #!/bin/bash
 # DO NOT use quantized model or quantization_bit when merging lora weights

-CUDA_VISIBLE_DEVICES= python ../../src/export_model.py \
+CUDA_VISIBLE_DEVICES=0 python ../../src/export_model.py \
    --model_name_or_path meta-llama/Llama-2-7b-hf \
    --adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
    --template default \
    --finetuning_type lora \
    --export_dir ../../models/llama2-7b-sft \
    --export_size 2 \
+    --export_device cpu \
    --export_legacy_format False
--- a/examples/merge_lora/quantize.sh
+++ b/examples/merge_lora/quantize.sh
@@ -1,4 +1,5 @@
 #!/bin/bash
+# NEED TO run `merge.sh` before using this script

 CUDA_VISIBLE_DEVICES=0 python ../../src/export_model.py \
    --model_name_or_path ../../models/llama2-7b-sft \
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ datasets>=2.14.3
 accelerate>=0.27.2
 peft>=0.10.0
 trl>=0.8.1
-gradio>=4.0.0,<=4.21.0
+gradio>=4.0.0
 scipy
 einops
 sentencepiece
@@ -15,3 +15,4 @@ fastapi
 sse-starlette
 matplotlib
 fire
+packaging
--- a/scripts/cal_lr.py
+++ b/scripts/cal_lr.py
@@ -44,8 +44,9 @@ def calculate_lr(
            overwrite_cache=True,
        )
    )
-    tokenizer = load_tokenizer(model_args)
-    trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage)
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
    if stage == "pt":
        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
    elif stage == "sft":
--- a/scripts/length_cdf.py
+++ b/scripts/length_cdf.py
@@ -32,8 +32,8 @@ def length_cdf(
            overwrite_cache=True,
        )
    )
-    tokenizer = load_tokenizer(model_args)
-    trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage="sft")
+    tokenizer_module = load_tokenizer(model_args)
+    trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
    total_num = len(trainset)
    length_dict = defaultdict(int)
    for sample in tqdm(trainset["input_ids"]):
--- a/setup.py
+++ b/setup.py
@@ -22,9 +22,9 @@ def get_requires():
 extra_require = {
    "deepspeed": ["deepspeed>=0.10.0"],
    "metrics": ["nltk", "jieba", "rouge-chinese"],
-    "unsloth": ["torch==2.2.0", "unsloth[cu121-ampere-torch220]"],
    "galore": ["galore-torch"],
-    "vllm": ["vllm>=0.3.3"],
+    "badam": ["badam"],
+    "vllm": ["vllm>=0.4.0"],
    "bitsandbytes": ["bitsandbytes>=0.39.0"],
    "gptq": ["optimum>=1.16.0", "auto-gptq>=0.5.0"],
    "awq": ["autoawq"],
--- a/src/llmtuner/init.py
+++ b/src/llmtuner/init.py
@@ -7,5 +7,5 @@ from .train import export_model, run_exp
 from .webui import create_ui, create_web_demo


-__version__ = "0.6.2"
+__version__ = "0.7.0"
 __all__ = ["create_app", "ChatModel", "Evaluator", "export_model", "run_exp", "create_ui", "create_web_demo"]
--- a/src/llmtuner/chat/base_engine.py
+++ b/src/llmtuner/chat/base_engine.py
@@ -4,15 +4,13 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Opti


 if TYPE_CHECKING:
+    from numpy.typing import NDArray
    from transformers import PreTrainedModel, PreTrainedTokenizer
+    from vllm import AsyncLLMEngine

    from ..data import Template
-    from ..extras.packages import is_vllm_available
    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments

-    if is_vllm_available():
-        from vllm import AsyncLLMEngine
-

@dataclass
 class Response:
@@ -49,6 +47,7 @@ class BaseEngine(ABC):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> List["Response"]: ...

@@ -58,6 +57,7 @@ class BaseEngine(ABC):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]: ...

--- a/src/llmtuner/chat/chat_model.py
+++ b/src/llmtuner/chat/chat_model.py
@@ -8,6 +8,8 @@ from .vllm_engine import VllmEngine


 if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
    from .base_engine import BaseEngine, Response


@@ -36,9 +38,10 @@ class ChatModel:
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> List["Response"]:
-        task = asyncio.run_coroutine_threadsafe(self.achat(messages, system, tools, **input_kwargs), self._loop)
+        task = asyncio.run_coroutine_threadsafe(self.achat(messages, system, tools, image, **input_kwargs), self._loop)
        return task.result()

    async def achat(
@@ -46,18 +49,20 @@ class ChatModel:
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> List["Response"]:
-        return await self.engine.chat(messages, system, tools, **input_kwargs)
+        return await self.engine.chat(messages, system, tools, image, **input_kwargs)

    def stream_chat(
        self,
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> Generator[str, None, None]:
-        generator = self.astream_chat(messages, system, tools, **input_kwargs)
+        generator = self.astream_chat(messages, system, tools, image, **input_kwargs)
        while True:
            try:
                task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
@@ -70,9 +75,10 @@ class ChatModel:
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
-        async for new_token in self.engine.stream_chat(messages, system, tools, **input_kwargs):
+        async for new_token in self.engine.stream_chat(messages, system, tools, image, **input_kwargs):
            yield new_token

    def get_scores(
--- a/src/llmtuner/chat/hf_engine.py
+++ b/src/llmtuner/chat/hf_engine.py
@@ -14,7 +14,9 @@ from .base_engine import BaseEngine, Response


 if TYPE_CHECKING:
-    from transformers import PreTrainedModel, PreTrainedTokenizer
+    from numpy.typing import NDArray
+    from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
+    from transformers.image_processing_utils import BaseImageProcessor
    from trl import PreTrainedModelWrapper

    from ..data import Template
@@ -30,7 +32,9 @@ class HuggingfaceEngine(BaseEngine):
        generating_args: "GeneratingArguments",
    ) -> None:
        self.can_generate = finetuning_args.stage == "sft"
-        self.tokenizer = load_tokenizer(model_args)
+        tokenizer_module = load_tokenizer(model_args)
+        self.tokenizer = tokenizer_module["tokenizer"]
+        self.processor = tokenizer_module["processor"]
        self.tokenizer.padding_side = "left" if self.can_generate else "right"
        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
        self.model = load_model(
@@ -42,13 +46,18 @@ class HuggingfaceEngine(BaseEngine):
    def _process_args(
        model: "PreTrainedModel",
        tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
        template: "Template",
        generating_args: Dict[str, Any],
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        input_kwargs: Optional[Dict[str, Any]] = {},
    ) -> Tuple[Dict[str, Any], int]:
+        if processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+            messages[0]["content"] = "<image>" + messages[0]["content"]
+
        paired_messages = messages + [{"role": "assistant", "content": ""}]
        prompt_ids, _ = template.encode_oneturn(
            tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools
@@ -95,6 +104,11 @@ class HuggingfaceEngine(BaseEngine):
            logits_processor=get_logits_processor(),
        )

+        if processor is not None and image is not None:
+            image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
+            gen_kwargs["pixel_values"] = pixel_values.to(model.device)
+
        return gen_kwargs, prompt_length

    @staticmethod
@@ -102,15 +116,17 @@ class HuggingfaceEngine(BaseEngine):
    def _chat(
        model: "PreTrainedModel",
        tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
        template: "Template",
        generating_args: Dict[str, Any],
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        input_kwargs: Optional[Dict[str, Any]] = {},
    ) -> List["Response"]:
        gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
-            model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
+            model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
        )
        generate_output = model.generate(**gen_kwargs)
        response_ids = generate_output[:, prompt_length:]
@@ -135,15 +151,17 @@ class HuggingfaceEngine(BaseEngine):
    def _stream_chat(
        model: "PreTrainedModel",
        tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
        template: "Template",
        generating_args: Dict[str, Any],
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        input_kwargs: Optional[Dict[str, Any]] = {},
    ) -> Callable[[], str]:
        gen_kwargs, _ = HuggingfaceEngine._process_args(
-            model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
+            model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
        )
        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
        gen_kwargs["streamer"] = streamer
@@ -199,6 +217,7 @@ class HuggingfaceEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> List["Response"]:
        if not self.can_generate:
@@ -208,11 +227,13 @@ class HuggingfaceEngine(BaseEngine):
        input_args = (
            self.model,
            self.tokenizer,
+            self.processor,
            self.template,
            self.generating_args,
            messages,
            system,
            tools,
+            image,
            input_kwargs,
        )
        async with self._semaphore:
@@ -224,6 +245,7 @@ class HuggingfaceEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
        if not self.can_generate:
@@ -233,11 +255,13 @@ class HuggingfaceEngine(BaseEngine):
        input_args = (
            self.model,
            self.tokenizer,
+            self.processor,
            self.template,
            self.generating_args,
            messages,
            system,
            tools,
+            image,
            input_kwargs,
        )
        async with self._semaphore:
--- a/src/llmtuner/chat/vllm_engine.py
+++ b/src/llmtuner/chat/vllm_engine.py
@@ -1,19 +1,24 @@
 import uuid
 from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence

-from transformers.utils.versions import require_version
-
 from ..data import get_template_and_fix_tokenizer
-from ..extras.misc import get_device_count
+from ..extras.misc import get_device_count, infer_optim_dtype
 from ..extras.packages import is_vllm_available
-from ..model import load_tokenizer
+from ..model import load_config, load_tokenizer
 from .base_engine import BaseEngine, Response


 if is_vllm_available():
    from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
+    from vllm.lora.request import LoRARequest
+    from vllm.sequence import MultiModalData
+

 if TYPE_CHECKING:
+    import torch
+    from numpy.typing import NDArray
+    from transformers.image_processing_utils import BaseImageProcessor
+
    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments


@@ -25,32 +30,59 @@ class VllmEngine(BaseEngine):
        finetuning_args: "FinetuningArguments",
        generating_args: "GeneratingArguments",
    ) -> None:
-        require_version("vllm>=0.3.3", "To fix: pip install vllm>=0.3.3")
+        config = load_config(model_args)  # may download model from ms hub
+        infer_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
+        infer_dtype = str(infer_dtype).split(".")[-1]
+
        self.can_generate = finetuning_args.stage == "sft"
-        engine_args = AsyncEngineArgs(
-            model=model_args.model_name_or_path,
-            trust_remote_code=True,
-            max_model_len=model_args.vllm_maxlen,
-            tensor_parallel_size=get_device_count() or 1,
-            gpu_memory_utilization=model_args.vllm_gpu_util,
-            disable_log_stats=True,
-            disable_log_requests=True,
-            enforce_eager=model_args.vllm_enforce_eager,
-        )
-        self.model = AsyncLLMEngine.from_engine_args(engine_args)
-        self.tokenizer = load_tokenizer(model_args)
+        tokenizer_module = load_tokenizer(model_args)
+        self.tokenizer = tokenizer_module["tokenizer"]
+        self.processor = tokenizer_module["processor"]
        self.tokenizer.padding_side = "left"
        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
        self.generating_args = generating_args.to_dict()

+        engine_args = {
+            "model": model_args.model_name_or_path,
+            "trust_remote_code": True,
+            "download_dir": model_args.cache_dir,
+            "dtype": infer_dtype,
+            "max_model_len": model_args.vllm_maxlen,
+            "tensor_parallel_size": get_device_count() or 1,
+            "gpu_memory_utilization": model_args.vllm_gpu_util,
+            "disable_log_stats": True,
+            "disable_log_requests": True,
+            "enforce_eager": model_args.vllm_enforce_eager,
+            "enable_lora": model_args.adapter_name_or_path is not None,
+        }
+
+        if model_args.visual_inputs:
+            # TODO: auto derive from config
+            # https://github.com/vllm-project/vllm/pull/3042#issuecomment-1984893549
+            self.image_feature_size = 576
+            engine_args["image_input_type"] = "pixel_values"
+            engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids("<image>")
+            engine_args["image_input_shape"] = "1,3,336,336"
+            engine_args["image_feature_size"] = self.image_feature_size
+
+        self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
+        if model_args.adapter_name_or_path is not None:
+            self.lora_request = LoRARequest("default", 1, model_args.adapter_name_or_path[0])
+        else:
+            self.lora_request = None
+
    async def _generate(
        self,
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> AsyncIterator["RequestOutput"]:
        request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+        if self.processor is not None and image is not None and "<image>" not in messages[0]["content"]:
+            messages[0]["content"] = "<image>" * self.image_feature_size + messages[0]["content"]
+
        paired_messages = messages + [{"role": "assistant", "content": ""}]
        prompt_ids, _ = self.template.encode_oneturn(
            tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
@@ -94,8 +126,21 @@ class VllmEngine(BaseEngine):
            max_tokens=generating_args["max_new_tokens"],
            skip_special_tokens=True,
        )
+
+        if self.processor is not None and image is not None:
+            image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
+            pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
+            multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
+        else:
+            multi_modal_data = None
+
        result_generator = self.model.generate(
-            prompt=None, sampling_params=sampling_params, request_id=request_id, prompt_token_ids=prompt_ids
+            prompt=None,
+            sampling_params=sampling_params,
+            request_id=request_id,
+            prompt_token_ids=prompt_ids,
+            lora_request=self.lora_request,
+            multi_modal_data=multi_modal_data,
        )
        return result_generator

@@ -107,10 +152,11 @@ class VllmEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> List["Response"]:
        final_output = None
-        generator = await self._generate(messages, system, tools, **input_kwargs)
+        generator = await self._generate(messages, system, tools, image, **input_kwargs)
        async for request_output in generator:
            final_output = request_output

@@ -132,10 +178,11 @@ class VllmEngine(BaseEngine):
        messages: Sequence[Dict[str, str]],
        system: Optional[str] = None,
        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
        **input_kwargs,
    ) -> AsyncGenerator[str, None]:
        generated_text = ""
-        generator = await self._generate(messages, system, tools, **input_kwargs)
+        generator = await self._generate(messages, system, tools, image, **input_kwargs)
        async for result in generator:
            delta_text = result.outputs[0].text[len(generated_text) :]
            generated_text = result.outputs[0].text
--- a/src/llmtuner/data/aligner.py
+++ b/src/llmtuner/data/aligner.py
@@ -1,3 +1,4 @@
+import os
 from functools import partial
 from typing import TYPE_CHECKING, Any, Dict, List, Union

@@ -13,8 +14,23 @@ if TYPE_CHECKING:
    from .parser import DatasetAttr


-def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
-    outputs = {"prompt": [], "response": [], "system": [], "tools": []}
+def _convert_images(images: List[Any], dataset_attr: "DatasetAttr", data_args: "DataArguments") -> List[Any]:
+    outputs = []
+    if dataset_attr.load_from in ["script", "file"]:
+        for image in images:
+            if isinstance(image, str) and os.path.isfile(os.path.join(data_args.dataset_dir, image)):
+                outputs.append(os.path.join(data_args.dataset_dir, image))
+            else:
+                outputs.append(image)
+
+    return outputs
+
+
+def convert_alpaca(
+    examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
+) -> Dict[str, List[Any]]:
+    outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
+    convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
    for i in range(len(examples[dataset_attr.prompt])):
        prompt = []
        if dataset_attr.history and isinstance(examples[dataset_attr.history][i], list):
@@ -44,12 +60,16 @@ def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr")
        outputs["response"].append(response)
        outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
        outputs["tools"].append("")
+        outputs["images"].append(convert_images(examples[dataset_attr.images][i]) if dataset_attr.images else [])

    return outputs


-def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
-    outputs = {"prompt": [], "response": [], "system": [], "tools": []}
+def convert_sharegpt(
+    examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
+) -> Dict[str, List[Any]]:
+    outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
+    convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
    tag_mapping = {
        dataset_attr.user_tag: Role.USER.value,
        dataset_attr.assistant_tag: Role.ASSISTANT.value,
@@ -84,6 +104,7 @@ def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr"
        outputs["response"].append(aligned_messages[-1:])
        outputs["system"].append(system)
        outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
+        outputs["images"].append(convert_images(examples[dataset_attr.images][i]) if dataset_attr.images else [])

    return outputs

@@ -96,12 +117,13 @@ def align_dataset(
        prompt: [{"role": "user", "content": "..."}] * (2T - 1)
        response: [{"role": "assistant", "content": "..."}] * N (N > 1 for ranking dataset)
        system: "..."
-        tools: "..."
+        tools: "...",
+        images: [],
    """
    if dataset_attr.formatting == "alpaca":
-        convert_func = partial(convert_alpaca, dataset_attr=dataset_attr)
+        convert_func = partial(convert_alpaca, dataset_attr=dataset_attr, data_args=data_args)
    else:
-        convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr)
+        convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr, data_args=data_args)

    column_names = list(next(iter(dataset)).keys())
    features = Features.from_dict(
@@ -114,6 +136,7 @@ def align_dataset(
            ],
            "system": {"dtype": "string", "_type": "Value"},
            "tools": {"dtype": "string", "_type": "Value"},
+            "images": [{"_type": "Image"}],
        }
    )
    kwargs = {}
--- a/src/llmtuner/data/loader.py
+++ b/src/llmtuner/data/loader.py
@@ -1,6 +1,6 @@
 import inspect
 import os
-from typing import TYPE_CHECKING, Literal, Union
+from typing import TYPE_CHECKING, Literal, Optional, Union

 from datasets import load_dataset, load_from_disk

@@ -16,7 +16,7 @@ from .utils import checksum, merge_dataset

 if TYPE_CHECKING:
    from datasets import Dataset, IterableDataset
-    from transformers import Seq2SeqTrainingArguments
+    from transformers import ProcessorMixin, Seq2SeqTrainingArguments
    from transformers.tokenization_utils import PreTrainedTokenizer

    from ..hparams import DataArguments, ModelArguments
@@ -115,11 +115,12 @@ def load_single_dataset(


 def get_dataset(
-    tokenizer: "PreTrainedTokenizer",
    model_args: "ModelArguments",
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    stage: Literal["pt", "sft", "rm", "ppo"],
+    tokenizer: "PreTrainedTokenizer",
+    processor: Optional["ProcessorMixin"] = None,
 ) -> Union["Dataset", "IterableDataset"]:
    template = get_template_and_fix_tokenizer(tokenizer, data_args.template)
    if data_args.train_on_prompt and template.efficient_eos:
@@ -149,7 +150,7 @@ def get_dataset(

    with training_args.main_process_first(desc="pre-process dataset"):
        preprocess_func, print_function = get_preprocess_and_print_func(
-            tokenizer, template, data_args, training_args, stage
+            data_args, training_args, stage, template, tokenizer, processor
        )
        column_names = list(next(iter(dataset)).keys())
        kwargs = {}
--- a/src/llmtuner/data/parser.py
+++ b/src/llmtuner/data/parser.py
@@ -28,6 +28,7 @@ class DatasetAttr:
    formatting: Literal["alpaca", "sharegpt"] = "alpaca"
    """ columns """
    system: Optional[str] = None
+    images: Optional[str] = None
    """ columns for the alpaca format """
    prompt: Optional[str] = "instruction"
    query: Optional[str] = "input"
@@ -105,7 +106,7 @@ def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
        dataset_attr.set_attr("formatting", dataset_info[name], default="alpaca")

        if "columns" in dataset_info[name]:
-            column_names = ["system"]
+            column_names = ["system", "images"]
            if dataset_attr.formatting == "alpaca":
                column_names.extend(["prompt", "query", "response", "history"])
            else:
--- a/src/llmtuner/data/preprocess.py
+++ b/src/llmtuner/data/preprocess.py
@@ -1,14 +1,22 @@
 from functools import partial
 from itertools import chain
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Tuple
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple

 from ..extras.constants import IGNORE_INDEX
 from ..extras.logging import get_logger
+from ..extras.packages import is_pillow_available
 from .utils import Role


+if is_pillow_available():
+    from PIL import Image
+
+
 if TYPE_CHECKING:
-    from transformers import Seq2SeqTrainingArguments
+    from numpy.typing import NDArray
+    from PIL.Image import Image as ImageObject
+    from transformers import ProcessorMixin, Seq2SeqTrainingArguments
+    from transformers.image_processing_utils import BaseImageProcessor
    from transformers.tokenization_utils import PreTrainedTokenizer

    from ..hparams import DataArguments
@@ -18,6 +26,13 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)


+def _preprocess_visual_inputs(images: Sequence["ImageObject"], processor: "ProcessorMixin") -> "NDArray":
+    # process visual inputs (currently only supports a single image)
+    image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+    image = images[0] if len(images) != 0 else Image.new("RGB", (100, 100), (255, 255, 255))
+    return image_processor(image, return_tensors="pt")["pixel_values"][0]
+
+
 def preprocess_pretrain_dataset(
    examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
 ) -> Dict[str, List[List[int]]]:
@@ -48,18 +63,25 @@ def preprocess_pretrain_dataset(

 def preprocess_supervised_dataset(
    examples: Dict[str, List[Any]],
-    tokenizer: "PreTrainedTokenizer",
    template: "Template",
+    tokenizer: "PreTrainedTokenizer",
+    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # build inputs with format `<bos> X Y <eos>` and labels with format `<ignore> ... <ignore> Y <eos>`
    # for multiturn examples, we only mask the prompt part in each prompt-response pair.
    model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
+    if processor is not None:
+        model_inputs["pixel_values"] = []
+        preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)

    for i in range(len(examples["prompt"])):
        if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) != 1:
            continue

+        if processor is not None:
+            examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
+
        messages = examples["prompt"][i] + examples["response"][i]
        input_ids, labels = [], []
        for turn_idx, (source_ids, target_ids) in enumerate(
@@ -89,14 +111,16 @@ def preprocess_supervised_dataset(
        model_inputs["input_ids"].append(input_ids)
        model_inputs["attention_mask"].append([1] * len(input_ids))
        model_inputs["labels"].append(labels)
+        if processor is not None:
+            model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))

    return model_inputs


 def preprocess_packed_supervised_dataset(
    examples: Dict[str, List[Any]],
-    tokenizer: "PreTrainedTokenizer",
    template: "Template",
+    tokenizer: "PreTrainedTokenizer",
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # build inputs with format `<bos> X1 Y1 <eos> <bos> X2 Y2 <eos>`
@@ -141,17 +165,24 @@ def preprocess_packed_supervised_dataset(

 def preprocess_unsupervised_dataset(
    examples: Dict[str, List[Any]],
-    tokenizer: "PreTrainedTokenizer",
    template: "Template",
+    tokenizer: "PreTrainedTokenizer",
+    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # build inputs with format `<bos> X` and labels with format `Y <eos>`
    model_inputs = {"input_ids": [], "attention_mask": [], "labels": []}
+    if processor is not None:
+        model_inputs["pixel_values"] = []
+        preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)

    for i in range(len(examples["prompt"])):
        if len(examples["prompt"][i]) % 2 != 1:
            continue

+        if processor is not None:
+            examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
+
        if len(examples["response"][i]) == 1:
            messages = examples["prompt"][i] + examples["response"][i]
        else:
@@ -172,22 +203,32 @@ def preprocess_unsupervised_dataset(
        model_inputs["input_ids"].append(input_ids)
        model_inputs["attention_mask"].append([1] * len(input_ids))
        model_inputs["labels"].append(labels)
+        if processor is not None:
+            model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))

    return model_inputs


 def preprocess_pairwise_dataset(
    examples: Dict[str, List[Any]],
-    tokenizer: "PreTrainedTokenizer",
    template: "Template",
+    tokenizer: "PreTrainedTokenizer",
+    processor: Optional["ProcessorMixin"],
    data_args: "DataArguments",
 ) -> Dict[str, List[List[int]]]:
    # build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
    model_inputs = {"prompt_ids": [], "chosen_ids": [], "rejected_ids": []}
+    if processor is not None:
+        model_inputs["pixel_values"] = []
+        preprocess_visual_inputs = partial(_preprocess_visual_inputs, processor=processor)
+
    for i in range(len(examples["prompt"])):
        if len(examples["prompt"][i]) % 2 != 1 or len(examples["response"][i]) < 2:
            continue

+        if processor is not None:
+            examples["prompt"][i][0]["content"] = "<image>" + examples["prompt"][i][0]["content"]
+
        chosen_messages = examples["prompt"][i] + [examples["response"][i][0]]
        rejected_messages = examples["prompt"][i] + [examples["response"][i][1]]
        prompt_ids, chosen_ids = template.encode_oneturn(
@@ -214,6 +255,8 @@ def preprocess_pairwise_dataset(
        model_inputs["prompt_ids"].append(prompt_ids)
        model_inputs["chosen_ids"].append(chosen_ids)
        model_inputs["rejected_ids"].append(rejected_ids)
+        if processor is not None:
+            model_inputs["pixel_values"].append(preprocess_visual_inputs(examples["images"][i]))

    return model_inputs

@@ -244,34 +287,54 @@ def print_unsupervised_dataset_example(example: Dict[str, List[int]], tokenizer:


 def get_preprocess_and_print_func(
-    tokenizer: "PreTrainedTokenizer",
-    template: "Template",
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    stage: Literal["pt", "sft", "rm", "ppo"],
+    template: "Template",
+    tokenizer: "PreTrainedTokenizer",
+    processor: Optional["ProcessorMixin"],
 ) -> Tuple[Callable, Callable]:
    if stage == "pt":
-        preprocess_func = partial(preprocess_pretrain_dataset, tokenizer=tokenizer, data_args=data_args)
+        preprocess_func = partial(
+            preprocess_pretrain_dataset,
+            tokenizer=tokenizer,
+            data_args=data_args,
+        )
        print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
    elif stage == "sft" and not training_args.predict_with_generate:
        if data_args.packing:
            preprocess_func = partial(
-                preprocess_packed_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
+                preprocess_packed_supervised_dataset,
+                template=template,
+                tokenizer=tokenizer,
+                data_args=data_args,
            )
        else:
            preprocess_func = partial(
-                preprocess_supervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
+                preprocess_supervised_dataset,
+                template=template,
+                tokenizer=tokenizer,
+                processor=processor,
+                data_args=data_args,
            )

        print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
    elif stage == "rm":
        preprocess_func = partial(
-            preprocess_pairwise_dataset, tokenizer=tokenizer, template=template, data_args=data_args
+            preprocess_pairwise_dataset,
+            template=template,
+            tokenizer=tokenizer,
+            processor=processor,
+            data_args=data_args,
        )
        print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
    else:
        preprocess_func = partial(
-            preprocess_unsupervised_dataset, tokenizer=tokenizer, template=template, data_args=data_args
+            preprocess_unsupervised_dataset,
+            template=template,
+            tokenizer=tokenizer,
+            processor=processor,
+            data_args=data_args,
        )
        print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)

--- a/src/llmtuner/data/template.py
+++ b/src/llmtuner/data/template.py
@@ -343,7 +343,7 @@ def get_template_and_fix_tokenizer(
    name: Optional[str] = None,
 ) -> Template:
    if name is None:
-        template = templates["vanilla"]  # placeholder
+        template = templates["empty"]  # placeholder
    else:
        template = templates.get(name, None)
        if template is None:
@@ -385,7 +385,8 @@ _register_template(
    format_user=StringFormatter(slots=["### Instruction:\n{{content}}\n\n### Response:\n"]),
    format_separator=EmptyFormatter(slots=["\n\n"]),
    default_system=(
-        "Below is an instruction that describes a task. " "Write a response that appropriately completes the request."
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request.\n\n"
    ),
 )

@@ -502,6 +503,7 @@ _register_template(
    name="chatml",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_separator=EmptyFormatter(slots=["\n"]),
    stop_words=["<|im_end|>", "<|im_start|>"],
    replace_eos=True,
@@ -512,6 +514,7 @@ _register_template(
    name="chatml_de",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_separator=EmptyFormatter(slots=["\n"]),
    default_system="Du bist ein freundlicher und hilfsbereiter KI-Assistent.",
    stop_words=["<|im_end|>", "<|im_start|>"],
@@ -526,6 +529,21 @@ _register_template(
 )


+_register_template(
+    name="cohere",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"
+                "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+            )
+        ]
+    ),
+    format_system=EmptyFormatter(slots=[{"bos_token"}]),
+    force_system=True,
+)
+
+
 _register_template(
    name="cpm",
    format_user=StringFormatter(slots=["<用户>{{content}}<AI>"]),
@@ -534,6 +552,32 @@ _register_template(
 )


+_register_template(
+    name="dbrx",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_separator=EmptyFormatter(slots=["\n"]),
+    default_system=(
+        "You are DBRX, created by Databricks. You were last updated in December 2023. "
+        "You answer questions based on information available up to that point.\n"
+        "YOU PROVIDE SHORT RESPONSES TO SHORT QUESTIONS OR STATEMENTS, but provide thorough "
+        "responses to more complex and open-ended questions.\nYou assist with various tasks, "
+        "from writing to coding (using markdown for code blocks — remember to use ``` with "
+        "code, JSON, and tables).\n(You do not have real-time data access or code execution "
+        "capabilities. You avoid stereotyping and provide balanced perspectives on "
+        "controversial topics. You do not provide song lyrics, poems, or news articles and "
+        "do not divulge details of your training data.)\nThis is your system prompt, "
+        "guiding your responses. Do not reference it, just respond to the user. If you find "
+        "yourself talking about this message, stop. You should be responding appropriately "
+        "and usually that means not mentioning this.\nYOU DO NOT MENTION ANY OF THIS INFORMATION "
+        "ABOUT YOURSELF UNLESS THE INFORMATION IS DIRECTLY PERTINENT TO THE USER'S QUERY."
+    ),
+    stop_words=["<|im_end|>"],
+    replace_eos=True,
+)
+
+
 _register_template(
    name="deepseek",
    format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
@@ -566,6 +610,13 @@ _register_template(
 )


+_register_template(
+    name="empty",
+    format_user=StringFormatter(slots=["{{content}}"]),
+    format_assistant=StringFormatter(slots=["{{content}}"]),
+)
+
+
 _register_template(
    name="falcon",
    format_user=StringFormatter(slots=["User: {{content}}\nFalcon:"]),
@@ -574,10 +625,20 @@ _register_template(
 )


+_register_template(
+    name="fewshot",
+    format_separator=EmptyFormatter(slots=["\n\n"]),
+    efficient_eos=True,
+)
+
+
 _register_template(
    name="gemma",
    format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
+    format_observation=StringFormatter(
+        slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
+    ),
    format_separator=EmptyFormatter(slots=["<end_of_turn>\n"]),
    efficient_eos=True,
    force_system=True,
@@ -635,9 +696,36 @@ _register_template(
 )


+_register_template(
+    name="llama3",
+    format_user=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    format_system=StringFormatter(
+        slots=[{"bos_token"}, "<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]
+    ),
+    format_observation=StringFormatter(
+        slots=[
+            (
+                "<|start_header_id|>tool<|end_header_id|>\n\n{{content}}<|eot_id|>"
+                "<|start_header_id|>assistant<|end_header_id|>\n\n"
+            )
+        ]
+    ),
+    default_system="You are a helpful assistant.",
+    stop_words=["<|eot_id|>"],
+    replace_eos=True,
+)
+
+
 _register_template(
    name="mistral",
-    format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
+    format_user=StringFormatter(slots=[" [INST] {{content}} [/INST]"]),
    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
    force_system=True,
 )
@@ -669,10 +757,23 @@ _register_template(
 )


+_register_template(
+    name="phi",
+    format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>\n"]),
+    format_system=StringFormatter(slots=[{"bos_token"}, "<|system|>\n{{content}}<|end|>\n"]),
+    format_observation=StringFormatter(slots=["<|function_output|>\n{{content}}<|end|>\n<|assistant|>\n"]),
+    format_separator=EmptyFormatter(slots=["\n"]),
+    default_system="You are a helpful AI assistant.",
+    stop_words=["<|end|>"],
+    replace_eos=True,
+)
+
+
 _register_template(
    name="qwen",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_separator=EmptyFormatter(slots=["\n"]),
    default_system="You are a helpful assistant.",
    stop_words=["<|im_end|>"],
@@ -699,13 +800,6 @@ _register_template(
 )


-_register_template(
-    name="vanilla",
-    format_separator=EmptyFormatter(slots=["\n"]),
-    efficient_eos=True,
-)
-
-
 _register_template(
    name="vicuna",
    format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
@@ -776,7 +870,7 @@ _register_template(
    format_user=StringFormatter(slots=["<|user|>\n{{content}}", {"eos_token"}, "<|assistant|>"]),
    format_assistant=StringFormatter(slots=["\n{{content}}", {"eos_token"}]),
    format_system=StringFormatter(slots=["<|system|>\n{{content}}", {"eos_token"}]),
-    default_system="You are a friendly chatbot who always responds in the style of a pirate",
+    default_system="You are Zephyr, a helpful assistant.",
 )


--- a/src/llmtuner/data/utils.py
+++ b/src/llmtuner/data/utils.py
@@ -78,9 +78,9 @@ def split_dataset(
    if training_args.do_train:
        if data_args.val_size > 1e-6:  # Split the dataset
            if data_args.streaming:
+                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
                val_set = dataset.take(int(data_args.val_size))
                train_set = dataset.skip(int(data_args.val_size))
-                dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
                return {"train_dataset": train_set, "eval_dataset": val_set}
            else:
                val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
--- a/src/llmtuner/eval/evaluator.py
+++ b/src/llmtuner/eval/evaluator.py
@@ -21,7 +21,7 @@ from .template import get_eval_template
 class Evaluator:
    def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
        self.model_args, self.data_args, self.eval_args, finetuning_args = get_eval_args(args)
-        self.tokenizer = load_tokenizer(self.model_args)
+        self.tokenizer = load_tokenizer(self.model_args)["tokenizer"]
        self.tokenizer.padding_side = "right"  # avoid overflow issue in batched inference for llama2
        self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args.template)
        self.model = load_model(self.tokenizer, self.model_args, finetuning_args)
--- a/src/llmtuner/extras/constants.py
+++ b/src/llmtuner/extras/constants.py
@@ -28,6 +28,10 @@ LOG_FILE_NAME = "trainer_log.jsonl"

 METHODS = ["full", "freeze", "lora"]

+MLLM_LIST = ["LLaVA1.5"]
+
+MOD_SUPPORTED_MODELS = ["bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"]
+
 PEFT_METHODS = ["lora"]

 SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]
@@ -45,6 +49,8 @@ TRAINING_STAGES = {

 STAGES_USE_PAIR_DATA = ["rm", "dpo", "orpo"]

+SUPPORTED_CLASS_FOR_S2ATTN = ["llama"]
+
 V_HEAD_WEIGHTS_NAME = "value_head.bin"

 V_HEAD_SAFE_WEIGHTS_NAME = "value_head.safetensors"
@@ -242,6 +248,44 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "CommandR-35B-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-v01",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/c4ai-command-r-v01",
+        },
+        "CommandR-Plus-104B-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-plus",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/c4ai-command-r-plus",
+        },
+        "CommandR-35B-4bit-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-v01-4bit",
+            DownloadSource.MODELSCOPE: "mirror013/c4ai-command-r-v01-4bit",
+        },
+        "CommandR-Plus-104B-4bit-Chat": {
+            DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-plus-4bit",
+        },
+    },
+    template="cohere",
+)
+
+
+register_model_group(
+    models={
+        "DBRX-132B-Base": {
+            DownloadSource.DEFAULT: "databricks/dbrx-base",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-base",
+        },
+        "DBRX-132B-Chat": {
+            DownloadSource.DEFAULT: "databricks/dbrx-instruct",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-instruct",
+        },
+    },
+    module="Wqkv",
+    template="dbrx",
+)
+
+
 register_model_group(
    models={
        "DeepSeek-LLM-7B-Base": {
@@ -262,9 +306,11 @@ register_model_group(
        },
        "DeepSeek-Math-7B-Base": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-math-7b-base",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-math-7b-base",
        },
        "DeepSeek-Math-7B-Chat": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-math-7b-instruct",
+            DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-math-7b-instruct",
        },
        "DeepSeek-MoE-16B-Base": {
            DownloadSource.DEFAULT: "deepseek-ai/deepseek-moe-16b-base",
@@ -363,6 +409,23 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "CodeGemma-2B": {
+            DownloadSource.DEFAULT: "google/codegemma-2b",
+        },
+        "CodeGemma-7B": {
+            DownloadSource.DEFAULT: "google/codegemma-7b",
+        },
+        "CodeGemma-7B-Chat": {
+            DownloadSource.DEFAULT: "google/codegemma-7b-it",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/codegemma-7b-it",
+        },
+    },
+    template="gemma",
+)
+
+
 register_model_group(
    models={
        "InternLM-7B": {
@@ -410,6 +473,16 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Jambda-v0.1": {
+            DownloadSource.DEFAULT: "ai21labs/Jamba-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Jamba-v0.1",
+        }
+    },
+)
+
+
 register_model_group(
    models={
        "LingoWhale-8B": {
@@ -474,6 +547,42 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "LLaMA3-8B": {
+            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B",
+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B",
+        },
+        "LLaMA3-70B": {
+            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B",
+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B",
+        },
+        "LLaMA3-8B-Chat": {
+            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B-Instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B-Instruct",
+        },
+        "LLaMA3-70B-Chat": {
+            DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B-Instruct",
+            DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B-Instruct",
+        },
+    },
+    template="llama3",
+)
+
+
+register_model_group(
+    models={
+        "LLaVA1.5-7B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/llava-1.5-7b-hf",
+        },
+        "LLaVA1.5-13B-Chat": {
+            DownloadSource.DEFAULT: "llava-hf/llava-1.5-13b-hf",
+        },
+    },
+    template="vicuna",
+)
+
+
 register_model_group(
    models={
        "Mistral-7B-v0.1": {
@@ -499,14 +608,21 @@ register_model_group(

 register_model_group(
    models={
-        "Mixtral-8x7B": {
+        "Mixtral-8x7B-v0.1": {
            DownloadSource.DEFAULT: "mistralai/Mixtral-8x7B-v0.1",
            DownloadSource.MODELSCOPE: "AI-ModelScope/Mixtral-8x7B-v0.1",
        },
-        "Mixtral-8x7B-Chat": {
+        "Mixtral-8x7B-v0.1-Chat": {
            DownloadSource.DEFAULT: "mistralai/Mixtral-8x7B-Instruct-v0.1",
            DownloadSource.MODELSCOPE: "AI-ModelScope/Mixtral-8x7B-Instruct-v0.1",
        },
+        "Mixtral-8x22B-v0.1": {
+            DownloadSource.DEFAULT: "mistralai/Mixtral-8x22B-v0.1",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/Mixtral-8x22B-v0.1",
+        },
+        "Mixtral-8x22B-v0.1-Chat": {
+            DownloadSource.DEFAULT: "mistralai/Mixtral-8x22B-Instruct-v0.1",
+        },
    },
    template="mistral",
 )
@@ -515,18 +631,15 @@ register_model_group(
 register_model_group(
    models={
        "OLMo-1B": {
-            DownloadSource.DEFAULT: "allenai/OLMo-1B",
+            DownloadSource.DEFAULT: "allenai/OLMo-1B-hf",
        },
        "OLMo-7B": {
-            DownloadSource.DEFAULT: "allenai/OLMo-7B",
-            DownloadSource.MODELSCOPE: "AI-ModelScope/OLMo-7B",
+            DownloadSource.DEFAULT: "allenai/OLMo-7B-hf",
        },
-        "OLMo-7B-Chat": {
-            DownloadSource.DEFAULT: "allenai/OLMo-7B-Instruct",
+        "OLMo-1.7-7B": {
+            DownloadSource.DEFAULT: "allenai/OLMo-1.7-7B-hf",
        },
    },
-    module="att_proj",
-    template="olmo",
 )


@@ -534,7 +647,7 @@ register_model_group(
    models={
        "OpenChat3.5-7B-Chat": {
            DownloadSource.DEFAULT: "openchat/openchat-3.5-0106",
-            DownloadSource.MODELSCOPE: "myxiongmodel/openchat_3.5",
+            DownloadSource.MODELSCOPE: "xcwzxcwz/openchat-3.5-0106",
        }
    },
    template="openchat",
@@ -582,6 +695,22 @@ register_model_group(
 )


+register_model_group(
+    models={
+        "Phi3-3.8B-4k-Chat": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-mini-4k-instruct",
+            DownloadSource.DEFAULT: "LLM-Research/Phi-3-mini-4k-instruct",
+        },
+        "Phi3-3.8B-128k-Chat": {
+            DownloadSource.DEFAULT: "microsoft/Phi-3-mini-128k-instruct",
+            DownloadSource.DEFAULT: "LLM-Research/Phi-3-mini-128k-instruct",
+        },
+    },
+    module="qkv_proj",
+    template="phi",
+)
+
+
 register_model_group(
    models={
        "Qwen-1.8B": {
@@ -684,10 +813,18 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B",
        },
+        "Qwen1.5-110B": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B",
+            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-110B",
+        },
        "Qwen1.5-MoE-A2.7B": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B",
        },
+        "Qwen1.5-Code-7B": {
+            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B",
+            DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B",
+        },
        "Qwen1.5-0.5B-Chat": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat",
@@ -716,10 +853,18 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat",
        },
+        "Qwen1.5-110B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B-Chat",
+            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-110B-Chat",
+        },
        "Qwen1.5-MoE-A2.7B-Chat": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat",
        },
+        "Qwen1.5-Code-7B-Chat": {
+            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat",
+            DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat",
+        },
        "Qwen1.5-0.5B-int8-Chat": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
@@ -772,10 +917,18 @@ register_model_group(
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-AWQ",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat-AWQ",
        },
+        "Qwen1.5-110B-int4-Chat": {
+            DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-110B-Chat-AWQ",
+        },
        "Qwen1.5-MoE-A2.7B-int4-Chat": {
            DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
            DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
        },
+        "Qwen1.5-Code-7B-int4-Chat": {
+            DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat-AWQ",
+            DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat-AWQ",
+        },
    },
    template="qwen",
 )
@@ -809,12 +962,15 @@ register_model_group(
    models={
        "StarCoder2-3B": {
            DownloadSource.DEFAULT: "bigcode/starcoder2-3b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/starcoder2-3b",
        },
        "StarCoder2-7B": {
            DownloadSource.DEFAULT: "bigcode/starcoder2-7b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/starcoder2-7b",
        },
        "StarCoder2-15B": {
            DownloadSource.DEFAULT: "bigcode/starcoder2-15b",
+            DownloadSource.MODELSCOPE: "AI-ModelScope/starcoder2-15b",
        },
    }
 )
@@ -837,17 +993,53 @@ register_model_group(

 register_model_group(
    models={
+        "XuanYuan-6B": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B",
+        },
        "XuanYuan-70B": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B",
+        },
+        "XuanYuan-2-70B": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B",
+        },
+        "XuanYuan-6B-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat",
        },
        "XuanYuan-70B-Chat": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat",
+        },
+        "XuanYuan-2-70B-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat",
+        },
+        "XuanYuan-6B-int8-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
+        },
+        "XuanYuan-6B-int4-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
        },
        "XuanYuan-70B-int8-Chat": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
        },
        "XuanYuan-70B-int4-Chat": {
            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
+        },
+        "XuanYuan-2-70B-int8-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
+        },
+        "XuanYuan-2-70B-int4-Chat": {
+            DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
+            DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
        },
    },
    template="xuanyuan",
@@ -884,6 +1076,30 @@ register_model_group(
            DownloadSource.DEFAULT: "xverse/XVERSE-65B-Chat",
            DownloadSource.MODELSCOPE: "xverse/XVERSE-65B-Chat",
        },
+        "XVERSE-MoE-A4.2B": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-MoE-A4.2B",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-MoE-A4.2B",
+        },
+        "XVERSE-7B-int8-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-7B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-7B-Chat-GPTQ-Int8",
+        },
+        "XVERSE-7B-int4-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-7B-Chat-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-7B-Chat-GPTQ-Int4",
+        },
+        "XVERSE-13B-int8-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-13B-Chat-GPTQ-Int8",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-13B-Chat-GPTQ-Int8",
+        },
+        "XVERSE-13B-int4-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-13B-Chat-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-13B-Chat-GPTQ-Int4",
+        },
+        "XVERSE-65B-int4-Chat": {
+            DownloadSource.DEFAULT: "xverse/XVERSE-65B-Chat-GPTQ-Int4",
+            DownloadSource.MODELSCOPE: "xverse/XVERSE-65B-Chat-GPTQ-Int4",
+        },
    },
    template="xverse",
 )
@@ -976,21 +1192,9 @@ register_model_group(
            DownloadSource.DEFAULT: "HuggingFaceH4/zephyr-7b-beta",
            DownloadSource.MODELSCOPE: "modelscope/zephyr-7b-beta",
        },
+        "Zephyr-141B-ORPO-Chat": {
+            DownloadSource.DEFAULT: "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
+        },
    },
    template="zephyr",
 )
-
-
-register_model_group(
-    models={
-        "Atom-7B": {
-            DownloadSource.DEFAULT: "FlagAlpha/Atom-7B",
-            DownloadSource.MODELSCOPE: "FlagAlpha/Atom-7B",
-        },
-        "Atom-7B-Chat": {
-            DownloadSource.DEFAULT: "FlagAlpha/Atom-7B-Chat",
-            DownloadSource.MODELSCOPE: "FlagAlpha/Atom-7B-Chat",
-        },
-    },
-    template="atom",
-)
--- a/src/llmtuner/extras/misc.py
+++ b/src/llmtuner/extras/misc.py
@@ -66,7 +66,6 @@ def check_dependencies() -> None:
        require_version("accelerate>=0.27.2", "To fix: pip install accelerate>=0.27.2")
        require_version("peft>=0.10.0", "To fix: pip install peft>=0.10.0")
        require_version("trl>=0.8.1", "To fix: pip install trl>=0.8.1")
-        require_version("gradio>=4.0.0,<=4.21.0", "To fix: pip install gradio==4.21.0")


 def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
@@ -84,6 +83,8 @@ def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
        if param.__class__.__name__ == "Params4bit":
            if hasattr(param, "quant_storage") and hasattr(param.quant_storage, "itemsize"):
                num_bytes = param.quant_storage.itemsize
+            elif hasattr(param, "element_size"):  # for older pytorch version
+                num_bytes = param.element_size()
            else:
                num_bytes = 1

--- a/src/llmtuner/extras/packages.py
+++ b/src/llmtuner/extras/packages.py
@@ -1,16 +1,23 @@
 import importlib.metadata
 import importlib.util
+from typing import TYPE_CHECKING
+
+from packaging import version
+
+
+if TYPE_CHECKING:
+    from packaging.version import Version


 def _is_package_available(name: str) -> bool:
    return importlib.util.find_spec(name) is not None


-def _get_package_version(name: str) -> str:
+def _get_package_version(name: str) -> "Version":
    try:
-        return importlib.metadata.version(name)
+        return version.parse(importlib.metadata.version(name))
    except Exception:
-        return "0.0.0"
+        return version.parse("0.0.0")


 def is_fastapi_availble():
@@ -18,13 +25,17 @@ def is_fastapi_availble():


 def is_flash_attn2_available():
-    return _is_package_available("flash_attn") and _get_package_version("flash_attn").startswith("2")
+    return _is_package_available("flash_attn") and _get_package_version("flash_attn") > version.parse("2.0.0")


 def is_galore_available():
    return _is_package_available("galore_torch")


+def is_gradio_available():
+    return _is_package_available("gradio")
+
+
 def is_jieba_available():
    return _is_package_available("jieba")

@@ -37,6 +48,10 @@ def is_nltk_available():
    return _is_package_available("nltk")


+def is_pillow_available():
+    return _is_package_available("PIL")
+
+
 def is_requests_available():
    return _is_package_available("requests")

@@ -45,14 +60,14 @@ def is_rouge_available():
    return _is_package_available("rouge_chinese")


+def is_sdpa_available():
+    return _get_package_version("torch") > version.parse("2.1.1")
+
+
 def is_starlette_available():
    return _is_package_available("sse_starlette")


-def is_unsloth_available():
-    return _is_package_available("unsloth")
-
-
 def is_uvicorn_available():
    return _is_package_available("uvicorn")

--- a/src/llmtuner/hparams/data_args.py
+++ b/src/llmtuner/hparams/data_args.py
@@ -26,11 +26,11 @@ class DataArguments:
    )
    cutoff_len: int = field(
        default=1024,
-        metadata={"help": "The cutoff length of the model inputs after tokenization."},
+        metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
    )
    reserved_label_len: int = field(
        default=1,
-        metadata={"help": "The minimum cutoff length reserved for label after tokenization."},
+        metadata={"help": "The minimum cutoff length reserved for the tokenized labels in the dataset."},
    )
    train_on_prompt: bool = field(
        default=False,
--- a/src/llmtuner/hparams/finetuning_args.py
+++ b/src/llmtuner/hparams/finetuning_args.py
@@ -172,7 +172,7 @@ class GaloreArguments:

    use_galore: bool = field(
        default=False,
-        metadata={"help": "Whether or not to use gradient low-Rank projection."},
+        metadata={"help": "Whether or not to use the gradient low-Rank projection (GaLore)."},
    )
    galore_target: str = field(
        default="all",
@@ -204,7 +204,54 @@ class GaloreArguments:


@dataclass
-class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments):
+class BAdamArgument:
+    r"""
+    Arguments pertaining to the BAdam optimizer.
+    """
+
+    use_badam: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use the BAdam optimizer."},
+    )
+    badam_mode: Literal["layer", "ratio"] = field(
+        default="layer",
+        metadata={"help": "Whether to use layer-wise or ratio-wise BAdam optimizer."},
+    )
+    badam_start_block: Optional[int] = field(
+        default=None,
+        metadata={"help": "The starting block index for layer-wise BAdam."},
+    )
+    badam_switch_block_every: Optional[int] = field(
+        default=50,
+        metadata={"help": "How often to switch model's block update. Set to -1 to disable the block update."},
+    )
+    badam_switch_mode: Optional[Literal["ascending", "descending", "random", "fixed"]] = field(
+        default="ascending",
+        metadata={"help": "the strategy of picking block to update for layer-wise BAdam."},
+    )
+    badam_update_ratio: float = field(
+        default=0.0,
+        metadata={"help": "The ratio of the update for ratio-wise BAdam."},
+    )
+    badam_mask_mode: Literal["adjacent", "scatter"] = field(
+        default="adjacent",
+        metadata={
+            "help": """The mode of the mask for BAdam optimizer. \
+                    `adjacent` means that the trainable parameters are adjacent to each other, \
+                    `scatter` means that trainable parameters are randomly choosed from the weight."""
+        },
+    )
+    badam_verbose: int = field(
+        default=0,
+        metadata={
+            "help": """The verbosity level of BAdam optimizer. \
+                    0 for no print, 1 for print the block prefix, 2 for print trainable parameters"""
+        },
+    )
+
+
+@dataclass
+class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreArguments, BAdamArgument):
    r"""
    Arguments pertaining to which techniques we are going to fine-tuning with.
    """
@@ -256,11 +303,14 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
            raise ValueError("`dpo_label_smoothing` is only valid for sigmoid loss function.")

        if self.use_llama_pro and self.finetuning_type == "full":
-            raise ValueError("`use_llama_pro` is only valid for the Freeze or LoRA method.")
+            raise ValueError("`use_llama_pro` is only valid for the Freeze or LoRA training.")

        if self.use_galore and self.finetuning_type == "lora":
            raise ValueError("Cannot use LoRA with GaLore together.")

+        if self.loraplus_lr_ratio is not None and self.finetuning_type != "lora":
+            raise ValueError("`loraplus_lr_ratio` is only valid for the LoRA training.")
+
    def save_to_json(self, json_path: str):
        r"""Saves the content of this instance in JSON format inside `json_path`."""
        json_string = json.dumps(asdict(self), indent=2, sort_keys=True) + "\n"
--- a/src/llmtuner/hparams/generating_args.py
+++ b/src/llmtuner/hparams/generating_args.py
@@ -31,11 +31,11 @@ class GeneratingArguments:
        metadata={"help": "Number of beams for beam search. 1 means no beam search."},
    )
    max_length: int = field(
-        default=512,
+        default=1024,
        metadata={"help": "The maximum length the generated tokens can have. It can be overridden by max_new_tokens."},
    )
    max_new_tokens: int = field(
-        default=512,
+        default=1024,
        metadata={"help": "The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt."},
    )
    repetition_penalty: float = field(
--- a/src/llmtuner/hparams/model_args.py
+++ b/src/llmtuner/hparams/model_args.py
@@ -22,7 +22,7 @@ class ModelArguments:
        metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
    )
    use_fast_tokenizer: bool = field(
-        default=False,
+        default=True,
        metadata={"help": "Whether or not to use one of the fast tokenizer (backed by the tokenizers library)."},
    )
    resize_vocab: bool = field(
@@ -33,6 +33,10 @@ class ModelArguments:
        default=False,
        metadata={"help": "Whether or not the special tokens should be split during the tokenization process."},
    )
+    new_special_tokens: Optional[str] = field(
+        default=None,
+        metadata={"help": "Special tokens to be added into the tokenizer."},
+    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
@@ -55,24 +59,32 @@ class ModelArguments:
    )
    quantization_device_map: Optional[Literal["auto"]] = field(
        default=None,
-        metadata={"help": "Device map used for loading the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
+        metadata={"help": "Device map used to infer the 4-bit quantized model, needs bitsandbytes>=0.43.0."},
    )
    rope_scaling: Optional[Literal["linear", "dynamic"]] = field(
        default=None,
        metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."},
    )
-    flash_attn: bool = field(
-        default=False,
-        metadata={"help": "Enable FlashAttention-2 for faster training."},
+    flash_attn: Literal["off", "sdpa", "fa2", "auto"] = field(
+        default="auto",
+        metadata={"help": "Enable FlashAttention for faster training and inference."},
    )
    shift_attn: bool = field(
        default=False,
        metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."},
    )
+    mixture_of_depths: Optional[Literal["convert", "load"]] = field(
+        default=None,
+        metadata={"help": "Convert the model to mixture-of-depths (MoD) or load the MoD model."},
+    )
    use_unsloth: bool = field(
        default=False,
        metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."},
    )
+    visual_inputs: bool = field(
+        default=False,
+        metadata={"help": "Whethor or not to use multimodal LLM that accepts visual inputs."},
+    )
    moe_aux_loss_coef: Optional[float] = field(
        default=None,
        metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},
@@ -129,6 +141,10 @@ class ModelArguments:
        default=1,
        metadata={"help": "The file shard size (in GB) of the exported model."},
    )
+    export_device: str = field(
+        default="cpu",
+        metadata={"help": "The device used in model export, use cuda to avoid addmm errors."},
+    )
    export_quantization_bit: Optional[int] = field(
        default=None,
        metadata={"help": "The number of bits to quantize the exported model."},
@@ -166,9 +182,15 @@ class ModelArguments:
        if self.split_special_tokens and self.use_fast_tokenizer:
            raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")

+        if self.visual_inputs and self.use_unsloth:
+            raise ValueError("Unsloth does not support MLLM yet. Stay tuned.")
+
        if self.adapter_name_or_path is not None:  # support merging multiple lora weights
            self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")]

+        if self.new_special_tokens is not None:  # support multiple special tokens
+            self.new_special_tokens = [token.strip() for token in self.new_special_tokens.split(",")]
+
        assert self.quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
        assert self.export_quantization_bit in [None, 8, 4, 3, 2], "We only accept 2/3/4/8-bit quantization."

--- a/src/llmtuner/hparams/parser.py
+++ b/src/llmtuner/hparams/parser.py
@@ -8,10 +8,10 @@ import transformers
 from transformers import HfArgumentParser, Seq2SeqTrainingArguments
 from transformers.trainer_utils import get_last_checkpoint
 from transformers.utils import is_torch_bf16_gpu_available
+from transformers.utils.versions import require_version

 from ..extras.logging import get_logger
-from ..extras.misc import check_dependencies
-from ..extras.packages import is_unsloth_available
+from ..extras.misc import check_dependencies, get_current_device
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments
@@ -67,6 +67,9 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
        if finetuning_args.finetuning_type != "lora":
            raise ValueError("Quantization is only compatible with the LoRA method.")

+        if model_args.resize_vocab:
+            raise ValueError("Cannot resize embedding layers of a quantized model.")
+
        if model_args.adapter_name_or_path is not None and finetuning_args.create_new_adapter:
            raise ValueError("Cannot create new adapter upon a quantized model.")

@@ -74,6 +77,35 @@ def _verify_model_args(model_args: "ModelArguments", finetuning_args: "Finetunin
            raise ValueError("Quantized model only accepts a single adapter. Merge them first.")


+def _check_extra_dependencies(
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    training_args: Optional["Seq2SeqTrainingArguments"] = None,
+) -> None:
+    if model_args.use_unsloth:
+        require_version("unsloth", "Please install unsloth: https://github.com/unslothai/unsloth")
+
+    if model_args.mixture_of_depths is not None:
+        require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")
+
+    if model_args.infer_backend == "vllm":
+        require_version("vllm>=0.4.0", "To fix: pip install vllm>=0.4.0")
+
+    if finetuning_args.use_galore:
+        require_version("galore_torch", "To fix: pip install galore_torch")
+
+    if finetuning_args.use_badam:
+        require_version("badam", "To fix: pip install badam")
+
+    if finetuning_args.plot_loss:
+        require_version("matplotlib", "To fix: pip install matplotlib")
+
+    if training_args is not None and training_args.predict_with_generate:
+        require_version("jieba", "To fix: pip install jieba")
+        require_version("nltk", "To fix: pip install nltk")
+        require_version("rouge_chinese", "To fix: pip install rouge-chinese")
+
+
 def _parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
    parser = HfArgumentParser(_TRAIN_ARGS)
    return _parse_args(parser, args)
@@ -131,8 +163,8 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
    if training_args.do_train and training_args.predict_with_generate:
        raise ValueError("`predict_with_generate` cannot be set as True while training.")

-    if training_args.do_train and model_args.use_unsloth and not is_unsloth_available():
-        raise ValueError("Unsloth was not installed: https://github.com/unslothai/unsloth")
+    if training_args.do_train and model_args.quantization_device_map == "auto":
+        raise ValueError("Cannot use device map for quantized models in training.")

    if finetuning_args.use_dora and model_args.use_unsloth:
        raise ValueError("Unsloth does not support DoRA.")
@@ -151,21 +183,33 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
    ):
        raise ValueError("Distributed training does not support layer-wise GaLore.")

-    if finetuning_args.use_galore and training_args.deepspeed is not None:
-        raise ValueError("GaLore is incompatible with DeepSpeed.")
+    if (
+        finetuning_args.use_badam
+        and finetuning_args.badam_mode == "layer"
+        and training_args.parallel_mode.value == "distributed"
+    ):
+        raise ValueError("Layer-wise BAdam does not yet support distributed training, use ratio-wise BAdam.")
+
+    if (finetuning_args.use_galore or finetuning_args.use_badam) and training_args.deepspeed is not None:
+        raise ValueError("GaLore and BAdam are incompatible with DeepSpeed yet.")

    if model_args.infer_backend == "vllm":
        raise ValueError("vLLM backend is only available for API, CLI and Web.")

+    if model_args.visual_inputs and data_args.packing:
+        raise ValueError("Cannot use packing in MLLM fine-tuning.")
+
    _verify_model_args(model_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args, training_args)

    if (
        training_args.do_train
        and finetuning_args.finetuning_type == "lora"
+        and model_args.quantization_bit is None
        and model_args.resize_vocab
        and finetuning_args.additional_target is None
    ):
-        logger.warning("Add token embeddings to `additional_target` to make the added tokens trainable.")
+        logger.warning("Remember to add embedding layers to `additional_target` to make the added tokens trainable.")

    if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
        logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
@@ -235,6 +279,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
    elif training_args.fp16:
        model_args.compute_dtype = torch.float16

+    model_args.device_map = {"": get_current_device()}
    model_args.model_max_length = data_args.cutoff_len
    data_args.packing = data_args.packing if data_args.packing is not None else finetuning_args.stage == "pt"

@@ -266,18 +311,25 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
        if finetuning_args.stage != "sft":
            raise ValueError("vLLM engine only supports auto-regressive models.")

-        if model_args.adapter_name_or_path is not None:
-            raise ValueError("vLLM engine does not support LoRA adapters. Merge them first.")
-
        if model_args.quantization_bit is not None:
-            raise ValueError("vLLM engine does not support quantization.")
+            raise ValueError("vLLM engine does not support bnb quantization (GPTQ and AWQ are supported).")

        if model_args.rope_scaling is not None:
            raise ValueError("vLLM engine does not support RoPE scaling.")

-    _verify_model_args(model_args, finetuning_args)
+        if model_args.adapter_name_or_path is not None and len(model_args.adapter_name_or_path) != 1:
+            raise ValueError("vLLM only accepts a single adapter. Merge them first.")

-    model_args.device_map = "auto"
+    if finetuning_args.stage == "rm" and model_args.visual_inputs:
+        raise ValueError("Reward server does not support MLLM yet. Stay tuned.")
+
+    _verify_model_args(model_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args)
+
+    if model_args.export_dir is not None:
+        model_args.device_map = {"": torch.device(model_args.export_device)}
+    else:
+        model_args.device_map = "auto"

    return model_args, data_args, finetuning_args, generating_args

@@ -294,6 +346,7 @@ def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
        raise ValueError("vLLM backend is only available for API, CLI and Web.")

    _verify_model_args(model_args, finetuning_args)
+    _check_extra_dependencies(model_args, finetuning_args)

    model_args.device_map = "auto"

--- a/src/llmtuner/model/init.py
+++ b/src/llmtuner/model/init.py
@@ -1,8 +1,10 @@
-from .loader import load_model, load_tokenizer
-from .utils import find_all_linear_modules, load_valuehead_params
+from .loader import load_config, load_model, load_tokenizer
+from .utils.misc import find_all_linear_modules
+from .utils.valuehead import load_valuehead_params


 __all__ = [
+    "load_config",
    "load_model",
    "load_tokenizer",
    "load_valuehead_params",
--- a/src/llmtuner/model/adapter.py
+++ b/src/llmtuner/model/adapter.py
@@ -5,11 +5,13 @@ from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
 from transformers.integrations import is_deepspeed_zero3_enabled

 from ..extras.logging import get_logger
-from .utils import QuantizationMethod, find_all_linear_modules, find_expanded_modules
+from .utils.misc import find_all_linear_modules, find_expanded_modules
+from .utils.quantization import QuantizationMethod
+from .utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model


 if TYPE_CHECKING:
-    from transformers.modeling_utils import PreTrainedModel
+    from transformers import PretrainedConfig, PreTrainedModel

    from ..hparams import FinetuningArguments, ModelArguments

@@ -18,7 +20,11 @@ logger = get_logger(__name__)


 def init_adapter(
-    model: "PreTrainedModel", model_args: "ModelArguments", finetuning_args: "FinetuningArguments", is_trainable: bool
+    config: "PretrainedConfig",
+    model: "PreTrainedModel",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
 ) -> "PreTrainedModel":
    r"""
    Initializes the adapters.
@@ -32,9 +38,12 @@ def init_adapter(
        logger.info("Adapter is not found at evaluation, load the base model.")
        return model

+    if finetuning_args.finetuning_type != "lora" and getattr(model, "quantization_method", None):
+        raise ValueError("You can only use lora for quantized models.")
+
    if finetuning_args.finetuning_type == "full" and is_trainable:
        logger.info("Fine-tuning method: Full")
-        if not finetuning_args.pure_bf16:
+        if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam):
            model = model.float()

    if finetuning_args.finetuning_type == "freeze" and is_trainable:
@@ -66,6 +75,8 @@ def init_adapter(
        for name, _ in model.named_modules():
            if ".0." in name:
                freeze_modules.add(name.split(".0.")[-1].split(".")[0])
+            elif ".1." in name:  # MoD starts from layer 1
+                freeze_modules.add(name.split(".1.")[-1].split(".")[0])

        trainable_layers = []
        for module_name in finetuning_args.name_module_trainable:
@@ -79,7 +90,7 @@ def init_adapter(

        for name, param in model.named_parameters():
            if any(trainable_layer in name for trainable_layer in trainable_layers):
-                if not finetuning_args.pure_bf16:
+                if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam):
                    param.data = param.data.to(torch.float32)
            else:
                param.requires_grad_(False)
@@ -100,6 +111,10 @@ def init_adapter(
                assert len(model_args.adapter_name_or_path) == 1, "Cannot use multiple adapters in DeepSpeed ZeRO-3."
                is_mergeable = False

+            if model_args.use_unsloth:
+                assert len(model_args.adapter_name_or_path) == 1, "Unsloth model only accepts a single adapter."
+                is_mergeable = False
+
            if (is_trainable and not finetuning_args.create_new_adapter) or (not is_mergeable):
                adapter_to_merge = model_args.adapter_name_or_path[:-1]
                adapter_to_resume = model_args.adapter_name_or_path[-1]
@@ -116,9 +131,15 @@ def init_adapter(
                logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))

            if adapter_to_resume is not None:  # resume lora training
-                model = PeftModel.from_pretrained(
-                    model, adapter_to_resume, is_trainable=is_trainable, offload_folder=model_args.offload_folder
-                )
+                if model_args.use_unsloth:
+                    model = load_unsloth_peft_model(config, model_args, is_trainable=is_trainable)
+                else:
+                    model = PeftModel.from_pretrained(
+                        model,
+                        adapter_to_resume,
+                        is_trainable=is_trainable,
+                        offload_folder=model_args.offload_folder,
+                    )

        if is_trainable and adapter_to_resume is None:  # create new lora weights while training
            if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
@@ -129,9 +150,23 @@ def init_adapter(
            if finetuning_args.use_llama_pro:
                target_modules = find_expanded_modules(model, target_modules, finetuning_args.num_layer_trainable)

-            if finetuning_args.use_dora and getattr(model, "quantization_method", None) is not None:
-                if getattr(model, "quantization_method", None) != QuantizationMethod.BITS_AND_BYTES:
-                    raise ValueError("DoRA is not compatible with PTQ-quantized models.")
+            if (
+                finetuning_args.use_dora
+                and getattr(model, "quantization_method", None) is not None
+                and getattr(model, "quantization_method", None) != QuantizationMethod.BITS_AND_BYTES
+            ):
+                raise ValueError("DoRA is not compatible with PTQ-quantized models.")
+
+            if model_args.resize_vocab and finetuning_args.additional_target is None:
+                input_embeddings = model.get_input_embeddings()
+                output_embeddings = model.get_output_embeddings()
+                module_names = set()
+                for name, module in model.named_modules():
+                    if module in [input_embeddings, output_embeddings]:
+                        module_names.add(name.split(".")[-1])
+
+                finetuning_args.additional_target = module_names
+                logger.warning("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))

            peft_kwargs = {
                "r": finetuning_args.lora_rank,
@@ -139,24 +174,21 @@ def init_adapter(
                "lora_alpha": finetuning_args.lora_alpha,
                "lora_dropout": finetuning_args.lora_dropout,
                "use_rslora": finetuning_args.use_rslora,
+                "modules_to_save": finetuning_args.additional_target,
            }

            if model_args.use_unsloth:
-                from unsloth import FastLanguageModel  # type: ignore
-
-                unsloth_peft_kwargs = {"model": model, "max_seq_length": model_args.model_max_length}
-                model = FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
+                model = get_unsloth_peft_model(model, model_args, peft_kwargs)
            else:
                lora_config = LoraConfig(
                    task_type=TaskType.CAUSAL_LM,
                    inference_mode=False,
-                    modules_to_save=finetuning_args.additional_target,
                    use_dora=finetuning_args.use_dora,
                    **peft_kwargs,
                )
                model = get_peft_model(model, lora_config)

-        if not finetuning_args.pure_bf16:
+        if (not finetuning_args.pure_bf16) and (not finetuning_args.use_badam):
            for param in filter(lambda p: p.requires_grad, model.parameters()):
                param.data = param.data.to(torch.float32)

--- a/src/llmtuner/model/loader.py
+++ b/src/llmtuner/model/loader.py
@@ -1,17 +1,20 @@
-from typing import TYPE_CHECKING, Any, Dict
+from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict

-from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
 from trl import AutoModelForCausalLMWithValueHead

 from ..extras.logging import get_logger
-from ..extras.misc import count_parameters, get_current_device, try_download_model_from_ms
+from ..extras.misc import count_parameters, try_download_model_from_ms
 from .adapter import init_adapter
 from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
-from .utils import load_valuehead_params, register_autoclass
+from .utils.misc import register_autoclass
+from .utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
+from .utils.unsloth import load_unsloth_pretrained_model
+from .utils.valuehead import load_valuehead_params


 if TYPE_CHECKING:
-    from transformers import PreTrainedModel, PreTrainedTokenizer
+    from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer, ProcessorMixin

    from ..hparams import FinetuningArguments, ModelArguments

@@ -19,7 +22,17 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)


+class TokenizerModule(TypedDict):
+    tokenizer: "PreTrainedTokenizer"
+    processor: Optional["ProcessorMixin"]
+
+
 def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
+    r"""
+    Gets arguments to load config/tokenizer/model.
+
+    Note: including inplace operation of model_args.
+    """
    model_args.model_name_or_path = try_download_model_from_ms(model_args)
    return {
        "trust_remote_code": True,
@@ -29,22 +42,56 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
    }


-def load_tokenizer(model_args: "ModelArguments") -> "PreTrainedTokenizer":
+def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
    r"""
-    Loads pretrained tokenizer. Must before load_model.
+    Loads pretrained tokenizer.

    Note: including inplace operation of model_args.
    """
    init_kwargs = _get_init_kwargs(model_args)
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.model_name_or_path,
-        use_fast=model_args.use_fast_tokenizer,
-        split_special_tokens=model_args.split_special_tokens,
-        padding_side="right",
-        **init_kwargs,
-    )
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path,
+            use_fast=model_args.use_fast_tokenizer,
+            split_special_tokens=model_args.split_special_tokens,
+            padding_side="right",
+            **init_kwargs,
+        )
+    except ValueError:  # try the fast one
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path,
+            use_fast=True,
+            padding_side="right",
+            **init_kwargs,
+        )
+
+    if model_args.new_special_tokens is not None:
+        num_added_tokens = tokenizer.add_special_tokens(
+            dict(additional_special_tokens=model_args.new_special_tokens),
+            replace_additional_special_tokens=False,
+        )
+        logger.info("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
+        if num_added_tokens > 0 and not model_args.resize_vocab:
+            model_args.resize_vocab = True
+            logger.warning("New tokens have been added, changed `resize_vocab` to True.")
+
    patch_tokenizer(tokenizer)
-    return tokenizer
+
+    if model_args.visual_inputs:
+        processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
+        setattr(processor, "tokenizer", tokenizer)
+    else:
+        processor = None
+
+    return {"tokenizer": tokenizer, "processor": processor}
+
+
+def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
+    r"""
+    Loads model config.
+    """
+    init_kwargs = _get_init_kwargs(model_args)
+    return AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)


 def load_model(
@@ -55,45 +102,42 @@ def load_model(
    add_valuehead: bool = False,
 ) -> "PreTrainedModel":
    r"""
-    Loads pretrained model. Must after load_tokenizer.
+    Loads pretrained model.
    """
    init_kwargs = _get_init_kwargs(model_args)
-    config = AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
-    patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
+    config = load_config(model_args)
+    patch_config(config, tokenizer, model_args, init_kwargs, is_trainable, add_valuehead)

    model = None
-    if is_trainable and model_args.use_unsloth:
-        from unsloth import FastLanguageModel  # type: ignore
+    lazy_load = False
+    if model_args.use_unsloth:
+        if model_args.adapter_name_or_path is not None:
+            lazy_load = True
+        elif is_trainable:
+            model = load_unsloth_pretrained_model(config, model_args)

-        unsloth_kwargs = {
-            "model_name": model_args.model_name_or_path,
-            "max_seq_length": model_args.model_max_length,
-            "dtype": model_args.compute_dtype,
-            "load_in_4bit": model_args.quantization_bit == 4,
-            "token": model_args.hf_hub_token,
-            "device_map": {"": get_current_device()},
-            "rope_scaling": getattr(config, "rope_scaling", None),
-        }
-        try:
-            model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
-        except NotImplementedError:
-            logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
-            model_args.use_unsloth = False
+    if model is None and not lazy_load:
+        init_kwargs["config"] = config
+        init_kwargs["pretrained_model_name_or_path"] = model_args.model_name_or_path

-        if model_args.adapter_name_or_path:
-            model_args.adapter_name_or_path = None
-            logger.warning("Unsloth does not support loading adapters.")
+        if model_args.mixture_of_depths == "load":
+            model = load_mod_pretrained_model(**init_kwargs)
+        elif model_args.visual_inputs:
+            model = AutoModelForVision2Seq.from_pretrained(**init_kwargs)
+        else:
+            model = AutoModelForCausalLM.from_pretrained(**init_kwargs)

-    if model is None:
-        model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, config=config, **init_kwargs)
+        if model_args.mixture_of_depths == "convert":
+            model = convert_pretrained_model_to_mod(model, config, model_args)

-    patch_model(model, tokenizer, model_args, is_trainable)
-    register_autoclass(config, model, tokenizer)
+    if not lazy_load:
+        patch_model(model, tokenizer, model_args, is_trainable, add_valuehead)
+        register_autoclass(config, model, tokenizer)

-    model = init_adapter(model, model_args, finetuning_args, is_trainable)
+    model = init_adapter(config, model, model_args, finetuning_args, is_trainable)

    if add_valuehead:
-        model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
+        model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
        patch_valuehead_model(model)

        if model_args.adapter_name_or_path is not None:
--- a/src/llmtuner/model/patcher.py
+++ b/src/llmtuner/model/patcher.py
@@ -1,23 +1,22 @@
-import math
-import os
-import random
-from contextlib import nullcontext
 from types import MethodType
-from typing import TYPE_CHECKING, Any, Dict, List, Tuple
+from typing import TYPE_CHECKING, Any, Dict

 import torch
-from datasets import load_dataset
 from peft import PeftModel
-from transformers import BitsAndBytesConfig, GPTQConfig, PreTrainedModel, PreTrainedTokenizerBase
+from transformers import PreTrainedModel, PreTrainedTokenizerBase
 from transformers.integrations import is_deepspeed_zero3_enabled
-from transformers.utils.versions import require_version

-from ..extras.constants import FILEEXT2TYPE, LAYERNORM_NAMES
 from ..extras.logging import get_logger
-from ..extras.misc import get_current_device, infer_optim_dtype
-from ..extras.packages import is_flash_attn2_available
-from ..extras.patches.llama_patch import apply_llama_patch
-from .utils import QuantizationMethod, add_z3_leaf_module
+from ..extras.misc import infer_optim_dtype
+from .utils.attention import configure_attn_implementation, print_attn_implementation
+from .utils.checkpointing import prepare_model_for_training
+from .utils.embedding import resize_embedding_layer
+from .utils.longlora import configure_longlora
+from .utils.moe import add_z3_leaf_module, configure_moe
+from .utils.quantization import configure_quantization
+from .utils.rope import configure_rope
+from .utils.valuehead import configure_valuehead, prepare_valuehead_model
+from .utils.visual import autocast_projector_dtype


 if TYPE_CHECKING:
@@ -28,254 +27,6 @@ if TYPE_CHECKING:


 logger = get_logger(__name__)
-SUPPORTED_CLASS_FOR_S2ATTN = ["llama"]
-
-
-def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
-    r"""
-    Inspired by: https://github.com/huggingface/optimum/blob/v1.16.0/optimum/gptq/data.py#L133
-    TODO: remove tokenizer.decode() https://github.com/huggingface/optimum/pull/1600
-    """
-    if os.path.isfile(model_args.export_quantization_dataset):
-        data_path = FILEEXT2TYPE.get(model_args.export_quantization_dataset.split(".")[-1], None)
-        data_files = model_args.export_quantization_dataset
-    else:
-        data_path = model_args.export_quantization_dataset
-        data_files = None
-
-    dataset = load_dataset(path=data_path, data_files=data_files, split="train", cache_dir=model_args.cache_dir)
-    maxlen = model_args.export_quantization_maxlen
-
-    samples = []
-    for _ in range(model_args.export_quantization_nsamples):
-        while True:
-            sample_idx = random.randint(0, len(dataset) - 1)
-            sample: Dict[str, torch.Tensor] = tokenizer(dataset[sample_idx]["text"], return_tensors="pt")
-            if sample["input_ids"].size(1) >= maxlen:
-                break  # TODO: fix large maxlen
-
-        word_idx = random.randint(0, sample["input_ids"].size(1) - maxlen - 1)
-        input_ids = sample["input_ids"][:, word_idx : word_idx + maxlen]
-        samples.append(tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True))
-
-    return samples
-
-
-def _configure_attn_implementation(
-    config: "PretrainedConfig", model_args: "ModelArguments", init_kwargs: Dict[str, Any]
-) -> None:
-    if model_args.flash_attn:
-        if not is_flash_attn2_available():
-            logger.warning("FlashAttention2 is not installed.")
-            return
-
-        logger.info("Using FlashAttention-2 for faster training and inference.")
-        if getattr(config, "model_type", None) == "internlm2":  # special case for custom models
-            setattr(config, "attn_implementation", "flash_attention_2")
-        else:
-            init_kwargs["attn_implementation"] = "flash_attention_2"
-    else:
-        init_kwargs["attn_implementation"] = "eager"
-
-
-def _configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
-    if model_args.rope_scaling is None:
-        return
-
-    if not hasattr(config, "rope_scaling"):
-        logger.warning("Current model does not support RoPE scaling.")
-        return
-
-    if is_trainable:
-        if model_args.rope_scaling == "dynamic":
-            logger.warning(
-                "Dynamic NTK scaling may not work well with fine-tuning. "
-                "See: https://github.com/huggingface/transformers/pull/24653"
-            )
-
-        current_max_length = getattr(config, "max_position_embeddings", None)
-        if current_max_length and model_args.model_max_length > current_max_length:
-            scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
-        else:
-            logger.warning("Input length is smaller than max length. Consider increase input length.")
-            scaling_factor = 1.0
-    else:
-        scaling_factor = 2.0
-
-    setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
-    logger.info(
-        "Using {} scaling strategy and setting scaling factor to {}".format(model_args.rope_scaling, scaling_factor)
-    )
-
-
-def _configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
-    if not is_trainable or not model_args.shift_attn:
-        return
-
-    if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
-        setattr(config, "group_size_ratio", 0.25)
-        apply_llama_patch()
-        logger.info("Using shift short attention with group_size_ratio=1/4.")
-    else:
-        logger.warning("Current model does not support shift short attention.")
-
-
-def _configure_quantization(
-    config: "PretrainedConfig",
-    tokenizer: "PreTrainedTokenizer",
-    model_args: "ModelArguments",
-    init_kwargs: Dict[str, Any],
-) -> None:
-    r"""
-    Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
-    """
-    if getattr(config, "quantization_config", None):  # ptq
-        if is_deepspeed_zero3_enabled():
-            raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantized models.")
-
-        init_kwargs["device_map"] = {"": get_current_device()}
-        quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
-        quant_method = quantization_config.get("quant_method", "")
-
-        if quant_method == QuantizationMethod.GPTQ:
-            require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
-            quantization_config["use_exllama"] = False  # disable exllama
-
-        if quant_method == QuantizationMethod.AWQ:
-            require_version("autoawq", "To fix: pip install autoawq")
-
-        if quant_method == QuantizationMethod.AQLM:
-            require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
-            require_version("aqlm>=1.1.0", "To fix: pip install aqlm[gpu]>=1.1.0")
-            quantization_config["bits"] = 2
-
-        quant_bits = quantization_config.get("bits", "?")
-        logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
-
-    elif model_args.export_quantization_bit is not None:  # auto-gptq
-        require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
-        require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
-        from accelerate.utils import get_max_memory
-
-        if getattr(config, "model_type", None) == "chatglm":
-            raise ValueError("ChatGLM model is not supported.")
-
-        init_kwargs["quantization_config"] = GPTQConfig(
-            bits=model_args.export_quantization_bit,
-            tokenizer=tokenizer,
-            dataset=_get_quantization_dataset(tokenizer, model_args),
-        )
-        init_kwargs["device_map"] = "auto"
-        init_kwargs["max_memory"] = get_max_memory()
-        logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
-
-    elif model_args.quantization_bit is not None:  # bnb
-        if model_args.quantization_bit == 8:
-            require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
-            init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
-
-        elif model_args.quantization_bit == 4:
-            require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
-            init_kwargs["quantization_config"] = BitsAndBytesConfig(
-                load_in_4bit=True,
-                bnb_4bit_compute_dtype=model_args.compute_dtype,
-                bnb_4bit_use_double_quant=model_args.double_quantization,
-                bnb_4bit_quant_type=model_args.quantization_type,
-                bnb_4bit_quant_storage=model_args.compute_dtype,  # crucial for fsdp qlora
-            )
-
-        if is_deepspeed_zero3_enabled() or model_args.quantization_device_map == "auto":
-            if model_args.quantization_bit != 4:
-                raise ValueError("Only 4-bit quantized model can use auto device map.")
-
-            require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
-            require_version("accelerate>=0.28.0", "To fix: pip install accelerate>=0.28.0")
-            require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
-        else:
-            init_kwargs["device_map"] = {"": get_current_device()}
-
-        logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
-
-
-def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int):
-    embedding_dim = embed_weight.size(1)
-    avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
-    noise_weight = torch.empty_like(embed_weight[-num_new_tokens:])
-    noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim)))
-    embed_weight[-num_new_tokens:] = avg_weight + noise_weight
-
-
-def _resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
-    r"""
-    Resize token embeddings.
-    """
-    if is_deepspeed_zero3_enabled():
-        import deepspeed  # type: ignore
-
-        params = [model.get_input_embeddings().weight]
-        if model.get_output_embeddings() is not None and not model.config.tie_word_embeddings:
-            params.append(model.get_output_embeddings().weight)
-
-        context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0)
-    else:
-        context_maybe_zero3 = nullcontext()
-
-    with context_maybe_zero3:
-        current_embedding_size = model.get_input_embeddings().weight.size(0)
-
-    if len(tokenizer) > current_embedding_size:
-        if not isinstance(model.get_output_embeddings(), torch.nn.Linear):
-            logger.warning("Current model does not support resizing token embeddings.")
-            return
-
-        model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)
-        with context_maybe_zero3:
-            new_embedding_size = model.get_input_embeddings().weight.size(0)
-            num_new_tokens = new_embedding_size - current_embedding_size
-            _noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
-            _noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
-
-        logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
-
-
-def _fp32_forward_post_hook(
-    module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
-) -> "torch.Tensor":
-    return output.to(torch.float32)
-
-
-def _prepare_model_for_training(
-    model: "PreTrainedModel", model_args: "ModelArguments", output_layer_name: str = "lm_head"
-) -> None:
-    r"""
-    Includes:
-        (1) cast the layernorm in fp32
-        (2) make output embedding layer require grads
-        (3) add the upcasting of the lm_head in fp32
-    Inspired by: https://github.com/huggingface/peft/blob/v0.7.1/src/peft/utils/other.py#L72
-    """
-    if model_args.upcast_layernorm:
-        logger.info("Upcasting layernorm weights in float32.")
-        for name, param in model.named_parameters():
-            if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
-                param.data = param.data.to(torch.float32)
-
-    if not model_args.disable_gradient_checkpointing:
-        if not getattr(model, "supports_gradient_checkpointing", False):
-            logger.warning("Current model does not support gradient checkpointing.")
-        else:
-            # use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
-            # According to: https://github.com/huggingface/transformers/issues/28339
-            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
-            model.enable_input_require_grads()
-            setattr(model.config, "use_cache", False)  # turn off when gradient checkpointing is enabled
-            logger.info("Gradient checkpointing enabled.")
-
-    if hasattr(model, output_layer_name) and model_args.upcast_lmhead_output:
-        logger.info("Upcasting lm_head outputs in float32.")
-        output_layer = getattr(model, output_layer_name)
-        if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
-            output_layer.register_forward_hook(_fp32_forward_post_hook)


 def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
@@ -289,25 +40,24 @@ def patch_config(
    model_args: "ModelArguments",
    init_kwargs: Dict[str, Any],
    is_trainable: bool,
+    add_valuehead: bool,
 ) -> None:
    if model_args.compute_dtype is None:  # priority: bf16 > fp16 > fp32
        model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))

-    _configure_attn_implementation(config, model_args, init_kwargs)
-    _configure_rope(config, model_args, is_trainable)
-    _configure_longlora(config, model_args, is_trainable)
-    _configure_quantization(config, tokenizer, model_args, init_kwargs)
+    configure_attn_implementation(config, model_args)
+    configure_rope(config, model_args, is_trainable)
+    configure_longlora(config, model_args, is_trainable)
+    configure_quantization(config, tokenizer, model_args, init_kwargs)
+    configure_moe(config, model_args, is_trainable)
+
+    if add_valuehead:
+        configure_valuehead(config)

    if model_args.use_cache and not is_trainable:
        setattr(config, "use_cache", True)
        logger.info("Using KV cache for faster generation.")

-    if model_args.moe_aux_loss_coef is not None:
-        if getattr(config, "model_type", None) in ["mixtral", "qwen2_moe"]:
-            setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
-        elif getattr(config, "model_type", None) == "deepseek":
-            setattr(config, "aux_loss_alpha", model_args.moe_aux_loss_coef)
-
    if getattr(config, "model_type", None) == "qwen":
        setattr(config, "use_flash_attn", model_args.flash_attn)
        for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]:
@@ -316,22 +66,23 @@ def patch_config(
    if getattr(config, "model_type", None) == "qwen2" and is_trainable and model_args.flash_attn:
        setattr(config, "use_cache", False)  # qwen2 does not support use_cache when using flashattn

-    if getattr(config, "model_type", None) == "qwen2_moe" and is_trainable:
-        setattr(config, "output_router_logits", True)
-
    init_kwargs["torch_dtype"] = model_args.compute_dtype
    if not is_deepspeed_zero3_enabled():
        init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage
        if init_kwargs["low_cpu_mem_usage"]:
-            if "device_map" not in init_kwargs:
-                init_kwargs["device_map"] = model_args.device_map or {"": get_current_device()}
+            if "device_map" not in init_kwargs and model_args.device_map:
+                init_kwargs["device_map"] = model_args.device_map

            if init_kwargs["device_map"] == "auto":
                init_kwargs["offload_folder"] = model_args.offload_folder


 def patch_model(
-    model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments", is_trainable: bool
+    model: "PreTrainedModel",
+    tokenizer: "PreTrainedTokenizer",
+    model_args: "ModelArguments",
+    is_trainable: bool,
+    add_valuehead: bool,
 ) -> None:
    gen_config = model.generation_config  # check and fix generation config
    if not gen_config.do_sample and (
@@ -344,25 +95,21 @@ def patch_model(
    if "GenerationMixin" not in str(model.generate.__func__):
        model.generate = MethodType(PreTrainedModel.generate, model)

-    if is_trainable and getattr(model.config, "model_type", None) == "chatglm":
-        setattr(model, "lm_head", model.transformer.output_layer)
-        setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
+    if add_valuehead:
+        prepare_valuehead_model(model)

    if model_args.resize_vocab:
-        _resize_embedding_layer(model, tokenizer)
+        resize_embedding_layer(model, tokenizer)
+
+    if model_args.visual_inputs:
+        autocast_projector_dtype(model, model_args)

    if is_trainable:
-        _prepare_model_for_training(model, model_args)
+        prepare_model_for_training(model, model_args)
+        add_z3_leaf_module(model)

-    if getattr(model.config, "model_type", None) == "mixtral":
-        from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
-
-        add_z3_leaf_module(model, MixtralSparseMoeBlock)
-
-    if getattr(model.config, "model_type", None) == "qwen2moe":
-        from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
-
-        add_z3_leaf_module(model, Qwen2MoeSparseMoeBlock)
+    if not model_args.use_unsloth:
+        print_attn_implementation(model.config)

    try:
        model.add_model_tags(["llama-factory"])
--- a/src/llmtuner/extras/patches/init.py
+++ b/src/llmtuner/extras/patches/init.py
--- a/src/llmtuner/model/utils/attention.py
+++ b/src/llmtuner/model/utils/attention.py
@@ -0,0 +1,55 @@
+from typing import TYPE_CHECKING
+
+from ...extras.logging import get_logger
+from ...extras.packages import is_flash_attn2_available, is_sdpa_available
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig
+
+    from ...hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def configure_attn_implementation(config: "PretrainedConfig", model_args: "ModelArguments") -> None:
+    if model_args.flash_attn == "auto":
+        return
+
+    elif model_args.flash_attn == "off":
+        requested_attn_implementation = "eager"
+
+    elif model_args.flash_attn == "sdpa":
+        if not is_sdpa_available():
+            logger.warning("Torch>=2.1.1 is required for SDPA attention.")
+            return
+
+        requested_attn_implementation = "sdpa"
+    elif model_args.flash_attn == "fa2":
+        if not is_flash_attn2_available():
+            logger.warning("FlashAttention-2 is not installed.")
+            return
+
+        requested_attn_implementation = "flash_attention_2"
+    else:
+        raise NotImplementedError("Unknown attention type: {}".format(model_args.flash_attn))
+
+    if getattr(config, "model_type", None) == "internlm2":  # special case for custom models
+        setattr(config, "attn_implementation", requested_attn_implementation)
+    else:
+        setattr(config, "_attn_implementation", requested_attn_implementation)
+
+
+def print_attn_implementation(config: "PretrainedConfig") -> None:
+    if getattr(config, "model_type", None) == "internlm2":  # special case for custom models
+        attn_implementation = getattr(config, "attn_implementation", None)
+    else:
+        attn_implementation = getattr(config, "_attn_implementation", None)
+
+    if attn_implementation == "flash_attention_2":
+        logger.info("Using FlashAttention-2 for faster training and inference.")
+    elif attn_implementation == "sdpa":
+        logger.info("Using torch SDPA for faster training and inference.")
+    else:
+        logger.info("Using vanilla Attention implementation.")
--- a/src/llmtuner/model/utils/checkpointing.py
+++ b/src/llmtuner/model/utils/checkpointing.py
@@ -0,0 +1,94 @@
+import inspect
+from functools import partial
+from types import MethodType
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
+
+import torch
+
+from ...extras.constants import LAYERNORM_NAMES
+from ...extras.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+
+    from ...hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def _gradient_checkpointing_enable(
+    self: "PreTrainedModel", gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
+) -> None:
+    r"""
+    Activates gradient checkpointing for the current model.
+
+    Modification of the original method to enable gradient checkpointing for block-wise optimizer.
+    """
+    from torch.utils.checkpoint import checkpoint
+
+    if not self.supports_gradient_checkpointing:
+        raise ValueError("{} does not support gradient checkpointing.".format(self.__class__.__name__))
+
+    if gradient_checkpointing_kwargs is None:
+        gradient_checkpointing_kwargs = {"use_reentrant": True}
+
+    gradient_checkpointing_func = partial(checkpoint, **gradient_checkpointing_kwargs)
+
+    def custom_gradient_checkpointing_func(func, *args, **kwargs):
+        module: "torch.nn.Module" = func.__self__
+
+        if any(param.requires_grad for param in module.parameters()):
+            for arg in args:
+                if torch.is_tensor(arg) and torch.is_floating_point(arg):
+                    arg.requires_grad_(True)
+
+        return gradient_checkpointing_func(func, *args, **kwargs)
+
+    if "value" in inspect.signature(self._set_gradient_checkpointing).parameters:  # old GC format
+        self.apply(partial(self._set_gradient_checkpointing, value=True))
+        self.enable_input_require_grads()
+        logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
+    else:  # have already enabled input require gradients
+        self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=custom_gradient_checkpointing_func)
+
+
+def _fp32_forward_post_hook(
+    module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
+) -> "torch.Tensor":
+    return output.to(torch.float32)
+
+
+def prepare_model_for_training(
+    model: "PreTrainedModel", model_args: "ModelArguments", output_layer_name: str = "lm_head"
+) -> None:
+    r"""
+    Includes:
+        (1) cast the layernorm in fp32
+        (2) make output embedding layer require grads
+        (3) add the upcasting of the lm_head in fp32
+    Inspired by: https://github.com/huggingface/peft/blob/v0.7.1/src/peft/utils/other.py#L72
+    """
+    if model_args.upcast_layernorm:
+        logger.info("Upcasting layernorm weights in float32.")
+        for name, param in model.named_parameters():
+            if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
+                param.data = param.data.to(torch.float32)
+
+    if not model_args.disable_gradient_checkpointing:
+        if not getattr(model, "supports_gradient_checkpointing", False):
+            logger.warning("Current model does not support gradient checkpointing.")
+        else:
+            # use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
+            # According to: https://github.com/huggingface/transformers/issues/28339
+            model.gradient_checkpointing_enable = MethodType(_gradient_checkpointing_enable, model)
+            model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
+            setattr(model.config, "use_cache", False)  # turn off when gradient checkpointing is enabled
+            logger.info("Gradient checkpointing enabled.")
+
+    if hasattr(model, output_layer_name) and model_args.upcast_lmhead_output:
+        logger.info("Upcasting lm_head outputs in float32.")
+        output_layer = getattr(model, output_layer_name)
+        if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
+            output_layer.register_forward_hook(_fp32_forward_post_hook)
--- a/src/llmtuner/model/utils/embedding.py
+++ b/src/llmtuner/model/utils/embedding.py
@@ -0,0 +1,58 @@
+import math
+from contextlib import nullcontext
+from typing import TYPE_CHECKING
+
+import torch
+from transformers.integrations import is_deepspeed_zero3_enabled
+
+from ...extras.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel, PreTrainedTokenizer
+
+
+logger = get_logger(__name__)
+
+
+def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int) -> None:
+    embedding_dim = embed_weight.size(1)
+    avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
+    noise_weight = torch.empty_like(embed_weight[-num_new_tokens:])
+    noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim)))
+    embed_weight[-num_new_tokens:] = avg_weight + noise_weight
+
+
+def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
+    r"""
+    Resize token embeddings.
+    """
+    if is_deepspeed_zero3_enabled():
+        import deepspeed  # type: ignore
+
+        params = [model.get_input_embeddings().weight]
+        if model.get_output_embeddings() is not None and not model.config.tie_word_embeddings:
+            params.append(model.get_output_embeddings().weight)
+
+        context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0)
+    else:
+        context_maybe_zero3 = nullcontext()
+
+    with context_maybe_zero3:
+        current_embedding_size = model.get_input_embeddings().weight.size(0)
+
+    if len(tokenizer) > current_embedding_size:
+        if getattr(model, "quantization_method", None):
+            raise ValueError("Cannot resize embedding layers of a quantized model.")
+
+        if not isinstance(model.get_output_embeddings(), torch.nn.Linear):
+            raise ValueError("Current model does not support resizing embedding layers.")
+
+        model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)
+        with context_maybe_zero3:
+            new_embedding_size = model.get_input_embeddings().weight.size(0)
+            num_new_tokens = new_embedding_size - current_embedding_size
+            _noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
+            _noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
+
+        logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
--- a/src/llmtuner/extras/patches/llama_patch.py
+++ b/src/llmtuner/extras/patches/llama_patch.py
@@ -1,5 +1,5 @@
 import math
-from typing import Optional, Tuple
+from typing import TYPE_CHECKING, Optional, Tuple

 import torch
 import torch.nn as nn
@@ -7,19 +7,28 @@ from transformers.models.llama.modeling_llama import (
    Cache,
    LlamaAttention,
    LlamaFlashAttention2,
+    LlamaSdpaAttention,
    apply_rotary_pos_emb,
    repeat_kv,
 )
 from transformers.utils import logging
 from transformers.utils.versions import require_version

+from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig
+
+    from ...hparams import ModelArguments
+

 logger = logging.get_logger(__name__)


 # Modified from:
-# https://github.com/huggingface/transformers/blob/v4.39.1/src/transformers/models/llama/modeling_llama.py
-def llama_torch_attn_forward(
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
+def llama_attention_forward(
    self: "LlamaAttention",
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
@@ -39,10 +48,11 @@ def llama_torch_attn_forward(
    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
    value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

-    past_key_value = getattr(self, "past_key_value", past_key_value)
    cos, sin = self.rotary_emb(value_states, position_ids)
    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

+    past_key_value = getattr(self, "past_key_value", past_key_value)
+
    if past_key_value is not None:
        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
@@ -69,8 +79,9 @@ def llama_torch_attn_forward(

    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

-    if attention_mask is not None:
-        attn_weights = attn_weights + attention_mask
+    if attention_mask is not None:  # no matter the length, we just slice it
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask

    # upcast attention to fp32
    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
@@ -97,8 +108,8 @@ def llama_torch_attn_forward(


 # Modified from:
-# https://github.com/huggingface/transformers/blob/v4.39.1/src/transformers/models/llama/modeling_llama.py
-def llama_flash_attn_forward(
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
+def llama_flash_attention_2_forward(
    self: "LlamaFlashAttention2",
    hidden_states: torch.Tensor,
    attention_mask: Optional[torch.Tensor] = None,
@@ -117,7 +128,6 @@ def llama_flash_attn_forward(
    key_states = self.k_proj(hidden_states)
    value_states = self.v_proj(hidden_states)

-    # FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim)
    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
    value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
@@ -134,9 +144,10 @@ def llama_flash_attn_forward(
    key_states = repeat_kv(key_states, self.num_key_value_groups)
    value_states = repeat_kv(value_states, self.num_key_value_groups)

-    query_states = query_states.transpose(1, 2)  # (bsz, seq_len, n_heads, head_dim)
-    key_states = key_states.transpose(1, 2)  # (bsz, seq_len, n_heads, head_dim)
-    value_states = value_states.transpose(1, 2)  # (bsz, seq_len, n_heads, head_dim)
+    # FlashAttention requires the input to have the shape (bsz, seq_len, n_heads, head_dim)
+    query_states = query_states.transpose(1, 2)
+    key_states = key_states.transpose(1, 2)
+    value_states = value_states.transpose(1, 2)

    dropout_rate = self.attention_dropout if self.training else 0.0

@@ -192,7 +203,115 @@ def llama_flash_attn_forward(
    return attn_output, attn_weights, past_key_value


-def apply_llama_patch() -> None:
-    require_version("transformers==4.39.3", "To fix: pip install transformers==4.39.3")
-    LlamaAttention.forward = llama_torch_attn_forward
-    LlamaFlashAttention2.forward = llama_flash_attn_forward
+# Modified from:
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
+def llama_sdpa_attention_forward(
+    self: "LlamaSdpaAttention",
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional["Cache"] = None,
+    output_attentions: bool = False,
+    cache_position: Optional[torch.LongTensor] = None,
+    **kwargs,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    if output_attentions:
+        logger.warning_once("SDPA does not support `output_attentions=True`. Falling back to the vanilla attention")
+        return llama_attention_forward(
+            self,
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+    bsz, q_len, _ = hidden_states.size()
+
+    query_states = self.q_proj(hidden_states)
+    key_states = self.k_proj(hidden_states)
+    value_states = self.v_proj(hidden_states)
+
+    query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+    value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+    cos, sin = self.rotary_emb(value_states, position_ids)
+    query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+    if past_key_value is not None:
+        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+        key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    if getattr(self.config, "group_size_ratio", None) and self.training:  # shift
+        groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
+        assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
+        num_groups = q_len // groupsz
+
+        def shift(state: torch.Tensor) -> torch.Tensor:
+            state = state.transpose(1, 2)  # output: (bsz, seq_len, n_heads, head_dim)
+            state = torch.cat(
+                (state[:, :, : self.num_heads // 2], state[:, :, self.num_heads // 2 :].roll(-groupsz // 2, dims=1)),
+                dim=2,
+            )
+            return state.reshape(bsz * num_groups, groupsz, self.num_heads, self.head_dim).transpose(1, 2)
+
+        query_states, key_states, value_states = shift(query_states), shift(key_states), shift(value_states)
+        if attention_mask is not None:
+            attention_mask = attention_mask[:, :, :groupsz, :groupsz].repeat(num_groups, 1, 1, 1)
+
+    causal_mask = attention_mask
+    if attention_mask is not None:
+        causal_mask = causal_mask[:, :, :, :groupsz]
+
+    query_states = query_states.contiguous()
+    key_states = key_states.contiguous()
+    value_states = value_states.contiguous()
+
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+        query_states,
+        key_states,
+        value_states,
+        attn_mask=causal_mask,
+        dropout_p=self.attention_dropout if self.training else 0.0,
+        is_causal=causal_mask is None and q_len > 1,
+    )
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    if getattr(self.config, "group_size_ratio", None) and self.training:  # shift back
+        attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim)
+        attn_output = torch.cat(
+            (
+                attn_output[:, :, : self.num_heads // 2],
+                attn_output[:, :, self.num_heads // 2 :].roll(groupsz // 2, dims=1),
+            )
+        )
+
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    attn_output = self.o_proj(attn_output)
+
+    return attn_output, None, past_key_value
+
+
+def _apply_llama_patch() -> None:
+    require_version("transformers==4.40.0", "To fix: pip install transformers==4.40.0")
+    LlamaAttention.forward = llama_attention_forward
+    LlamaFlashAttention2.forward = llama_flash_attention_2_forward
+    LlamaSdpaAttention.forward = llama_sdpa_attention_forward
+
+
+def configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
+    if not is_trainable or not model_args.shift_attn:
+        return
+
+    if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
+        setattr(config, "group_size_ratio", 0.25)
+        _apply_llama_patch()
+        logger.info("Using shift short attention with group_size_ratio=1/4.")
+    else:
+        logger.warning("Current model does not support shift short attention.")
--- a/src/llmtuner/model/utils/misc.py
+++ b/src/llmtuner/model/utils/misc.py
@@ -1,49 +1,18 @@
-from enum import Enum, unique
-from typing import TYPE_CHECKING, Dict, List
+from typing import TYPE_CHECKING, List

 import torch
-from transformers import PreTrainedModel
-from transformers.integrations import is_deepspeed_zero3_enabled
-from transformers.utils import cached_file
-from transformers.utils.versions import require_version

-from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
-from ..extras.logging import get_logger
+from ...extras.logging import get_logger
+from .quantization import QuantizationMethod


 if TYPE_CHECKING:
-    from transformers import PretrainedConfig, PreTrainedTokenizer
-
-    from ..hparams import ModelArguments
+    from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer


 logger = get_logger(__name__)


-@unique
-class QuantizationMethod(str, Enum):
-    r"""
-    Borrowed from `transformers.utils.quantization_config.QuantizationMethod`.
-    """
-
-    BITS_AND_BYTES = "bitsandbytes"
-    GPTQ = "gptq"
-    AWQ = "awq"
-    AQLM = "aqlm"
-    QUANTO = "quanto"
-
-
-def add_z3_leaf_module(model: "PreTrainedModel", module: "torch.nn.Module") -> None:
-    r"""
-    Sets module as a leaf module to skip partitioning in deepspeed zero3.
-    """
-    if is_deepspeed_zero3_enabled():
-        require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
-        from deepspeed.utils import set_z3_leaf_modules  # type: ignore
-
-        set_z3_leaf_modules(model, [module])
-
-
 def find_all_linear_modules(model: "PreTrainedModel") -> List[str]:
    r"""
    Finds all available modules to apply lora or galore.
@@ -100,34 +69,6 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
    return module_names


-def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
-    r"""
-    Loads value head parameters from Hugging Face Hub or local disk.
-
-    Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
-    """
-    kwargs = {"path_or_repo_id": path_or_repo_id, "cache_dir": model_args.cache_dir, "token": model_args.hf_hub_token}
-
-    try:
-        from safetensors import safe_open
-
-        vhead_file = cached_file(filename=V_HEAD_SAFE_WEIGHTS_NAME, **kwargs)
-        with safe_open(vhead_file, framework="pt", device="cpu") as f:
-            return {key: f.get_tensor(key) for key in f.keys()}
-    except Exception as err:
-        logger.info("Failed to load {}: {}".format(V_HEAD_SAFE_WEIGHTS_NAME, str(err)))
-
-    try:
-        vhead_file = cached_file(filename=V_HEAD_WEIGHTS_NAME, **kwargs)
-        return torch.load(vhead_file, map_location="cpu")
-    except Exception as err:
-        logger.info("Failed to load {}: {}".format(V_HEAD_WEIGHTS_NAME, str(err)))
-
-    logger.info("Provided path ({}) does not contain value head weights.".format(path_or_repo_id))
-    logger.info("Ignore these messages if you are not resuming the training of a value head model.")
-    return None
-
-
 def register_autoclass(config: "PretrainedConfig", model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer"):
    if "AutoConfig" in getattr(config, "auto_map", {}):
        config.__class__.register_for_auto_class()
--- a/src/llmtuner/model/utils/mod.py
+++ b/src/llmtuner/model/utils/mod.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING
+
+from ...extras.constants import MOD_SUPPORTED_MODELS
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig, PreTrainedModel
+
+    from ...hparams import ModelArguments
+
+
+def load_mod_pretrained_model(**init_kwargs) -> "PreTrainedModel":
+    from MoD import AutoMoDModelForCausalLM
+
+    return AutoMoDModelForCausalLM.from_pretrained(**init_kwargs)
+
+
+def convert_pretrained_model_to_mod(
+    model: "PreTrainedModel", config: "PretrainedConfig", model_args: "ModelArguments"
+) -> "PreTrainedModel":
+    from MoD import apply_mod_to_hf
+
+    if getattr(config, "model_type", None) not in MOD_SUPPORTED_MODELS:
+        raise ValueError("Current model is not supported by mixture-of-depth.")
+
+    model = apply_mod_to_hf(model)
+    model = model.to(model_args.compute_dtype)
+    return model
--- a/src/llmtuner/model/utils/moe.py
+++ b/src/llmtuner/model/utils/moe.py
@@ -0,0 +1,53 @@
+from typing import TYPE_CHECKING
+
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.utils.versions import require_version
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig, PreTrainedModel
+
+    from ...hparams import ModelArguments
+
+
+def add_z3_leaf_module(model: "PreTrainedModel") -> None:
+    r"""
+    Sets module as a leaf module to skip partitioning in deepspeed zero3.
+    """
+    if not is_deepspeed_zero3_enabled():
+        return
+
+    require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
+    from deepspeed.utils import set_z3_leaf_modules  # type: ignore
+
+    if getattr(model.config, "model_type", None) == "mixtral":
+        from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
+
+        set_z3_leaf_modules(model, [MixtralSparseMoeBlock])
+
+    if getattr(model.config, "model_type", None) == "qwen2moe":
+        from transformers.models.qwen2_moe.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
+
+        set_z3_leaf_modules(model, [Qwen2MoeSparseMoeBlock])
+
+    if getattr(model.config, "model_type", None) == "jamba":
+        from transformers.models.jamba.modeling_jamba import JambaSparseMoeBlock
+
+        set_z3_leaf_modules(model, [JambaSparseMoeBlock])
+
+    if getattr(model.config, "model_type", None) == "dbrx":
+        from transformers.models.dbrx.modeling_dbrx import DbrxFFN
+
+        set_z3_leaf_modules(model, [DbrxFFN])
+
+
+def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
+    if model_args.moe_aux_loss_coef is not None:
+        if getattr(config, "model_type", None) in ["jamba", "mixtral", "qwen2_moe"]:
+            setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
+
+        elif getattr(config, "model_type", None) == "deepseek":
+            setattr(config, "aux_loss_alpha", model_args.moe_aux_loss_coef)
+
+    if getattr(config, "model_type", None) in ["dbrx", "jamba", "mixtral", "qwen2_moe"]:
+        setattr(config, "output_router_logits", is_trainable)
--- a/src/llmtuner/model/utils/quantization.py
+++ b/src/llmtuner/model/utils/quantization.py
@@ -0,0 +1,146 @@
+import os
+import random
+from enum import Enum, unique
+from typing import TYPE_CHECKING, Any, Dict, List
+
+import torch
+from datasets import load_dataset
+from transformers import BitsAndBytesConfig, GPTQConfig
+from transformers.integrations import is_deepspeed_zero3_enabled
+from transformers.utils.versions import require_version
+
+from ...extras.constants import FILEEXT2TYPE
+from ...extras.logging import get_logger
+from ...extras.misc import get_current_device
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig, PreTrainedTokenizer
+
+    from ...hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+@unique
+class QuantizationMethod(str, Enum):
+    r"""
+    Borrowed from `transformers.utils.quantization_config.QuantizationMethod`.
+    """
+
+    BITS_AND_BYTES = "bitsandbytes"
+    GPTQ = "gptq"
+    AWQ = "awq"
+    AQLM = "aqlm"
+    QUANTO = "quanto"
+
+
+def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
+    r"""
+    Inspired by: https://github.com/huggingface/optimum/blob/v1.16.0/optimum/gptq/data.py#L133
+    TODO: remove tokenizer.decode() https://github.com/huggingface/optimum/pull/1600
+    """
+    if os.path.isfile(model_args.export_quantization_dataset):
+        data_path = FILEEXT2TYPE.get(model_args.export_quantization_dataset.split(".")[-1], None)
+        data_files = model_args.export_quantization_dataset
+    else:
+        data_path = model_args.export_quantization_dataset
+        data_files = None
+
+    dataset = load_dataset(path=data_path, data_files=data_files, split="train", cache_dir=model_args.cache_dir)
+    maxlen = model_args.export_quantization_maxlen
+
+    samples = []
+    for _ in range(model_args.export_quantization_nsamples):
+        while True:
+            sample_idx = random.randint(0, len(dataset) - 1)
+            sample: Dict[str, torch.Tensor] = tokenizer(dataset[sample_idx]["text"], return_tensors="pt")
+            if sample["input_ids"].size(1) >= maxlen:
+                break  # TODO: fix large maxlen
+
+        word_idx = random.randint(0, sample["input_ids"].size(1) - maxlen - 1)
+        input_ids = sample["input_ids"][:, word_idx : word_idx + maxlen]
+        samples.append(tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True))
+
+    return samples
+
+
+def configure_quantization(
+    config: "PretrainedConfig",
+    tokenizer: "PreTrainedTokenizer",
+    model_args: "ModelArguments",
+    init_kwargs: Dict[str, Any],
+) -> None:
+    r"""
+    Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
+    """
+    if getattr(config, "quantization_config", None):  # ptq
+        if is_deepspeed_zero3_enabled():
+            raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantized models.")
+
+        if model_args.quantization_device_map != "auto":
+            init_kwargs["device_map"] = {"": get_current_device()}
+
+        quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
+        quant_method = quantization_config.get("quant_method", "")
+
+        if quant_method == QuantizationMethod.GPTQ:
+            require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
+            quantization_config.pop("disable_exllama", None)  # remove deprecated args
+            quantization_config["use_exllama"] = False  # disable exllama
+
+        if quant_method == QuantizationMethod.AWQ:
+            require_version("autoawq", "To fix: pip install autoawq")
+
+        if quant_method == QuantizationMethod.AQLM:
+            require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
+            require_version("aqlm>=1.1.0", "To fix: pip install aqlm[gpu]>=1.1.0")
+            quantization_config["bits"] = 2
+
+        quant_bits = quantization_config.get("bits", "?")
+        logger.info("Loading {}-bit {}-quantized model.".format(quant_bits, quant_method.upper()))
+
+    elif model_args.export_quantization_bit is not None:  # auto-gptq
+        require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
+        require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
+        from accelerate.utils import get_max_memory
+
+        if getattr(config, "model_type", None) == "chatglm":
+            raise ValueError("ChatGLM model is not supported.")
+
+        init_kwargs["quantization_config"] = GPTQConfig(
+            bits=model_args.export_quantization_bit,
+            tokenizer=tokenizer,
+            dataset=_get_quantization_dataset(tokenizer, model_args),
+        )
+        init_kwargs["device_map"] = "auto"
+        init_kwargs["max_memory"] = get_max_memory()
+        logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
+
+    elif model_args.quantization_bit is not None:  # bnb
+        if model_args.quantization_bit == 8:
+            require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
+            init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
+
+        elif model_args.quantization_bit == 4:
+            require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
+            init_kwargs["quantization_config"] = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_compute_dtype=model_args.compute_dtype,
+                bnb_4bit_use_double_quant=model_args.double_quantization,
+                bnb_4bit_quant_type=model_args.quantization_type,
+                bnb_4bit_quant_storage=model_args.compute_dtype,  # crucial for fsdp qlora
+            )
+
+        if is_deepspeed_zero3_enabled() or model_args.quantization_device_map == "auto":
+            if model_args.quantization_bit != 4:
+                raise ValueError("Only 4-bit quantized model can use auto device map.")
+
+            require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
+            require_version("accelerate>=0.28.0", "To fix: pip install accelerate>=0.28.0")
+            require_version("bitsandbytes>=0.43.0", "To fix: pip install bitsandbytes>=0.43.0")
+        else:
+            init_kwargs["device_map"] = {"": get_current_device()}
+
+        logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
--- a/src/llmtuner/model/utils/rope.py
+++ b/src/llmtuner/model/utils/rope.py
@@ -0,0 +1,47 @@
+import math
+from typing import TYPE_CHECKING
+
+from ...extras.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig
+
+    from ...hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
+    if model_args.rope_scaling is None:
+        return
+
+    if not hasattr(config, "rope_scaling"):
+        logger.warning("Current model does not support RoPE scaling.")
+        return
+
+    if is_trainable:
+        if model_args.rope_scaling == "dynamic":
+            logger.warning(
+                "Dynamic NTK scaling may not work well with fine-tuning. "
+                "See: https://github.com/huggingface/transformers/pull/24653"
+            )
+
+        current_max_length = getattr(config, "max_position_embeddings", None)
+        if current_max_length and model_args.model_max_length > current_max_length:
+            logger.info(
+                "Enlarge max model length from {} to {}.".format(current_max_length, model_args.model_max_length)
+            )
+            setattr(config, "max_position_embeddings", model_args.model_max_length)
+            scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
+        else:
+            logger.warning("Input length is smaller than max length. Consider increase input length.")
+            scaling_factor = 1.0
+    else:
+        scaling_factor = 2.0
+
+    setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
+    logger.info(
+        "Using {} scaling strategy and setting scaling factor to {}".format(model_args.rope_scaling, scaling_factor)
+    )
--- a/src/llmtuner/model/utils/unsloth.py
+++ b/src/llmtuner/model/utils/unsloth.py
@@ -0,0 +1,88 @@
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+from ...extras.logging import get_logger
+from ...extras.misc import get_current_device
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig, PreTrainedModel
+
+    from ...hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def _get_unsloth_kwargs(
+    config: "PretrainedConfig", model_name_or_path: str, model_args: "ModelArguments"
+) -> Dict[str, Any]:
+    return {
+        "model_name": model_name_or_path,
+        "max_seq_length": model_args.model_max_length or 4096,
+        "dtype": model_args.compute_dtype,
+        "load_in_4bit": model_args.quantization_bit == 4,
+        "token": model_args.hf_hub_token,
+        "device_map": {"": get_current_device()},
+        "rope_scaling": getattr(config, "rope_scaling", None),
+        "fix_tokenizer": False,
+        "trust_remote_code": True,
+        "use_gradient_checkpointing": "unsloth",
+    }
+
+
+def load_unsloth_pretrained_model(
+    config: "PretrainedConfig", model_args: "ModelArguments"
+) -> Optional["PreTrainedModel"]:
+    r"""
+    Optionally loads pretrained model with unsloth. Used in training.
+    """
+    from unsloth import FastLanguageModel
+
+    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.model_name_or_path, model_args)
+    try:
+        model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
+    except NotImplementedError:
+        logger.warning("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
+        model = None
+        model_args.use_unsloth = False
+
+    return model
+
+
+def get_unsloth_peft_model(
+    model: "PreTrainedModel", model_args: "ModelArguments", peft_kwargs: Dict[str, Any]
+) -> "PreTrainedModel":
+    r"""
+    Gets the peft model for the pretrained model with unsloth. Used in training.
+    """
+    from unsloth import FastLanguageModel
+
+    unsloth_peft_kwargs = {
+        "model": model,
+        "max_seq_length": model_args.model_max_length,
+        "use_gradient_checkpointing": "unsloth",
+    }
+    return FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)
+
+
+def load_unsloth_peft_model(
+    config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
+) -> "PreTrainedModel":
+    r"""
+    Loads peft model with unsloth. Used in both training and inference.
+    """
+    from unsloth import FastLanguageModel
+
+    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args)
+    try:
+        if not is_trainable:
+            unsloth_kwargs["use_gradient_checkpointing"] = False
+
+        model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
+    except NotImplementedError:
+        raise ValueError("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
+
+    if not is_trainable:
+        FastLanguageModel.for_inference(model)
+
+    return model
--- a/src/llmtuner/model/utils/valuehead.py
+++ b/src/llmtuner/model/utils/valuehead.py
@@ -0,0 +1,59 @@
+from typing import TYPE_CHECKING, Dict
+
+import torch
+from transformers.utils import cached_file
+
+from ...extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
+from ...extras.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig, PreTrainedModel
+
+    from ...hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def configure_valuehead(config: "PretrainedConfig") -> None:
+    if getattr(config, "model_type", None) == "llava":
+        setattr(config, "hidden_size", getattr(config.vision_config, "intermediate_size", None))
+
+
+def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
+    r"""
+    Loads value head parameters from Hugging Face Hub or local disk.
+
+    Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
+    """
+    kwargs = {"path_or_repo_id": path_or_repo_id, "cache_dir": model_args.cache_dir, "token": model_args.hf_hub_token}
+
+    try:
+        from safetensors import safe_open
+
+        vhead_file = cached_file(filename=V_HEAD_SAFE_WEIGHTS_NAME, **kwargs)
+        with safe_open(vhead_file, framework="pt", device="cpu") as f:
+            return {key: f.get_tensor(key) for key in f.keys()}
+    except Exception as err:
+        logger.info("Failed to load {}: {}".format(V_HEAD_SAFE_WEIGHTS_NAME, str(err)))
+
+    try:
+        vhead_file = cached_file(filename=V_HEAD_WEIGHTS_NAME, **kwargs)
+        return torch.load(vhead_file, map_location="cpu")
+    except Exception as err:
+        logger.info("Failed to load {}: {}".format(V_HEAD_WEIGHTS_NAME, str(err)))
+
+    logger.info("Provided path ({}) does not contain value head weights.".format(path_or_repo_id))
+    logger.info("Ignore these messages if you are not resuming the training of a value head model.")
+    return None
+
+
+def prepare_valuehead_model(model: "PreTrainedModel") -> None:
+    if getattr(model.config, "model_type", None) == "llava":
+        setattr(model, "lm_head", model.language_model.get_output_embeddings())
+        setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
+
+    if getattr(model.config, "model_type", None) == "chatglm":
+        setattr(model, "lm_head", model.transformer.output_layer)
+        setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
--- a/src/llmtuner/model/utils/visual.py
+++ b/src/llmtuner/model/utils/visual.py
@@ -0,0 +1,28 @@
+from typing import TYPE_CHECKING, Tuple
+
+import torch
+
+from ...extras.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+
+    from ...hparams import ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+def autocast_projector_dtype(
+    model: "PreTrainedModel", model_args: "ModelArguments", mm_projector_name: str = "multi_modal_projector"
+) -> None:
+    def _mm_projector_forward_post_hook(
+        module: "torch.nn.Module", args: Tuple["torch.Tensor"], output: "torch.Tensor"
+    ) -> "torch.Tensor":
+        return output.to(model_args.compute_dtype)
+
+    if hasattr(model, mm_projector_name):
+        logger.info("Casting multimodal projector outputs in {}.".format(model_args.compute_dtype))
+        mm_projector: "torch.nn.Module" = getattr(model, mm_projector_name)
+        mm_projector.register_forward_hook(_mm_projector_forward_post_hook)
--- a/src/llmtuner/train/dpo/trainer.py
+++ b/src/llmtuner/train/dpo/trainer.py
@@ -1,5 +1,6 @@
 from collections import defaultdict
 from contextlib import nullcontext
+from types import MethodType
 from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union

 import torch
@@ -63,6 +64,11 @@ class CustomDPOTrainer(DPOTrainer):
            else:
                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

+        if finetuning_args.use_badam:
+            from badam import clip_grad_norm_for_sparse_tensor
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)
+
    def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
--- a/src/llmtuner/train/dpo/workflow.py
+++ b/src/llmtuner/train/dpo/workflow.py
@@ -24,8 +24,9 @@ def run_dpo(
    finetuning_args: "FinetuningArguments",
    callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)

    data_collator = PairwiseDataCollatorWithPadding(
--- a/src/llmtuner/train/orpo/trainer.py
+++ b/src/llmtuner/train/orpo/trainer.py
@@ -1,4 +1,5 @@
 from collections import defaultdict
+from types import MethodType
 from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union

 import torch
@@ -44,6 +45,10 @@ class CustomORPOTrainer(DPOTrainer):
        self._stored_metrics = defaultdict(lambda: defaultdict(list))

        Trainer.__init__(self, model=model, **kwargs)
+        if finetuning_args.use_badam:
+            from badam import clip_grad_norm_for_sparse_tensor
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)

    def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
--- a/src/llmtuner/train/orpo/workflow.py
+++ b/src/llmtuner/train/orpo/workflow.py
@@ -24,8 +24,9 @@ def run_orpo(
    finetuning_args: "FinetuningArguments",
    callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)

    data_collator = PairwiseDataCollatorWithPadding(
--- a/src/llmtuner/train/ppo/trainer.py
+++ b/src/llmtuner/train/ppo/trainer.py
@@ -1,6 +1,7 @@
 import math
 import os
 import sys
+from types import MethodType
 from typing import TYPE_CHECKING, Dict, List, Optional, Tuple

 import torch
@@ -124,6 +125,11 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
            else:
                self.reward_model = self.accelerator.prepare_model(self.reward_model, evaluation_mode=True)

+        if finetuning_args.use_badam:
+            from badam import clip_grad_norm_for_sparse_tensor
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)
+
    def ppo_train(self, resume_from_checkpoint: Optional[str] = None) -> None:
        r"""
        Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer.
--- a/src/llmtuner/train/ppo/workflow.py
+++ b/src/llmtuner/train/ppo/workflow.py
@@ -27,8 +27,9 @@ def run_ppo(
    generating_args: "GeneratingArguments",
    callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="ppo")
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="ppo", **tokenizer_module)
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)

    tokenizer.padding_side = "left"  # use left-padding in generation while using right-padding in training
--- a/src/llmtuner/train/pt/trainer.py
+++ b/src/llmtuner/train/pt/trainer.py
@@ -1,3 +1,4 @@
+from types import MethodType
 from typing import TYPE_CHECKING, Optional

 from transformers import Trainer
@@ -23,6 +24,10 @@ class CustomTrainer(Trainer):
    def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
        super().__init__(**kwargs)
        self.finetuning_args = finetuning_args
+        if finetuning_args.use_badam:
+            from badam import clip_grad_norm_for_sparse_tensor
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)

    def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
--- a/src/llmtuner/train/pt/workflow.py
+++ b/src/llmtuner/train/pt/workflow.py
@@ -25,8 +25,9 @@ def run_pt(
    finetuning_args: "FinetuningArguments",
    callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="pt")
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="pt", **tokenizer_module)
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

--- a/src/llmtuner/train/rm/trainer.py
+++ b/src/llmtuner/train/rm/trainer.py
@@ -1,5 +1,6 @@
 import json
 import os
+from types import MethodType
 from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

 import torch
@@ -28,6 +29,10 @@ class PairwiseTrainer(Trainer):
        super().__init__(**kwargs)
        self.finetuning_args = finetuning_args
        self.can_return_loss = True  # override property to return eval_loss
+        if finetuning_args.use_badam:
+            from badam import clip_grad_norm_for_sparse_tensor
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)

    def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
--- a/src/llmtuner/train/rm/workflow.py
+++ b/src/llmtuner/train/rm/workflow.py
@@ -25,8 +25,9 @@ def run_rm(
    finetuning_args: "FinetuningArguments",
    callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="rm")
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="rm", **tokenizer_module)
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True)
    data_collator = PairwiseDataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)

--- a/src/llmtuner/train/sft/metric.py
+++ b/src/llmtuner/train/sft/metric.py
@@ -2,7 +2,6 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union

 import numpy as np
-from transformers.utils.versions import require_version

 from ...extras.constants import IGNORE_INDEX
 from ...extras.packages import is_jieba_available, is_nltk_available, is_rouge_available
@@ -33,10 +32,6 @@ class ComputeMetrics:
        r"""
        Uses the model predictions to compute metrics.
        """
-        require_version("jieba", "To fix: pip install jieba")
-        require_version("nltk", "To fix: pip install nltk")
-        require_version("rouge_chinese", "To fix: pip install rouge-chinese")
-
        preds, labels = eval_preds
        score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}

--- a/src/llmtuner/train/sft/trainer.py
+++ b/src/llmtuner/train/sft/trainer.py
@@ -1,5 +1,6 @@
 import json
 import os
+from types import MethodType
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

 import numpy as np
@@ -28,6 +29,10 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
    def __init__(self, finetuning_args: "FinetuningArguments", **kwargs) -> None:
        super().__init__(**kwargs)
        self.finetuning_args = finetuning_args
+        if finetuning_args.use_badam:
+            from badam import clip_grad_norm_for_sparse_tensor
+
+            self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator)

    def create_optimizer(self) -> "torch.optim.Optimizer":
        if self.optimizer is None:
--- a/src/llmtuner/train/sft/workflow.py
+++ b/src/llmtuner/train/sft/workflow.py
@@ -28,8 +28,9 @@ def run_sft(
    generating_args: "GeneratingArguments",
    callbacks: Optional[List["TrainerCallback"]] = None,
 ):
-    tokenizer = load_tokenizer(model_args)
-    dataset = get_dataset(tokenizer, model_args, data_args, training_args, stage="sft")
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    dataset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)

    if training_args.predict_with_generate:
@@ -47,6 +48,7 @@ def run_sft(
    # Override the decoding parameters of Seq2SeqTrainer
    training_args.generation_max_length = training_args.generation_max_length or data_args.cutoff_len
    training_args.generation_num_beams = data_args.eval_num_beams or training_args.generation_num_beams
+    training_args.remove_unused_columns = False if model_args.visual_inputs else training_args.remove_unused_columns

    # Initialize our Trainer
    trainer = CustomSeq2SeqTrainer(
--- a/src/llmtuner/train/tuner.py
+++ b/src/llmtuner/train/tuner.py
@@ -52,7 +52,7 @@ def export_model(args: Optional[Dict[str, Any]] = None):
    if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
        raise ValueError("Please merge adapters before quantizing the model.")

-    tokenizer = load_tokenizer(model_args)
+    tokenizer = load_tokenizer(model_args)["tokenizer"]
    get_template_and_fix_tokenizer(tokenizer, data_args.template)
    model = load_model(tokenizer, model_args, finetuning_args)  # must after fixing tokenizer to resize vocab

@@ -65,8 +65,7 @@ def export_model(args: Optional[Dict[str, Any]] = None):
    if getattr(model, "quantization_method", None) is None:  # cannot convert dtype of a quantized model
        output_dtype = getattr(model.config, "torch_dtype", torch.float16)
        setattr(model.config, "torch_dtype", output_dtype)
-        for param in model.parameters():
-            param.data = param.data.to(output_dtype)
+        model = model.to(output_dtype)

    model.save_pretrained(
        save_directory=model_args.export_dir,
--- a/src/llmtuner/train/utils.py
+++ b/src/llmtuner/train/utils.py
@@ -5,7 +5,6 @@ from transformers import Trainer
 from transformers.optimization import get_scheduler
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.trainer_pt_utils import get_parameter_names
-from transformers.utils.versions import require_version

 from ..extras.logging import get_logger
 from ..extras.packages import is_galore_available
@@ -57,9 +56,14 @@ def create_modelcard_and_push(
    kwargs = {
        "tasks": "text-generation",
        "finetuned_from": model_args.model_name_or_path,
-        "dataset": [dataset.strip() for dataset in data_args.dataset.split(",")],
        "tags": ["llama-factory", finetuning_args.finetuning_type],
    }
+    if data_args.dataset is not None:
+        kwargs["dataset"] = [dataset.strip() for dataset in data_args.dataset.split(",")]
+
+    if model_args.use_unsloth:
+        kwargs["tags"] = kwargs["tags"] + ["unsloth"]
+
    if not training_args.do_train:
        pass
    elif training_args.push_to_hub:
@@ -87,7 +91,7 @@ def create_ref_model(
        )
        ref_model_args = ModelArguments(**ref_model_args_dict)
        ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
-        tokenizer = load_tokenizer(ref_model_args)
+        tokenizer = load_tokenizer(ref_model_args)["tokenizer"]
        ref_model = load_model(
            tokenizer, ref_model_args, ref_finetuning_args, is_trainable=False, add_valuehead=add_valuehead
        )
@@ -96,7 +100,7 @@ def create_ref_model(
        if finetuning_args.finetuning_type == "lora":
            ref_model = None
        else:
-            tokenizer = load_tokenizer(model_args)
+            tokenizer = load_tokenizer(model_args)["tokenizer"]
            ref_model = load_model(
                tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=add_valuehead
            )
@@ -143,7 +147,7 @@ def create_reward_model(
        )
        reward_model_args = ModelArguments(**reward_model_args_dict)
        reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
-        tokenizer = load_tokenizer(reward_model_args)
+        tokenizer = load_tokenizer(reward_model_args)["tokenizer"]
        reward_model = load_model(
            tokenizer, reward_model_args, reward_finetuning_args, is_trainable=False, add_valuehead=True
        )
@@ -166,8 +170,6 @@ def _create_galore_optimizer(
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
 ) -> "torch.optim.Optimizer":
-    require_version("galore_torch", "To fix: pip install galore_torch")
-
    if len(finetuning_args.galore_target) == 1 and finetuning_args.galore_target[0] == "all":
        galore_targets = find_all_linear_modules(model)
    else:
@@ -217,7 +219,7 @@ def _create_galore_optimizer(

        optimizer_dict: Dict["torch.Tensor", "torch.optim.Optimizer"] = {}
        for param in nodecay_params:
-            param_groups = [dict(params=[param])]
+            param_groups = [dict(params=[param], weight_decay=0.0)]
            optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
        for param in decay_params:
            param_groups = [dict(params=[param], weight_decay=training_args.weight_decay)]
@@ -237,7 +239,7 @@ def _create_galore_optimizer(
        optimizer = DummyOptimizer(lr=training_args.learning_rate, optimizer_dict=optimizer_dict)
    else:
        param_groups = [
-            dict(params=nodecay_params),
+            dict(params=nodecay_params, weight_decay=0.0),
            dict(params=decay_params, weight_decay=training_args.weight_decay),
            dict(params=galore_params, weight_decay=training_args.weight_decay, **galore_kwargs),
        ]
@@ -252,11 +254,9 @@ def _create_loraplus_optimizer(
    training_args: "Seq2SeqTrainingArguments",
    finetuning_args: "FinetuningArguments",
 ) -> "torch.optim.Optimizer":
-    if finetuning_args.finetuning_type != "lora":
-        raise ValueError("You should use LoRA tuning to activate LoRA+.")
-
+    default_lr = training_args.learning_rate
    loraplus_lr = training_args.learning_rate * finetuning_args.loraplus_lr_ratio
-    decay_args = {"weight_decay": training_args.weight_decay}
+    embedding_lr = finetuning_args.loraplus_lr_embedding

    decay_param_names = _get_decay_parameter_names(model)
    param_dict: Dict[str, List["torch.nn.Parameter"]] = {
@@ -279,16 +279,76 @@ def _create_loraplus_optimizer(

    optim_class, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
    param_groups = [
-        dict(params=param_dict["lora_a"], **decay_args),
-        dict(params=param_dict["lora_b"], lr=loraplus_lr, **decay_args),
-        dict(params=param_dict["lora_b_nodecay"], lr=loraplus_lr),
-        dict(params=param_dict["embedding"], lr=finetuning_args.loraplus_lr_embedding, **decay_args),
+        dict(params=param_dict["lora_a"], lr=default_lr, weight_decay=training_args.weight_decay),
+        dict(params=param_dict["lora_b"], lr=loraplus_lr, weight_decay=training_args.weight_decay),
+        dict(params=param_dict["lora_b_nodecay"], lr=loraplus_lr, weight_decay=0.0),
+        dict(params=param_dict["embedding"], lr=embedding_lr, weight_decay=training_args.weight_decay),
    ]
    optimizer = optim_class(param_groups, **optim_kwargs)
    logger.info("Using LoRA+ optimizer with loraplus lr ratio {:.2f}.".format(finetuning_args.loraplus_lr_ratio))
    return optimizer


+def _create_badam_optimizer(
+    model: "PreTrainedModel",
+    training_args: "Seq2SeqTrainingArguments",
+    finetuning_args: "FinetuningArguments",
+) -> "torch.optim.Optimizer":
+    decay_params, nodecay_params = [], []
+    decay_param_names = _get_decay_parameter_names(model)
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            if name in decay_param_names:
+                decay_params.append(param)
+            else:
+                nodecay_params.append(param)
+
+    optim_class, optim_kwargs = Trainer.get_optimizer_cls_and_kwargs(training_args)
+    param_groups = [
+        dict(params=nodecay_params, weight_decay=0.0),
+        dict(params=decay_params, weight_decay=training_args.weight_decay),
+    ]
+
+    if finetuning_args.badam_mode == "layer":
+        from badam import BlockOptimizer
+
+        base_optimizer = optim_class(param_groups, **optim_kwargs)
+        optimizer = BlockOptimizer(
+            base_optimizer=base_optimizer,
+            named_parameters_list=list(model.named_parameters()),
+            block_prefix_list=None,
+            switch_block_every=finetuning_args.badam_switch_block_every,
+            start_block=finetuning_args.badam_start_block,
+            switch_mode=finetuning_args.badam_switch_mode,
+            verbose=finetuning_args.badam_verbose,
+        )
+        logger.info(
+            f"Using BAdam optimizer with layer-wise update, switch mode is {finetuning_args.badam_switch_mode}, "
+            f"switch block every {finetuning_args.badam_switch_block_every} steps, "
+            f"default start block is {finetuning_args.badam_start_block}"
+        )
+
+    elif finetuning_args.badam_mode == "ratio":
+        from badam import BlockOptimizerRatio
+
+        assert finetuning_args.badam_update_ratio > 1e-6
+        optimizer = BlockOptimizerRatio(
+            param_groups=param_groups,
+            named_parameters_list=list(model.named_parameters()),
+            update_ratio=finetuning_args.badam_update_ratio,
+            mask_mode=finetuning_args.badam_mask_mode,
+            verbose=finetuning_args.badam_verbose,
+            include_embedding=False,
+            **optim_kwargs,
+        )
+        logger.info(
+            f"Using BAdam optimizer with ratio-wise update, update ratio is {finetuning_args.badam_update_ratio}, "
+            f"mask mode is {finetuning_args.badam_mask_mode}"
+        )
+
+    return optimizer
+
+
 def create_custom_optimzer(
    model: "PreTrainedModel",
    training_args: "Seq2SeqTrainingArguments",
@@ -300,6 +360,9 @@ def create_custom_optimzer(
    if finetuning_args.loraplus_lr_ratio is not None:
        return _create_loraplus_optimizer(model, training_args, finetuning_args)

+    if finetuning_args.use_badam:
+        return _create_badam_optimizer(model, training_args, finetuning_args)
+

 def create_custom_scheduler(
    training_args: "Seq2SeqTrainingArguments",
@@ -314,13 +377,12 @@ def create_custom_scheduler(
            scheduler_dict[param] = get_scheduler(
                training_args.lr_scheduler_type,
                optimizer=optimizer_dict[param],
-                num_warmup_steps=training_args.get_warmup_steps(num_training_steps) * 2,
-                num_training_steps=num_training_steps * 2,
+                num_warmup_steps=training_args.get_warmup_steps(num_training_steps),
+                num_training_steps=num_training_steps,
            )

        def scheduler_hook(param: "torch.nn.Parameter"):
-            if param.grad is not None:
-                scheduler_dict[param].step()
+            scheduler_dict[param].step()

        for param in optimizer_dict.keys():
            param.register_post_accumulate_grad_hook(scheduler_hook)
--- a/src/llmtuner/webui/chatter.py
+++ b/src/llmtuner/webui/chatter.py
@@ -1,13 +1,13 @@
 import json
 import os
-from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Sequence, Tuple
+from typing import TYPE_CHECKING, Dict, Generator, List, Optional, Sequence, Tuple

-import gradio as gr
-from gradio.components import Component  # cannot use TYPE_CHECKING here
+from numpy.typing import NDArray

 from ..chat import ChatModel
 from ..data import Role
 from ..extras.misc import torch_gc
+from ..extras.packages import is_gradio_available
 from .common import get_save_dir
 from .locales import ALERTS

@@ -17,6 +17,10 @@ if TYPE_CHECKING:
    from .manager import Manager


+if is_gradio_available():
+    import gradio as gr
+
+
 class WebChatModel(ChatModel):
    def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
        self.manager = manager
@@ -29,13 +33,16 @@ class WebChatModel(ChatModel):
        if demo_mode and os.environ.get("DEMO_MODEL") and os.environ.get("DEMO_TEMPLATE"):  # load demo model
            model_name_or_path = os.environ.get("DEMO_MODEL")
            template = os.environ.get("DEMO_TEMPLATE")
-            super().__init__(dict(model_name_or_path=model_name_or_path, template=template))
+            infer_backend = os.environ.get("DEMO_BACKEND", "huggingface")
+            super().__init__(
+                dict(model_name_or_path=model_name_or_path, template=template, infer_backend=infer_backend)
+            )

    @property
    def loaded(self) -> bool:
        return self.engine is not None

-    def load_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]:
+    def load_model(self, data) -> Generator[str, None, None]:
        get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
        lang = get("top.lang")
        error = ""
@@ -70,8 +77,9 @@ class WebChatModel(ChatModel):
            finetuning_type=get("top.finetuning_type"),
            quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
            template=get("top.template"),
-            flash_attn=(get("top.booster") == "flash_attn"),
+            flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
            use_unsloth=(get("top.booster") == "unsloth"),
+            visual_inputs=get("top.visual_inputs"),
            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
            infer_backend=get("infer.infer_backend"),
        )
@@ -79,7 +87,7 @@ class WebChatModel(ChatModel):

        yield ALERTS["info_loaded"][lang]

-    def unload_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]:
+    def unload_model(self, data) -> Generator[str, None, None]:
        lang = data[self.manager.get_elem_by_id("top.lang")]

        if self.demo_mode:
@@ -107,6 +115,7 @@ class WebChatModel(ChatModel):
        messages: Sequence[Dict[str, str]],
        system: str,
        tools: str,
+        image: Optional[NDArray],
        max_new_tokens: int,
        top_p: float,
        temperature: float,
@@ -114,7 +123,7 @@ class WebChatModel(ChatModel):
        chatbot[-1][1] = ""
        response = ""
        for new_text in self.stream_chat(
-            messages, system, tools, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
+            messages, system, tools, image, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature
        ):
            response += new_text
            if tools:
--- a/src/llmtuner/webui/common.py
+++ b/src/llmtuner/webui/common.py
@@ -3,13 +3,13 @@ import os
 from collections import defaultdict
 from typing import Any, Dict, Optional

-import gradio as gr
 from peft.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME

 from ..extras.constants import (
    DATA_CONFIG,
    DEFAULT_MODULE,
    DEFAULT_TEMPLATE,
+    MLLM_LIST,
    PEFT_METHODS,
    STAGES_USE_PAIR_DATA,
    SUPPORTED_MODELS,
@@ -17,6 +17,11 @@ from ..extras.constants import (
    DownloadSource,
 )
 from ..extras.misc import use_modelscope
+from ..extras.packages import is_gradio_available
+
+
+if is_gradio_available():
+    import gradio as gr


 ADAPTER_NAMES = {WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME}
@@ -101,6 +106,10 @@ def get_template(model_name: str) -> str:
    return "default"


+def get_visual(model_name: str) -> bool:
+    return get_prefix(model_name) in MLLM_LIST
+
+
 def list_adapters(model_name: str, finetuning_type: str) -> "gr.Dropdown":
    if finetuning_type not in PEFT_METHODS:
        return gr.Dropdown(value=[], choices=[], interactive=False)
--- a/src/llmtuner/webui/components/chatbot.py
+++ b/src/llmtuner/webui/components/chatbot.py
@@ -1,11 +1,14 @@
 from typing import TYPE_CHECKING, Dict, Tuple

-import gradio as gr
-
 from ...data import Role
+from ...extras.packages import is_gradio_available
 from ..utils import check_json_schema


+if is_gradio_available():
+    import gradio as gr
+
+
 if TYPE_CHECKING:
    from gradio.components import Component

@@ -14,15 +17,21 @@ if TYPE_CHECKING:

 def create_chat_box(
    engine: "Engine", visible: bool = False
-) -> Tuple["gr.Column", "Component", "Component", Dict[str, "Component"]]:
+) -> Tuple["Component", "Component", Dict[str, "Component"]]:
    with gr.Column(visible=visible) as chat_box:
        chatbot = gr.Chatbot(show_copy_button=True)
        messages = gr.State([])
        with gr.Row():
            with gr.Column(scale=4):
-                role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value)
-                system = gr.Textbox(show_label=False)
-                tools = gr.Textbox(show_label=False, lines=2)
+                with gr.Row():
+                    with gr.Column():
+                        role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value)
+                        system = gr.Textbox(show_label=False)
+                        tools = gr.Textbox(show_label=False, lines=3)
+
+                    with gr.Column() as image_box:
+                        image = gr.Image(sources=["upload"], type="numpy")
+
                query = gr.Textbox(show_label=False, lines=8)
                submit_btn = gr.Button(variant="primary")

@@ -40,19 +49,21 @@ def create_chat_box(
        [chatbot, messages, query],
    ).then(
        engine.chatter.stream,
-        [chatbot, messages, system, tools, max_new_tokens, top_p, temperature],
+        [chatbot, messages, system, tools, image, max_new_tokens, top_p, temperature],
        [chatbot, messages],
    )
    clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])

    return (
-        chat_box,
        chatbot,
        messages,
        dict(
+            chat_box=chat_box,
            role=role,
            system=system,
            tools=tools,
+            image_box=image_box,
+            image=image,
            query=query,
            submit_btn=submit_btn,
            max_new_tokens=max_new_tokens,
--- a/src/llmtuner/webui/components/data.py
+++ b/src/llmtuner/webui/components/data.py
@@ -1,10 +1,13 @@
 import json
 import os
-from typing import TYPE_CHECKING, Dict, Tuple
-
-import gradio as gr
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple

 from ...extras.constants import DATA_CONFIG
+from ...extras.packages import is_gradio_available
+
+
+if is_gradio_available():
+    import gradio as gr


 if TYPE_CHECKING:
@@ -29,28 +32,38 @@ def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
    except Exception:
        return gr.Button(interactive=False)

-    if (
-        len(dataset) > 0
-        and "file_name" in dataset_info[dataset[0]]
-        and os.path.isfile(os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"]))
-    ):
+    if len(dataset) == 0 or "file_name" not in dataset_info[dataset[0]]:
+        return gr.Button(interactive=False)
+
+    data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])
+    if os.path.isfile(data_path) or (os.path.isdir(data_path) and os.listdir(data_path)):
        return gr.Button(interactive=True)
    else:
        return gr.Button(interactive=False)


+def _load_data_file(file_path: str) -> List[Any]:
+    with open(file_path, "r", encoding="utf-8") as f:
+        if file_path.endswith(".json"):
+            return json.load(f)
+        elif file_path.endswith(".jsonl"):
+            return [json.loads(line) for line in f]
+        else:
+            return list(f)
+
+
 def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int, list, "gr.Column"]:
    with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
        dataset_info = json.load(f)

-    data_file: str = dataset_info[dataset[0]]["file_name"]
-    with open(os.path.join(dataset_dir, data_file), "r", encoding="utf-8") as f:
-        if data_file.endswith(".json"):
-            data = json.load(f)
-        elif data_file.endswith(".jsonl"):
-            data = [json.loads(line) for line in f]
-        else:
-            data = [line for line in f]  # noqa: C416
+    data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])
+    if os.path.isfile(data_path):
+        data = _load_data_file(data_path)
+    else:
+        data = []
+        for file_name in os.listdir(data_path):
+            data.extend(_load_data_file(os.path.join(data_path, file_name)))
+
    return len(data), data[PAGE_SIZE * page_index : PAGE_SIZE * (page_index + 1)], gr.Column(visible=True)


--- a/src/llmtuner/webui/components/eval.py
+++ b/src/llmtuner/webui/components/eval.py
@@ -1,11 +1,14 @@
 from typing import TYPE_CHECKING, Dict

-import gradio as gr
-
+from ...extras.packages import is_gradio_available
 from ..common import DEFAULT_DATA_DIR, list_dataset
 from .data import create_preview_box


+if is_gradio_available():
+    import gradio as gr
+
+
 if TYPE_CHECKING:
    from gradio.components import Component

@@ -18,7 +21,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:

    with gr.Row():
        dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
-        dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
+        dataset = gr.Dropdown(multiselect=True, scale=4)
        preview_elems = create_preview_box(dataset_dir, dataset)

    input_elems.update({dataset_dir, dataset})
--- a/src/llmtuner/webui/components/export.py
+++ b/src/llmtuner/webui/components/export.py
@@ -1,12 +1,16 @@
 from typing import TYPE_CHECKING, Dict, Generator, List

-import gradio as gr
-
+from ...extras.misc import torch_gc
+from ...extras.packages import is_gradio_available
 from ...train import export_model
 from ..common import get_save_dir
 from ..locales import ALERTS


+if is_gradio_available():
+    import gradio as gr
+
+
 if TYPE_CHECKING:
    from gradio.components import Component

@@ -23,9 +27,11 @@ def save_model(
    adapter_path: List[str],
    finetuning_type: str,
    template: str,
-    max_shard_size: int,
+    visual_inputs: bool,
+    export_size: int,
    export_quantization_bit: int,
    export_quantization_dataset: str,
+    export_device: str,
    export_legacy_format: bool,
    export_dir: str,
    export_hub_model_id: str,
@@ -41,6 +47,8 @@ def save_model(
        error = ALERTS["err_no_dataset"][lang]
    elif export_quantization_bit not in GPTQ_BITS and not adapter_path:
        error = ALERTS["err_no_adapter"][lang]
+    elif export_quantization_bit in GPTQ_BITS and adapter_path:
+        error = ALERTS["err_gptq_lora"][lang]

    if error:
        gr.Warning(error)
@@ -59,24 +67,28 @@ def save_model(
        adapter_name_or_path=adapter_name_or_path,
        finetuning_type=finetuning_type,
        template=template,
+        visual_inputs=visual_inputs,
        export_dir=export_dir,
        export_hub_model_id=export_hub_model_id or None,
-        export_size=max_shard_size,
+        export_size=export_size,
        export_quantization_bit=int(export_quantization_bit) if export_quantization_bit in GPTQ_BITS else None,
        export_quantization_dataset=export_quantization_dataset,
+        export_device=export_device,
        export_legacy_format=export_legacy_format,
    )

    yield ALERTS["info_exporting"][lang]
    export_model(args)
+    torch_gc()
    yield ALERTS["info_exported"][lang]


 def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
    with gr.Row():
-        max_shard_size = gr.Slider(value=1, minimum=1, maximum=100, step=1)
+        export_size = gr.Slider(value=1, minimum=1, maximum=100, step=1)
        export_quantization_bit = gr.Dropdown(choices=["none", "8", "4", "3", "2"], value="none")
        export_quantization_dataset = gr.Textbox(value="data/c4_demo.json")
+        export_device = gr.Radio(choices=["cpu", "cuda"], value="cpu")
        export_legacy_format = gr.Checkbox()

    with gr.Row():
@@ -95,9 +107,11 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
            engine.manager.get_elem_by_id("top.adapter_path"),
            engine.manager.get_elem_by_id("top.finetuning_type"),
            engine.manager.get_elem_by_id("top.template"),
-            max_shard_size,
+            engine.manager.get_elem_by_id("top.visual_inputs"),
+            export_size,
            export_quantization_bit,
            export_quantization_dataset,
+            export_device,
            export_legacy_format,
            export_dir,
            export_hub_model_id,
@@ -106,9 +120,10 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
    )

    return dict(
-        max_shard_size=max_shard_size,
+        export_size=export_size,
        export_quantization_bit=export_quantization_bit,
        export_quantization_dataset=export_quantization_dataset,
+        export_device=export_device,
        export_legacy_format=export_legacy_format,
        export_dir=export_dir,
        export_hub_model_id=export_hub_model_id,
--- a/src/llmtuner/webui/components/infer.py
+++ b/src/llmtuner/webui/components/infer.py
@@ -1,10 +1,13 @@
 from typing import TYPE_CHECKING, Dict

-import gradio as gr
-
+from ...extras.packages import is_gradio_available
 from .chatbot import create_chat_box


+if is_gradio_available():
+    import gradio as gr
+
+
 if TYPE_CHECKING:
    from gradio.components import Component

@@ -25,15 +28,21 @@ def create_infer_tab(engine: "Engine") -> Dict[str, "Component"]:
    input_elems.update({infer_backend})
    elem_dict.update(dict(infer_backend=infer_backend, load_btn=load_btn, unload_btn=unload_btn, info_box=info_box))

-    chat_box, chatbot, messages, chat_elems = create_chat_box(engine, visible=False)
-    elem_dict.update(dict(chat_box=chat_box, **chat_elems))
+    chatbot, messages, chat_elems = create_chat_box(engine, visible=False)
+    elem_dict.update(chat_elems)

    load_btn.click(engine.chatter.load_model, input_elems, [info_box]).then(
-        lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_box]
+        lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]]
    )

    unload_btn.click(engine.chatter.unload_model, input_elems, [info_box]).then(
        lambda: ([], []), outputs=[chatbot, messages]
-    ).then(lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_box])
+    ).then(lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]])
+
+    engine.manager.get_elem_by_id("top.visual_inputs").change(
+        lambda enabled: gr.Column(visible=enabled),
+        [engine.manager.get_elem_by_id("top.visual_inputs")],
+        [chat_elems["image_box"]],
+    )

    return elem_dict
--- a/src/llmtuner/webui/components/top.py
+++ b/src/llmtuner/webui/components/top.py
@@ -1,13 +1,16 @@
 from typing import TYPE_CHECKING, Dict

-import gradio as gr
-
 from ...data import templates
 from ...extras.constants import METHODS, SUPPORTED_MODELS
-from ..common import get_model_path, get_template, list_adapters, save_config
+from ...extras.packages import is_gradio_available
+from ..common import get_model_path, get_template, get_visual, list_adapters, save_config
 from ..utils import can_quantize


+if is_gradio_available():
+    import gradio as gr
+
+
 if TYPE_CHECKING:
    from gradio.components import Component

@@ -27,14 +30,17 @@ def create_top() -> Dict[str, "Component"]:

    with gr.Accordion(open=False) as advanced_tab:
        with gr.Row():
-            quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none")
-            template = gr.Dropdown(choices=list(templates.keys()), value="default")
-            rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none")
-            booster = gr.Radio(choices=["none", "flashattn", "unsloth"], value="none")
+            quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", scale=2)
+            template = gr.Dropdown(choices=list(templates.keys()), value="default", scale=2)
+            rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none", scale=3)
+            booster = gr.Radio(choices=["none", "flashattn2", "unsloth"], value="none", scale=3)
+            visual_inputs = gr.Checkbox(scale=1)

    model_name.change(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False).then(
        get_model_path, [model_name], [model_path], queue=False
-    ).then(get_template, [model_name], [template], queue=False)  # do not save config since the below line will save
+    ).then(get_template, [model_name], [template], queue=False).then(
+        get_visual, [model_name], [visual_inputs], queue=False
+    )  # do not save config since the below line will save

    model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False)

@@ -56,4 +62,5 @@ def create_top() -> Dict[str, "Component"]:
        template=template,
        rope_scaling=rope_scaling,
        booster=booster,
+        visual_inputs=visual_inputs,
    )
--- a/src/llmtuner/webui/components/train.py
+++ b/src/llmtuner/webui/components/train.py
@@ -1,13 +1,17 @@
 from typing import TYPE_CHECKING, Dict

-import gradio as gr
 from transformers.trainer_utils import SchedulerType

 from ...extras.constants import TRAINING_STAGES
+from ...extras.packages import is_gradio_available
 from ..common import DEFAULT_DATA_DIR, autoset_packing, list_adapters, list_dataset
 from ..components.data import create_preview_box


+if is_gradio_available():
+    import gradio as gr
+
+
 if TYPE_CHECKING:
    from gradio.components import Component

@@ -23,7 +27,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
            choices=list(TRAINING_STAGES.keys()), value=list(TRAINING_STAGES.keys())[0], scale=1
        )
        dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=1)
-        dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
+        dataset = gr.Dropdown(multiselect=True, scale=4)
        preview_elems = create_preview_box(dataset_dir, dataset)

    input_elems.update({training_stage, dataset_dir, dataset})
@@ -134,7 +138,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
        with gr.Row():
            lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1)
            lora_alpha = gr.Slider(value=16, minimum=1, maximum=2048, step=1)
-            lora_dropout = gr.Slider(value=0.1, minimum=0, maximum=1, step=0.01)
+            lora_dropout = gr.Slider(value=0, minimum=0, maximum=1, step=0.01)
            loraplus_lr_ratio = gr.Slider(value=0, minimum=0, maximum=64, step=0.01)
            create_new_adapter = gr.Checkbox()

--- a/src/llmtuner/webui/engine.py
+++ b/src/llmtuner/webui/engine.py
@@ -1,6 +1,4 @@
-from typing import Any, Dict, Generator
-
-from gradio.components import Component  # cannot use TYPE_CHECKING here
+from typing import TYPE_CHECKING, Any, Dict

 from .chatter import WebChatModel
 from .common import get_model_path, list_dataset, load_config
@@ -10,6 +8,10 @@ from .runner import Runner
 from .utils import get_time


+if TYPE_CHECKING:
+    from gradio.components import Component
+
+
 class Engine:
    def __init__(self, demo_mode: bool = False, pure_chat: bool = False) -> None:
        self.demo_mode = demo_mode
@@ -29,7 +31,7 @@ class Engine:

        return output_dict

-    def resume(self) -> Generator[Dict[Component, Component], None, None]:
+    def resume(self):
        user_config = load_config() if not self.demo_mode else {}
        lang = user_config.get("lang", None) or "en"

@@ -41,6 +43,7 @@ class Engine:
            init_dict["train.output_dir"] = {"value": "train_{}".format(get_time())}
            init_dict["train.config_path"] = {"value": "{}.json".format(get_time())}
            init_dict["eval.output_dir"] = {"value": "eval_{}".format(get_time())}
+            init_dict["infer.image_box"] = {"visible": False}

            if user_config.get("last_model", None):
                init_dict["top.model_name"] = {"value": user_config["last_model"]}
@@ -55,7 +58,7 @@ class Engine:
            else:
                yield self._update_component({"eval.resume_btn": {"value": True}})

-    def change_lang(self, lang: str) -> Dict[Component, Component]:
+    def change_lang(self, lang: str):
        return {
            elem: elem.__class__(**LOCALES[elem_name][lang])
            for elem_name, elem in self.manager.get_elem_iter()
--- a/src/llmtuner/webui/interface.py
+++ b/src/llmtuner/webui/interface.py
@@ -1,5 +1,4 @@
-import gradio as gr
-
+from ..extras.packages import is_gradio_available
 from .common import save_config
 from .components import (
    create_chat_box,
@@ -13,6 +12,10 @@ from .css import CSS
 from .engine import Engine


+if is_gradio_available():
+    import gradio as gr
+
+
 def create_ui(demo_mode: bool = False) -> gr.Blocks:
    engine = Engine(demo_mode=demo_mode, pure_chat=False)

@@ -55,8 +58,8 @@ def create_web_demo() -> gr.Blocks:
        lang = gr.Dropdown(choices=["en", "zh"])
        engine.manager.add_elems("top", dict(lang=lang))

-        chat_box, _, _, chat_elems = create_chat_box(engine, visible=True)
-        engine.manager.add_elems("infer", dict(chat_box=chat_box, **chat_elems))
+        _, _, chat_elems = create_chat_box(engine, visible=True)
+        engine.manager.add_elems("infer", chat_elems)

        demo.load(engine.resume, outputs=engine.manager.get_elem_list(), concurrency_limit=None)
        lang.change(engine.change_lang, [lang], engine.manager.get_elem_list(), queue=False)
--- a/src/llmtuner/webui/locales.py
+++ b/src/llmtuner/webui/locales.py
@@ -129,6 +129,17 @@ LOCALES = {
            "label": "加速方式",
        },
    },
+    "visual_inputs": {
+        "en": {
+            "label": "Visual inputs",
+        },
+        "ru": {
+            "label": "визуальные входы",
+        },
+        "zh": {
+            "label": "图像输入",
+        },
+    },
    "training_stage": {
        "en": {
            "label": "Stage",
@@ -1073,6 +1084,17 @@ LOCALES = {
            "placeholder": "工具列表（非必填）",
        },
    },
+    "image": {
+        "en": {
+            "label": "Image (optional)",
+        },
+        "ru": {
+            "label": "Изображение (по желанию)",
+        },
+        "zh": {
+            "label": "图像（非必填）",
+        },
+    },
    "query": {
        "en": {
            "placeholder": "Input...",
@@ -1150,7 +1172,7 @@ LOCALES = {
            "value": "清空历史",
        },
    },
-    "max_shard_size": {
+    "export_size": {
        "en": {
            "label": "Max shard size (GB)",
            "info": "The maximum size for a model file.",
@@ -1192,6 +1214,20 @@ LOCALES = {
            "info": "量化过程中使用的校准数据集。",
        },
    },
+    "export_device": {
+        "en": {
+            "label": "Export device",
+            "info": "Which device should be used to export model.",
+        },
+        "ru": {
+            "label": "Экспорт устройство",
+            "info": "Какое устройство следует использовать для экспорта модели.",
+        },
+        "zh": {
+            "label": "导出设备",
+            "info": "导出模型使用的设备类型。",
+        },
+    },
    "export_legacy_format": {
        "en": {
            "label": "Export legacy format",
@@ -1287,7 +1323,12 @@ ALERTS = {
    "err_no_export_dir": {
        "en": "Please provide export dir.",
        "ru": "Пожалуйста, укажите каталог для экспорта.",
-        "zh": "请填写导出目录",
+        "zh": "请填写导出目录。",
+    },
+    "err_gptq_lora": {
+        "en": "Please merge adapters before quantizing the model.",
+        "ru": "Пожалуйста, объедините адаптеры перед квантованием модели.",
+        "zh": "量化模型前请先合并适配器。",
    },
    "err_failed": {
        "en": "Failed.",
--- a/src/llmtuner/webui/manager.py
+++ b/src/llmtuner/webui/manager.py
@@ -60,4 +60,5 @@ class Manager:
            self._id_to_elem["top.template"],
            self._id_to_elem["top.rope_scaling"],
            self._id_to_elem["top.booster"],
+            self._id_to_elem["top.visual_inputs"],
        }
--- a/src/llmtuner/webui/runner.py
+++ b/src/llmtuner/webui/runner.py
@@ -4,9 +4,7 @@ import time
 from threading import Thread
 from typing import TYPE_CHECKING, Any, Dict, Generator

-import gradio as gr
 import transformers
-from gradio.components import Component  # cannot use TYPE_CHECKING here
 from transformers.trainer import TRAINING_ARGS_NAME
 from transformers.utils import is_torch_cuda_available

@@ -14,13 +12,20 @@ from ..extras.callbacks import LogCallback
 from ..extras.constants import TRAINING_STAGES
 from ..extras.logging import LoggerHandler
 from ..extras.misc import get_device_count, torch_gc
+from ..extras.packages import is_gradio_available
 from ..train import run_exp
 from .common import get_module, get_save_dir, load_args, load_config, save_args
 from .locales import ALERTS
 from .utils import gen_cmd, gen_plot, get_eval_results, update_process_bar


+if is_gradio_available():
+    import gradio as gr
+
+
 if TYPE_CHECKING:
+    from gradio.components import Component
+
    from .manager import Manager


@@ -62,7 +67,7 @@ class Runner:
        if not model_path:
            return ALERTS["err_no_path"][lang]

-        if len(dataset) == 0:
+        if not dataset:
            return ALERTS["err_no_dataset"][lang]

        if not from_preview and self.demo_mode:
@@ -117,8 +122,9 @@ class Runner:
            quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
            template=get("top.template"),
            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
-            flash_attn=(get("top.booster") == "flashattn"),
+            flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
            use_unsloth=(get("top.booster") == "unsloth"),
+            visual_inputs=get("top.visual_inputs"),
            dataset_dir=get("train.dataset_dir"),
            dataset=",".join(get("train.dataset")),
            cutoff_len=get("train.cutoff_len"),
@@ -217,8 +223,9 @@ class Runner:
            quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
            template=get("top.template"),
            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
-            flash_attn=(get("top.booster") == "flashattn"),
+            flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
            use_unsloth=(get("top.booster") == "unsloth"),
+            visual_inputs=get("top.visual_inputs"),
            dataset_dir=get("eval.dataset_dir"),
            dataset=",".join(get("eval.dataset")),
            cutoff_len=get("eval.cutoff_len"),
@@ -239,7 +246,7 @@ class Runner:

        return args

-    def _preview(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict[Component, str], None, None]:
+    def _preview(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", str], None, None]:
        output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
        error = self._initialize(data, do_train, from_preview=True)
        if error:
@@ -249,7 +256,7 @@ class Runner:
            args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
            yield {output_box: gen_cmd(args)}

-    def _launch(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict[Component, Any], None, None]:
+    def _launch(self, data: Dict["Component", Any], do_train: bool) -> Generator[Dict["Component", Any], None, None]:
        output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
        error = self._initialize(data, do_train, from_preview=False)
        if error:
@@ -263,19 +270,19 @@ class Runner:
            self.thread.start()
            yield from self.monitor()

-    def preview_train(self, data: Dict[Component, Any]) -> Generator[Dict[Component, str], None, None]:
+    def preview_train(self, data):
        yield from self._preview(data, do_train=True)

-    def preview_eval(self, data: Dict[Component, Any]) -> Generator[Dict[Component, str], None, None]:
+    def preview_eval(self, data):
        yield from self._preview(data, do_train=False)

-    def run_train(self, data: Dict[Component, Any]) -> Generator[Dict[Component, Any], None, None]:
+    def run_train(self, data):
        yield from self._launch(data, do_train=True)

-    def run_eval(self, data: Dict[Component, Any]) -> Generator[Dict[Component, Any], None, None]:
+    def run_eval(self, data):
        yield from self._launch(data, do_train=False)

-    def monitor(self) -> Generator[Dict[Component, Any], None, None]:
+    def monitor(self):
        get = lambda elem_id: self.running_data[self.manager.get_elem_by_id(elem_id)]
        self.aborted = False
        self.running = True
@@ -332,7 +339,7 @@ class Runner:

        yield return_dict

-    def save_args(self, data: Dict[Component, Any]) -> Dict[Component, str]:
+    def save_args(self, data):
        output_box = self.manager.get_elem_by_id("train.output_box")
        error = self._initialize(data, do_train=True, from_preview=True)
        if error:
@@ -351,7 +358,7 @@ class Runner:
        save_path = save_args(config_path, config_dict)
        return {output_box: ALERTS["info_config_saved"][lang] + save_path}

-    def load_args(self, lang: str, config_path: str) -> Dict[Component, Any]:
+    def load_args(self, lang: str, config_path: str):
        output_box = self.manager.get_elem_by_id("train.output_box")
        config_dict = load_args(config_path)
        if config_dict is None:
--- a/src/llmtuner/webui/utils.py
+++ b/src/llmtuner/webui/utils.py
@@ -3,21 +3,24 @@ import os
 from datetime import datetime
 from typing import TYPE_CHECKING, Any, Dict, Optional

-import gradio as gr
-
-from ..extras.packages import is_matplotlib_available
+from ..extras.packages import is_gradio_available, is_matplotlib_available
 from ..extras.ploting import smooth
 from .locales import ALERTS


-if TYPE_CHECKING:
-    from ..extras.callbacks import LogCallback
+if is_gradio_available():
+    import gradio as gr
+

 if is_matplotlib_available():
    import matplotlib.figure
    import matplotlib.pyplot as plt


+if TYPE_CHECKING:
+    from ..extras.callbacks import LogCallback
+
+
 def update_process_bar(callback: "LogCallback") -> "gr.Slider":
    if not callback.max_steps:
        return gr.Slider(visible=False)
Author	SHA1	Message	Date
hiyouga	3cef844079	fix setup Former-commit-id: 7d3e7db46a5f8672dd57fa5fcc03822e175047f9	2024-04-28 03:49:13 +08:00
hiyouga	4dcd47100d	fix llava rlhf Former-commit-id: f6863cbbcbf960d6481296c6cae3e40fd70e4e14	2024-04-28 03:01:49 +08:00
hiyouga	a412b4ed4a	add models to 0.7.0 Former-commit-id: 436d3754452f839c617839ab3bbaacc4a8908e19	2024-04-28 01:50:30 +08:00
hiyouga	544a6259b6	update readme Former-commit-id: c9190fe36f511c3a5149d45c85a10b02a57fa88a	2024-04-26 23:39:19 +08:00
hiyouga	c501f377dd	release v0.7.0 Former-commit-id: 45bb89cb4d26a6b3fb5360bc90ab950738fe4920	2024-04-26 23:18:00 +08:00
hiyouga	cb8b8f40cd	update readme Former-commit-id: f3d4b46338d4d484b205d0651a1fa7b2e77a1654	2024-04-26 20:09:14 +08:00
hiyouga	70bed8ad8f	support Qwen1.5 110B Former-commit-id: d6e5ecaf4109127bab24e39a0696076bceb0b37c	2024-04-26 19:59:22 +08:00
hiyouga	51f776ae2a	fix llava qlora Former-commit-id: 01c5a669f6fe598aac1758a700a7607da37db1bc	2024-04-26 18:00:23 +08:00
hiyouga	697bc20941	add llava to llamaboard Former-commit-id: deaaff0a9de0eef9691991c99cd797461b1165cc	2024-04-26 06:41:35 +08:00
hiyouga	1480e3a88f	update readme Former-commit-id: df1155245d3f71ba4f3361d43aa662ab3b024de8	2024-04-26 05:49:26 +08:00
hoshi-hiyouga	19029d5b0f	Merge pull request #3454 from hiyouga/mllm Support fine-tuning LLaVA-1.5 MLLM @BUAADreamer Former-commit-id: c4195d1e26349795f7aad5c10a8a9e2abb7b64a3	2024-04-26 05:46:29 +08:00
hiyouga	7773ac0ead	update readme Former-commit-id: 41728fd74de7bec0cc6135aef9dfa3ae9fe7af73	2024-04-26 05:44:30 +08:00
hiyouga	23b881bff1	support mllm hf inference Former-commit-id: 2c7c01282acd7ddabbb17ce3246b8dae4bc4b8cf	2024-04-26 05:34:58 +08:00
hoshi-hiyouga	10a6c395bb	Merge pull request #3450 from BUAADreamer/mllm Add Multimodal LLM Finetuning Former-commit-id: 7cacbcfdf7391080ef43eb2b2c79a5237e6120e8	2024-04-26 05:30:30 +08:00
hoshi-hiyouga	f9a7732a1f	Update preprocess.py Former-commit-id: 0e376eab23d38b8fca05f054f3cde308756ee3b1	2024-04-26 04:10:28 +08:00
hoshi-hiyouga	c37582af02	Update aligner.py Former-commit-id: 855489074c469f47572153df0fa1e251b187b232	2024-04-26 03:48:34 +08:00
hoshi-hiyouga	ece67f8c7f	Update parser.py Former-commit-id: 4df75e8a9a391565cc3eec69bc0ebf5d5192de61	2024-04-26 03:35:39 +08:00
hoshi-hiyouga	e1838e76fe	Update loader.py Former-commit-id: 6a5f2e2ab7304113ff71cb77aafff6a1f74831f8	2024-04-26 03:33:07 +08:00
hoshi-hiyouga	2eede9ffd6	Update workflow.py Former-commit-id: 5b8b5b975716d539ae2fae8536f79e106aa0b566	2024-04-26 03:29:12 +08:00
hoshi-hiyouga	a6f6b406b3	Update loader.py Former-commit-id: 72d4817a15f6916706828ea2a61d808183c23773	2024-04-26 03:22:40 +08:00
hoshi-hiyouga	279439abbe	update hparam name Former-commit-id: 9941adfbf06db37f8ba32c4555f6e58e27188aaf	2024-04-26 02:49:39 +08:00
hoshi-hiyouga	13117b69d7	delete llava template (use vicuna) Former-commit-id: 420e64970e5a0e45453041927e0366ee8beb73d5	2024-04-26 02:20:47 +08:00
BUAADreamer	5d03ac642d	modify some bug Former-commit-id: 593b7b004df74bd24361c9883401a656c08fb589	2024-04-25 22:59:46 +08:00
BUAADreamer	5062ee547e	modify some style Former-commit-id: 1291c7ee39361dd75247c67f04dcf20b472faf83	2024-04-25 22:40:53 +08:00
BUAADreamer	59817c27e3	modify some style Former-commit-id: d578a90cefa7ec813355795bdd6ead5ee558ce26	2024-04-25 22:40:25 +08:00
BUAADreamer	759bee48d2	merge some func Former-commit-id: 3085107c44715e4b2ca96d73b20d90c172b95219	2024-04-25 22:35:17 +08:00
BUAADreamer	514ffafc12	modify some style Former-commit-id: 053062abc007014a7fde95c5ae9f4d859893d8ad	2024-04-25 22:04:09 +08:00
BUAADreamer	8b2a735c14	modify some style Former-commit-id: b016e6a671a2f228f0bdd9b8d5995b4669609655	2024-04-25 21:58:18 +08:00
BUAADreamer	10d59e9e4a	make dataset script Former-commit-id: 25892f958da14976025a775febf628cd0e0a3d85	2024-04-25 21:32:01 +08:00
BUAADreamer	058ed5e607	modify style Former-commit-id: c1f1df99e4dc3d0aadf1207b4e9a16218187fd5a	2024-04-25 21:29:50 +08:00
BUAADreamer	110c2ce2a5	modify style Former-commit-id: 3bffc1e1b8bcc4582cebea06d35e5146163c7bec	2024-04-25 21:27:48 +08:00
BUAADreamer	c425436676	modify style Former-commit-id: 54b713d0c4ffdfc6a7faeb14471b58bb1cd8acf5	2024-04-25 21:15:16 +08:00
BUAADreamer	266fe908e3	Merge branch 'main' of https://github.com/BUAADreamer/LLaMA-Factory Former-commit-id: c4bb5af69c5bbf0b1ea044cbb2b18acddc6733ac	2024-04-25 21:08:40 +08:00
BUAADreamer	dbd905438b	add some Former-commit-id: 8d035a849c4a441d457791aab073861adf69a09f	2024-04-25 21:08:32 +08:00
hoshi-hiyouga	d64c87f928	Merge pull request #3449 from hiyouga/mllm add webui backend option Former-commit-id: 372fcedef40b79fe8bd3932c06c720f2a03db6e6	2024-04-25 20:58:16 +08:00
hiyouga	29eebef696	add webui backend option Former-commit-id: 3764586cb3ed64fe376d0ae420ff5690c28459e2	2024-04-25 20:49:23 +08:00
hiyouga	7bfbcb1fe3	vllm + lora support Former-commit-id: 8cb86ba355195f5d6dcb95ee6b6b7203463a34db	2024-04-25 20:24:31 +08:00
BUAADreamer	9b210cf4b3	rm some Former-commit-id: 2c85b4fabbebd8b51eee53f5d29184d4a6e97569	2024-04-25 20:09:43 +08:00
BUAADreamer	f74e640565	Merge branch 'hiyouga:main' into main Former-commit-id: 131d0bcd554dedd794add7eb3d7b1201cac80e7c	2024-04-25 20:02:50 +08:00
BUAADreamer	d1d08d066a	merge data part to the text stream Former-commit-id: 80537d580119d9d5a06ab236a5284aaae2f83b5b	2024-04-25 19:58:47 +08:00
hiyouga	6be321b5da	fix #3374 Former-commit-id: 0097d7968b3b570e1705caff26f42d9ed71ad974	2024-04-25 19:56:49 +08:00
BUAADreamer	3c792174db	merge data part to the text stream Former-commit-id: 7ee20286d9bcc2d5378bfd6bb02cd3648396d873	2024-04-25 19:19:59 +08:00
hiyouga	9aeb88c426	add export_device in webui #3333 Former-commit-id: 30ebd3652809d73941e0a5e4a8be11d989faf98d	2024-04-25 19:02:32 +08:00
BUAADreamer	00e2a272ef	merge model part to the text stream Former-commit-id: b6fcb832ddaed4647d6f2b926f3dfccd47f3ea84	2024-04-25 08:20:41 +08:00
BUAADreamer	5142349661	remove error Former-commit-id: 2bcd1c7dc3595f17ae4e2c4475196cc2d03d0e75	2024-04-25 01:01:59 +08:00
BUAADreamer	0e3cc52327	remove conflicts Former-commit-id: e5750ee202eb67cf5fc54f464548e2eb43d00900	2024-04-25 00:56:06 +08:00
BUAADreamer	6c1db2d012	remove conflicts Former-commit-id: f8b637eb76cba7ec229e2978068805ad1cca8adb	2024-04-25 00:34:22 +08:00
BUAADreamer	12c51655ce	add llava and instructblip Former-commit-id: 142fb6f4541a1acfefe66ff2574dabde53b00c06	2024-04-25 00:22:43 +08:00
hiyouga	36be12a3b7	update tool template Former-commit-id: c72a1981859818c257c5271d32e03c9d3c344206	2024-04-25 00:21:34 +08:00
hiyouga	21fac4c98c	fix log level Former-commit-id: 8d21302f6201b3f33c10f61f3559bd95be3363c2	2024-04-24 23:42:59 +08:00
hiyouga	83404c4fa9	support new special token #3420 Former-commit-id: f5c6a47f5193ab3a6c137580992bdcce0b31fdd5	2024-04-24 23:39:31 +08:00
hoshi-hiyouga	12f852b8d4	fix phi template Former-commit-id: 14a1ff665eaebfc618229efbe96f09848d52faec	2024-04-24 13:55:14 +08:00
hoshi-hiyouga	a88873116a	fix webchatmodel Former-commit-id: dc6d8b5dc42c363dd180aaf90c9a2f2d0cce6725	2024-04-24 13:54:21 +08:00
hoshi-hiyouga	7cfcd69c64	fix inference in llamaboard Former-commit-id: 5e631915157083b61e2d5a183e0c91f2d11f416e	2024-04-24 13:53:39 +08:00
hiyouga	a5eabbe933	add olmo 1.7 Former-commit-id: 86a3fb3a141d2702b15af08df36ffcf9b3d6de14	2024-04-24 05:50:50 +08:00
hiyouga	aa25716a5d	add dbrx and jamba models Former-commit-id: ce35c80b4b00152185285d6064939803d14487f0	2024-04-24 05:39:52 +08:00
hiyouga	94c8219575	fix bug Former-commit-id: 38e164fe4aaea6f0baf121a720291ca42643ba8c	2024-04-24 05:21:18 +08:00
hiyouga	ad24a2a0c9	fix bug Former-commit-id: 271c24d2c82d645fa9072e6de94ca38f20411537	2024-04-24 05:10:07 +08:00
hiyouga	c05027d14a	remove redundant code Former-commit-id: 4a7a7ad2bcdc493458084f5f3d384239228b7d5a	2024-04-24 05:02:18 +08:00
hiyouga	5420905a2e	support unsloth generate Former-commit-id: 0ef1ad9f505dba71db9342f524cc3a7565e5e09e	2024-04-24 04:46:53 +08:00
hiyouga	03f2e3284a	refactor patcher Former-commit-id: 263cfe1294f5c3188f5e8d65791f35ee0d87315a	2024-04-24 03:02:23 +08:00
hiyouga	d2bb1b3a6b	reenable sdpa and fast tok by default Former-commit-id: 9e00902dbedc71d55743d1bf237843506a557891	2024-04-24 02:18:44 +08:00
hiyouga	35c4a2c212	fix #3347 #3387 Former-commit-id: c253c18185a29b59190f3e0ed236c2bb4c788085	2024-04-24 01:30:16 +08:00
hiyouga	1e4010a1fb	support phi-3 Former-commit-id: 7e8ffa9beee3893e051ceeade443bd56c4a07b1c	2024-04-24 00:28:53 +08:00
BUAADreamer	1451297c78	add multimodal LLM BLIP-2 and InstructBLIP Former-commit-id: 67800c565b086f362b8cf131b0c9babaa7a7ebc7	2024-04-23 19:22:42 +08:00
BUAADreamer	0b99b13786	add multimodal LLM BLIP-2 and InstructBLIP Former-commit-id: b78b5f290aa38a7454e101ee9703fb6fac5064ac	2024-04-23 18:47:03 +08:00
BUAADreamer	f5edbf2b49	Merge branch 'hiyouga:main' into main Former-commit-id: 6287d1b789c631205c1033adf036e28deaef4167	2024-04-23 18:46:12 +08:00
BUAADreamer	ab6dc0ea30	add multimodal LLM BLIP-2 and InstructBLIP Former-commit-id: a730f89a972f1a9d37c718c716f199cb8d4903b2	2024-04-23 18:45:43 +08:00
hiyouga	79d34ce0f3	update examples Former-commit-id: 8bf55682cdfbbdca0f01073eac0084c20a6a09d1	2024-04-23 18:29:46 +08:00
hiyouga	1d2e372a8e	update readme Former-commit-id: d4eaee262a64e716ce475dc4eb18d8d9697d8dd8	2024-04-22 17:09:17 +08:00
hiyouga	f6a53d83c8	update readme Former-commit-id: 3eab580703ee01a0d2d75e7f01df5165af551386	2024-04-22 00:51:35 +08:00
hiyouga	4ec56dd958	update readme Former-commit-id: fdca136309709e43d75a831252b9375a5a99635a	2024-04-22 00:42:25 +08:00
hiyouga	ba06eb65ca	update readme and examples Former-commit-id: 27dd9bf201c24f7804811398bc2758966ec78432	2024-04-22 00:37:32 +08:00
hiyouga	be716972fe	remove extras Former-commit-id: d67e972f8c3d5273e589c8c85c0a1620f59785c5	2024-04-22 00:35:41 +08:00
hiyouga	719585a128	update readme Former-commit-id: 3a8c17907c71f46b1b37501e2afdc99ad89fb4bc	2024-04-22 00:21:01 +08:00
hiyouga	348f29aa50	set dev version Former-commit-id: b9557887d7506ff57b2b2bf490092aac4e4becf0	2024-04-21 23:14:30 +08:00
hiyouga	c8fe3f544b	release v0.6.3 Former-commit-id: 947572af8de201669598f54735f35b50bb719d71	2024-04-21 23:13:23 +08:00
hiyouga	0f1ad7140f	fix #3366 Former-commit-id: dc20237455c36de44f8922539d7dfadd8bedb12f	2024-04-21 21:34:25 +08:00
hiyouga	233e167f68	fix optimizers Former-commit-id: f811eee2fa12a89a55a9c5d3a05a1521b4347727	2024-04-21 20:40:54 +08:00
hiyouga	1d341dcd83	fix #3365 Former-commit-id: 415ce41e8fa887e980e5bd575c8e95bd4076b90b	2024-04-21 19:20:18 +08:00
hiyouga	d16561e7a4	fix bug in galore optimizer Former-commit-id: c05ac23261a5a8ba893c2918a43dc7777307407b	2024-04-21 18:53:22 +08:00
hiyouga	f8e219dc81	fix mod stuff Former-commit-id: cf3988226e6398c67bb2955578e436fc505aa5c5	2024-04-21 18:11:10 +08:00
hoshi-hiyouga	3365cc8cf0	Merge pull request #3338 from astramind-ai/main Adding Mixture of Depth Former-commit-id: 4da2ece53353b63e672ff529d6beba41ff710c14	2024-04-21 18:05:52 +08:00
hoshi-hiyouga	3a5e68b7d9	fix #3348 Former-commit-id: aa5e921c00f60074eceb2f9d4d8837cc713edba6	2024-04-20 10:34:09 +08:00
hiyouga	0cb596fee1	add dpo mix dataset Former-commit-id: 6def3f8bfa51b2d9d73af112352ce07db972e4c9	2024-04-20 01:31:38 +08:00
hiyouga	b3b5b530d1	fix #3352 Former-commit-id: f315f8e8ec916b82bac94a159e55839ff155c6b5	2024-04-19 22:40:01 +08:00
hiyouga	9225c15c88	fix llama3 template Former-commit-id: 20e95250168fbe081c779b2e1ff23f5df3ce02f7	2024-04-19 15:46:51 +08:00
Marco	abd9fed445	fix small typo Former-commit-id: 5638a03cd0cf8119ff366b3b3e303b5a2351b065	2024-04-18 20:33:29 +02:00
Marco	44cda2eece	Added Mixture of Depths Former-commit-id: 75dd98b9abc847e22cb263c17ebcd2ca5dd98345	2024-04-18 20:31:24 +02:00
hoshi-hiyouga	8397808d1d	support llama3 Former-commit-id: c1eabb751a5fd73b710714451b146732e0ed4558	2024-04-19 01:13:50 +08:00
hiyouga	9e1bd6420d	fix #3324 Former-commit-id: 5e710c4ac331f3400534d33b2646c4108c898d98	2024-04-18 15:34:45 +08:00
hiyouga	619264c854	tiny fix Former-commit-id: 86399ca8c06273c42c2b184664ae25d3405b3bf6	2024-04-18 00:22:17 +08:00
hiyouga	1ebac62e3d	update readme Former-commit-id: a49112a74339ba77bfec53f7870e821fe148db2c	2024-04-17 23:40:49 +08:00
hiyouga	ce9bdb3509	add mixtral 8x22B models Former-commit-id: eccbeecff0909e1fa124b5439ffbbfbc5607e1d6	2024-04-17 23:35:59 +08:00
hiyouga	0c8d6369ac	add CodeQwen models Former-commit-id: 9f6094241391f8f717818c8ba94e11d1791b4a5c	2024-04-17 23:27:22 +08:00
hiyouga	bee796f6b5	fix #3316 Former-commit-id: 7395e9e90a209228ff563ab54319955608850fc3	2024-04-17 22:54:34 +08:00
hiyouga	9f6349a333	fix #3317 Former-commit-id: 7dce1763be4374cf616d96db95ae964ff510a9d6	2024-04-17 22:17:19 +08:00
hiyouga	171a029c5e	lint Former-commit-id: 917d65ce65024d17a5030bc57083a427cfae16d7	2024-04-16 18:21:09 +08:00
hoshi-hiyouga	eaefaa0fe0	Merge pull request #3291 from codemayq/main support for previewing custom dataset in directory format Former-commit-id: 40d89152282101a7c08f53e72c2ad7124a0595f3	2024-04-16 18:12:09 +08:00
hiyouga	d301f0a64b	Update parser.py Former-commit-id: 92c2133896c20054db86dd53508c982e39bd5ca0	2024-04-16 18:09:31 +08:00
hiyouga	0a1578e4e3	update readme and gradio version Former-commit-id: 4029b60ddcbd15b5354503c51178f0f5e7e9aedf	2024-04-16 18:09:16 +08:00
hiyouga	a4167fd925	support badam for all stages Former-commit-id: 7a1380646119bfe6855f73dd90570defcea05281	2024-04-16 17:44:48 +08:00
hoshi-hiyouga	42084e08ae	Merge pull request #3287 from Ledzy/badam [Feature] Add BAdam algorithm Former-commit-id: 10a5e1e65b34b03e5ca2a41bf6ded09a3fb25f0c	2024-04-16 17:32:16 +08:00
hoshi-hiyouga	9d23f5dc89	Update utils.py Former-commit-id: 01147536b2bb507e87e033fa696e9eb39fe96bbe	2024-04-16 17:30:12 +08:00
hoshi-hiyouga	5978427ae0	Update trainer.py Former-commit-id: c6163be1444c00dd000f288e2f834968bd932981	2024-04-16 17:29:52 +08:00
hoshi-hiyouga	c7c216069c	Update utils.py Former-commit-id: 7edf4dbed88b8034282f14fd6e0cb6f7f9e5f805	2024-04-16 17:29:30 +08:00
hoshi-hiyouga	cde9d1b917	Update patcher.py Former-commit-id: 494e6a1e05b38f5ff61d83327303614f53c92e64	2024-04-16 17:29:19 +08:00
hoshi-hiyouga	96213f04b0	Update adapter.py Former-commit-id: 8f7b75b26f020d8ae85baab7b082475c3bfeb512	2024-04-16 17:28:12 +08:00
hoshi-hiyouga	7ecea08b9b	Update parser.py Former-commit-id: 898239883afc79f03abd0dc276eef901662a9591	2024-04-16 17:27:25 +08:00
hoshi-hiyouga	191971865d	Update parser.py Former-commit-id: 2f3da8169d18b026760cc0ac7dd6141bdd08c932	2024-04-16 17:27:02 +08:00
hoshi-hiyouga	ff4f587dd9	Update finetuning_args.py Former-commit-id: 3a23d900aea74078f0bc8cf73fac860a4ce3df67	2024-04-16 17:26:30 +08:00
hoshi-hiyouga	de728d0371	Update sft.sh Former-commit-id: 2b4b1562e91bbb02e345e71b7721da9333c0791b	2024-04-16 17:25:40 +08:00
hoshi-hiyouga	d08e09642d	Update requirements.txt Former-commit-id: 1e45537ca0bb4d49b4147df01122e365b3d617e4	2024-04-16 17:10:17 +08:00
hoshi-hiyouga	351493b183	Update setup.py Former-commit-id: 5df30ea166aff29d48ff83a22ac6ef1611ce3e35	2024-04-16 17:10:02 +08:00
Jonery	86ab47e121	remove badam from core requirements Former-commit-id: fa5898944a3867ac5108dd0d579ca0677c87d3d6	2024-04-16 12:25:50 +08:00
Jonery	6dd6b3e396	resolve gradient checkpointing issue. Former-commit-id: 6df9135d063bb6102f0cbcdf0d702076f5febbae	2024-04-16 12:05:27 +08:00
codingma	5f1418a68b	add check Former-commit-id: 008f6498977c243c80e87242f05c9cf9573541ac	2024-04-16 10:56:39 +08:00
codingma	7b97a79efc	support for previewing custom dataset in directory format Former-commit-id: 501cff38c819f06f15194907ce7e052d5f28025a	2024-04-16 10:43:14 +08:00
hiyouga	ce4f653121	add empty template Former-commit-id: a325ffa8a668bec354d2636683806acef105e196	2024-04-16 03:10:02 +08:00
hiyouga	b053c6454e	update readme Former-commit-id: 8f233745c3aa7a6ef57f275bec80ee731ff76de3	2024-04-16 02:36:54 +08:00
hiyouga	ebf0f4a77c	update readme Former-commit-id: f9a246572c1ec0e4b36bff237c6523ce629b7000	2024-04-16 02:35:36 +08:00
hiyouga	efa808069a	support unsloth 2024.4 Former-commit-id: 14a83f8bc4fe44783252378fce59198194a96bb8	2024-04-16 00:25:03 +08:00
hiyouga	b5c5283dd6	add codegemma Former-commit-id: 9324176525c2eda22962b0ca1895009b6237e6e3	2024-04-16 00:11:15 +08:00
hiyouga	b638c65519	support cohere commandR #3184 Former-commit-id: e077c36872740f6b2ac255aee9da6c4c70f28977	2024-04-15 23:26:42 +08:00
Jonery	d4d471450f	Feature BAdam Former-commit-id: d8d2807fbcf587c37f7fd34a23e9397d2775ceed	2024-04-15 23:15:27 +08:00
hoshi-hiyouga	3144bdec2c	Merge pull request #3254 from marko1616/feature/Add-support-for-CohereForAI/c4ai-command-r-plus Add template&support for c4ai-command-r/plus (tested) Former-commit-id: 41d39ec4889abad050820bf153133ac3a11228a3	2024-04-15 22:59:35 +08:00
hoshi-hiyouga	c6d6c4c209	Update template.py Former-commit-id: 00b8be7dafa65e13b344724a8d3855919ee4f631	2024-04-15 22:58:01 +08:00
hoshi-hiyouga	f5f1589662	Update constants.py Former-commit-id: 39199f712aa7b7a1c66080d9c84651fd2eb0b425	2024-04-15 22:56:55 +08:00
hiyouga	276f2cb24e	update examples Former-commit-id: 369294b31c8a03a1cafcee83eb31a817007d3c49	2024-04-15 22:14:34 +08:00
marko1616	952b785bb3	change default_system accroding to official template Former-commit-id: 7ad9029c5e77a87a7c324b8f90b4f80a31a5c78b	2024-04-15 20:45:46 +08:00
marko1616	72dd676208	Revert "Add support for function call(Not strictly following origin)" This reverts commit `dfaa31e991` [formerly 44f3ada4e394c06b0d972329ed2a62d2be2ea0c6]. Former-commit-id: fac9cc6e01dd8f3bc449b656804476e1871326f0	2024-04-15 20:27:09 +08:00
marko1616	dfaa31e991	Add support for function call(Not strictly following origin) Former-commit-id: 44f3ada4e394c06b0d972329ed2a62d2be2ea0c6	2024-04-15 20:16:52 +08:00
hoshi-hiyouga	86556b1c74	Merge pull request #3261 from khazic/main Added specimens for single-card full parameter prediction Former-commit-id: 60df2a9519fbd8215c3afacc831b0cc89006457a	2024-04-15 16:30:57 +08:00
hoshi-hiyouga	0c80751e87	Merge pull request #3276 from liu-zichen/fix_mixtral fix: turn on output_router_logits of mixtral Former-commit-id: 07bbaf5c67d00a152e5304e81b15fd9189e7bb99	2024-04-15 15:38:16 +08:00
hiyouga	9338f878a3	fix #3273 Former-commit-id: 3b20c89b342a068356ffc29c3724b645775c65db	2024-04-15 15:32:58 +08:00
liuzc	fde3d91242	fix: mixtral output_router_logits Former-commit-id: ab3171ea97ec968b972287287ef9ee2502c6d37c	2024-04-15 12:11:49 +08:00
khazic	19adfb88a9	Upgrade README.md Former-commit-id: 697f768d7185789ee054c94f4f161a65b8a505bc	2024-04-13 20:50:49 +08:00
khazic	daaafa900a	Added specimens for single-card full parameter prediction Former-commit-id: d8d4fb9fa4b0e1950a453682e5e186f34f085dee	2024-04-13 20:45:19 +08:00
marko1616	0dcc9e0bca	Typo fix Former-commit-id: 607625497738b2c8be736be7b0bd5c6f4cbaad5e	2024-04-13 17:30:21 +08:00
marko1616	aeec78b35c	Typo fix Former-commit-id: 51b1e49e288e66c1b0c24ac070201c988fb2a389	2024-04-13 07:52:11 +08:00
marko1616	c991654cb4	Add c4ai-command-r-plus link Former-commit-id: acaf953ca46eca8fb378067f4ada133654e4f088	2024-04-13 07:32:40 +08:00
marko1616	f328413646	Add template&support(Not tested) Former-commit-id: 60bb60c4dc30a9641ddb57a44ef126f0768566c4	2024-04-13 04:31:33 +08:00
hiyouga	106a0104da	fix #3247 Former-commit-id: bb67c66f80627805b585d157ba807c0ce378d3f2	2024-04-12 17:41:33 +08:00
hiyouga	5486ea09e3	fix model card Former-commit-id: 920e7149bf2b559c9829aa4b11cfb6d00bbb2f9e	2024-04-12 17:11:59 +08:00
hiyouga	31bbbb6d13	fix #3238 Former-commit-id: 4d7e81ab4722d13bec6ca1af141f94bdc74d0883	2024-04-12 14:28:11 +08:00
hiyouga	1a77de82fa	set dev version Former-commit-id: f6cc76571d2c789675883a18e0db3d0c61f33808	2024-04-11 20:27:34 +08:00