Update publish.yml

Former-commit-id: 60b0633e29c9e701aa3813bd1fdc0282bd07f7c8

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -38,7 +38,9 @@ body:
         请合理使用 Markdown 标签来格式化您的文本。
 
       placeholder: |
+        ```bash
         llamafactory-cli train ...
+        ```
 
   - type: textarea
     id: expected-behavior

.github/PULL_REQUEST_TEMPLATE.md

@@ -5,3 +5,4 @@ Fixes # (issue)
 ## Before submitting
 
 - [ ] Did you read the [contributor guideline](https://github.com/hiyouga/LLaMA-Factory/blob/main/.github/CONTRIBUTING.md)?
+- [ ] Did you write any new necessary tests?

.github/workflows/label_issue.yml

@@ -0,0 +1,17 @@
+name: label_issue
+
+on:
+  issues:
+    types:
+      - opened
+
+jobs:
+  label_issue:
+    runs-on: ubuntu-latest
+
+    steps:
+      - env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          ISSUE_URL: ${{ github.event.issue.html_url }}
+        run: |
+          gh issue edit $ISSUE_URL --add-label "pending"

.github/workflows/publish.yml

@@ -0,0 +1,40 @@
+name: publish
+
+on:
+  release:
+    types:
+      - published
+
+jobs:
+  publish:
+    name: Upload release to PyPI
+
+    runs-on: ubuntu-latest
+
+    environment:
+      name: release
+      url: https://pypi.org/p/llamafactory
+
+    permissions:
+      id-token: write
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.8"
+      
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build
+      
+      - name: Build package
+        run: |
+          python -m build
+      
+      - name: Publish package
+        uses: pypa/gh-action-pypi-publish@release/v1

.github/workflows/tests.yml

@@ -19,21 +19,27 @@ on:
 jobs:
   tests:
     runs-on: ubuntu-latest
+
     steps:
-      - uses: actions/checkout@v4
+      - name: Checkout
+        uses: actions/checkout@v4
+
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
           python-version: "3.8"
           cache: "pip"
           cache-dependency-path: "setup.py"
+
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
           python -m pip install .[torch,dev]
+
       - name: Check quality
         run: |
           make style && make quality
+
       - name: Test with pytest
         run: |
           make test

Dockerfile

@@ -1,14 +1,47 @@
-FROM nvcr.io/nvidia/pytorch:24.01-py3
+# Use the NVIDIA official image with PyTorch 2.3.0
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
+FROM nvcr.io/nvidia/pytorch:24.02-py3
 
+# Define installation arguments
+ARG INSTALL_BNB=false
+ARG INSTALL_VLLM=false
+ARG INSTALL_DEEPSPEED=false
+ARG PIP_INDEX=https://pypi.org/simple
+
+# Set the working directory
 WORKDIR /app
 
+# Install the requirements
 COPY requirements.txt /app/
-RUN pip install -r requirements.txt
+RUN pip config set global.index-url $PIP_INDEX
+RUN python -m pip install --upgrade pip
+RUN python -m pip install -r requirements.txt
 
+# Copy the rest of the application into the image
 COPY . /app/
-RUN pip install -e .[metrics,bitsandbytes,qwen]
 
+# Install the LLaMA Factory
+RUN EXTRA_PACKAGES="metrics"; \
+    if [ "$INSTALL_BNB" = "true" ]; then \
+        EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
+    fi; \
+    if [ "$INSTALL_VLLM" = "true" ]; then \
+        EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
+    fi; \
+    if [ "$INSTALL_DEEPSPEED" = "true" ]; then \
+        EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
+    fi; \
+    pip install -e .[$EXTRA_PACKAGES] && \
+    pip uninstall -y transformer-engine flash-attn
+
+# Set up volumes
 VOLUME [ "/root/.cache/huggingface/", "/app/data", "/app/output" ]
+
+# Expose port 7860 for the LLaMA Board
 EXPOSE 7860
 
+# Expose port 8000 for the API service
+EXPOSE 8000
+
+# Launch LLaMA Board
 CMD [ "llamafactory-cli", "webui" ]

MANIFEST.in

@@ -0,0 +1 @@
+include LICENSE requirements.txt

Makefile

@@ -11,4 +11,4 @@ style:
 	ruff format $(check_dirs)
 
 test:
-	pytest tests/
+	CUDA_VISIBLE_DEVICES= pytest tests/

README.md

@@ -49,7 +49,7 @@ Choose your path:
 - **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, KTO, ORPO, etc.
 - **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, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning.
+- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA 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.
@@ -71,9 +71,9 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Changelog
 
-[24/06/07] We supported fine-tuning the **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** series models.
+[24/06/16] We support **[PiSSA](https://arxiv.org/abs/2404.02948)** algorithm. See [examples](examples/README.md) for usage.
 
-[24/06/05] We supported fine-tuning the **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** models.
+[24/06/07] We supported fine-tuning the **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** and **[GLM-4](https://github.com/THUDM/GLM-4)** models.
 
 [24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
 
@@ -151,35 +151,35 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 ## Supported Models
 
-| Model                                                    | Model size                       | Template  |
-| -------------------------------------------------------- | -------------------------------- | --------- |
-| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | chatglm3  |
-| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | cohere    |
-| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | deepseek  |
-| [Falcon](https://huggingface.co/tiiuae)                  | 7B/11B/40B/180B                  | falcon    |
-| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | gemma     |
-| [GLM4](https://huggingface.co/THUDM)                     | 9B                               | glm4      |
-| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | intern2   |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | llama2    |
-| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | llama3    |
-| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | vicuna    |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | mistral   |
-| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | -         |
-| [PaliGemma](https://huggingface.co/google)               | 3B                               | gemma     |
-| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | -         |
-| [Phi-3](https://huggingface.co/microsoft)                | 4B/7B/14B                        | phi       |
-| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | qwen      |
-| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen      |
-| [Qwen2 (MoE)](https://huggingface.co/Qwen)               | 0.5B/1.5B/7B/57B/72B             | qwen      |
-| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | -         |
-| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | xverse    |
-| [Yi (1/1.5)](https://huggingface.co/01-ai)               | 6B/9B/34B                        | yi        |
-| [Yi-VL](https://huggingface.co/01-ai)                    | 6B/34B                           | yi_vl     |
-| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | yuan      |
+| Model                                                     | Model size                       | Template  |
+| --------------------------------------------------------- | -------------------------------- | --------- |
+| [Baichuan2](https://huggingface.co/baichuan-inc)          | 7B/13B                           | baichuan2 |
+| [BLOOM](https://huggingface.co/bigscience)                | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
+| [BLOOMZ](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
+| [ChatGLM3](https://huggingface.co/THUDM)                  | 6B                               | chatglm3  |
+| [Command-R](https://huggingface.co/CohereForAI)           | 35B/104B                         | cohere    |
+| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B                  | deepseek  |
+| [Falcon](https://huggingface.co/tiiuae)                   | 7B/11B/40B/180B                  | falcon    |
+| [Gemma/CodeGemma](https://huggingface.co/google)          | 2B/7B                            | gemma     |
+| [GLM4](https://huggingface.co/THUDM)                      | 9B                               | glm4      |
+| [InternLM2](https://huggingface.co/internlm)              | 7B/20B                           | intern2   |
+| [LLaMA](https://github.com/facebookresearch/llama)        | 7B/13B/33B/65B                   | -         |
+| [LLaMA-2](https://huggingface.co/meta-llama)              | 7B/13B/70B                       | llama2    |
+| [LLaMA-3](https://huggingface.co/meta-llama)              | 8B/70B                           | llama3    |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)              | 7B/13B                           | vicuna    |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)       | 7B/8x7B/8x22B                    | mistral   |
+| [OLMo](https://huggingface.co/allenai)                    | 1B/7B                            | -         |
+| [PaliGemma](https://huggingface.co/google)                | 3B                               | gemma     |
+| [Phi-1.5/2](https://huggingface.co/microsoft)             | 1.3B/2.7B                        | -         |
+| [Phi-3](https://huggingface.co/microsoft)                 | 4B/7B/14B                        | phi       |
+| [Qwen](https://huggingface.co/Qwen)                       | 1.8B/7B/14B/72B                  | qwen      |
+| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)         | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen      |
+| [Qwen2 (MoE)](https://huggingface.co/Qwen)                | 0.5B/1.5B/7B/57B/72B             | qwen      |
+| [StarCoder2](https://huggingface.co/bigcode)              | 3B/7B/15B                        | -         |
+| [XVERSE](https://huggingface.co/xverse)                   | 7B/13B/65B                       | xverse    |
+| [Yi (1/1.5)](https://huggingface.co/01-ai)                | 6B/9B/34B                        | yi        |
+| [Yi-VL](https://huggingface.co/01-ai)                     | 6B/34B                           | yi_vl     |
+| [Yuan](https://huggingface.co/IEITYuan)                   | 2B/51B/102B                      | yuan      |
 
 > [!NOTE]
 > 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.
@@ -259,6 +259,9 @@ You also can add a custom chat template to [template.py](src/llamafactory/data/t
 - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
 - [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
 - [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
+- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
+- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
+- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
 - [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)
@@ -335,7 +338,7 @@ huggingface-cli login
 ```bash
 git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
 cd LLaMA-Factory
-pip install -e '.[torch,metrics]'
+pip install -e ".[torch,metrics]"
 ```
 
 Extra dependencies available: torch, torch_npu, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
@@ -405,9 +408,9 @@ Please refer to [data/README.md](data/README.md) for checking the details about
 Use the following 3 commands to run LoRA **fine-tuning**, **inference** and **merging** of the Llama3-8B-Instruct model, respectively.
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 ```
 
 See [examples/README.md](examples/README.md) for advanced usage (including distributed training).
@@ -417,32 +420,38 @@ See [examples/README.md](examples/README.md) for advanced usage (including distr
 
 ### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
 
-#### Use local environment
-
 ```bash
-CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
+llamafactory-cli webui
 ```
 
-</details>
+### Build Docker
 
 #### Use Docker
 
 ```bash
-docker build -f ./Dockerfile -t llama-factory:latest .
-docker run --gpus=all \
+docker build -f ./Dockerfile \
+    --build-arg INSTALL_BNB=false \
+    --build-arg INSTALL_VLLM=false \
+    --build-arg INSTALL_DEEPSPEED=false \
+    --build-arg PIP_INDEX=https://pypi.org/simple \
+    -t llamafactory:latest .
+
+docker run -it --gpus=all \
     -v ./hf_cache:/root/.cache/huggingface/ \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -p 7860:7860 \
+    -p 8000:8000 \
     --shm-size 16G \
-    --name llama_factory \
-    -d llama-factory:latest
+    --name llamafactory \
+    llamafactory:latest
 ```
 
 #### Use Docker Compose
 
 ```bash
-docker compose -f ./docker-compose.yml up -d
+docker-compose up -d
+docker-compose exec llamafactory bash
 ```
 
 <details><summary>Details about volume</summary>
@@ -456,7 +465,7 @@ docker compose -f ./docker-compose.yml up -d
 ### Deploy with OpenAI-style API and vLLM
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
+API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 ```
 
 > [!TIP]
@@ -474,7 +483,7 @@ Train the model by specifying a model ID of the ModelScope Hub as the `model_nam
 
 ### Use W&B Logger
 
-To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments.
+To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.
 
 ```yaml
 report_to: wandb

README_zh.md

@@ -49,7 +49,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - **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、KTO 训练、ORPO 训练等等。
 - **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、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
+- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 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。
@@ -71,9 +71,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 
 ## 更新日志
 
-[24/06/07] 我们支持了 **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** 系列模型的微调。
+[24/06/16] 我们支持了 **[PiSSA](https://arxiv.org/abs/2404.02948)** 算法。详细用法请参照 [examples](examples/README_zh.md)。
 
-[24/06/05] 我们支持了 **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** 模型的微调。
+[24/06/07] 我们支持了 **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** 和 **[GLM-4](https://github.com/THUDM/GLM-4)** 模型的微调。
 
 [24/05/26] 我们支持了 **[SimPO](https://arxiv.org/abs/2405.14734)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
 
@@ -151,35 +151,35 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 
 ## 模型
 
-| 模型名                                                   | 模型大小                          | Template  |
-| -------------------------------------------------------- | -------------------------------- | --------- |
-| [Baichuan2](https://huggingface.co/baichuan-inc)         | 7B/13B                           | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [BLOOMZ](https://huggingface.co/bigscience)              | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
-| [ChatGLM3](https://huggingface.co/THUDM)                 | 6B                               | chatglm3  |
-| [Command-R](https://huggingface.co/CohereForAI)          | 35B/104B                         | cohere    |
-| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai)     | 7B/16B/67B/236B                  | deepseek  |
-| [Falcon](https://huggingface.co/tiiuae)                  | 7B/11B/40B/180B                  | falcon    |
-| [Gemma/CodeGemma](https://huggingface.co/google)         | 2B/7B                            | gemma     |
-| [GLM4](https://huggingface.co/THUDM)                     | 9B                               | glm4      |
-| [InternLM2](https://huggingface.co/internlm)             | 7B/20B                           | intern2   |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B                   | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                       | llama2    |
-| [LLaMA-3](https://huggingface.co/meta-llama)             | 8B/70B                           | llama3    |
-| [LLaVA-1.5](https://huggingface.co/llava-hf)             | 7B/13B                           | vicuna    |
-| [Mistral/Mixtral](https://huggingface.co/mistralai)      | 7B/8x7B/8x22B                    | mistral   |
-| [OLMo](https://huggingface.co/allenai)                   | 1B/7B                            | -         |
-| [PaliGemma](https://huggingface.co/google)               | 3B                               | gemma     |
-| [Phi-1.5/2](https://huggingface.co/microsoft)            | 1.3B/2.7B                        | -         |
-| [Phi-3](https://huggingface.co/microsoft)                | 4B/7B/14B                        | phi       |
-| [Qwen](https://huggingface.co/Qwen)                      | 1.8B/7B/14B/72B                  | qwen      |
-| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)        | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen      |
-| [Qwen2 (MoE)](https://huggingface.co/Qwen)               | 0.5B/1.5B/7B/57B/72B             | qwen      |
-| [StarCoder2](https://huggingface.co/bigcode)             | 3B/7B/15B                        | -         |
-| [XVERSE](https://huggingface.co/xverse)                  | 7B/13B/65B                       | xverse    |
-| [Yi (1/1.5)](https://huggingface.co/01-ai)               | 6B/9B/34B                        | yi        |
-| [Yi-VL](https://huggingface.co/01-ai)                    | 6B/34B                           | yi_vl     |
-| [Yuan](https://huggingface.co/IEITYuan)                  | 2B/51B/102B                      | yuan      |
+| 模型名                                                    | 模型大小                          | Template  |
+| --------------------------------------------------------- | -------------------------------- | --------- |
+| [Baichuan2](https://huggingface.co/baichuan-inc)          | 7B/13B                           | baichuan2 |
+| [BLOOM](https://huggingface.co/bigscience)                | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
+| [BLOOMZ](https://huggingface.co/bigscience)               | 560M/1.1B/1.7B/3B/7.1B/176B      | -         |
+| [ChatGLM3](https://huggingface.co/THUDM)                  | 6B                               | chatglm3  |
+| [Command-R](https://huggingface.co/CohereForAI)           | 35B/104B                         | cohere    |
+| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B                  | deepseek  |
+| [Falcon](https://huggingface.co/tiiuae)                   | 7B/11B/40B/180B                  | falcon    |
+| [Gemma/CodeGemma](https://huggingface.co/google)          | 2B/7B                            | gemma     |
+| [GLM4](https://huggingface.co/THUDM)                      | 9B                               | glm4      |
+| [InternLM2](https://huggingface.co/internlm)              | 7B/20B                           | intern2   |
+| [LLaMA](https://github.com/facebookresearch/llama)        | 7B/13B/33B/65B                   | -         |
+| [LLaMA-2](https://huggingface.co/meta-llama)              | 7B/13B/70B                       | llama2    |
+| [LLaMA-3](https://huggingface.co/meta-llama)              | 8B/70B                           | llama3    |
+| [LLaVA-1.5](https://huggingface.co/llava-hf)              | 7B/13B                           | vicuna    |
+| [Mistral/Mixtral](https://huggingface.co/mistralai)       | 7B/8x7B/8x22B                    | mistral   |
+| [OLMo](https://huggingface.co/allenai)                    | 1B/7B                            | -         |
+| [PaliGemma](https://huggingface.co/google)                | 3B                               | gemma     |
+| [Phi-1.5/2](https://huggingface.co/microsoft)             | 1.3B/2.7B                        | -         |
+| [Phi-3](https://huggingface.co/microsoft)                 | 4B/7B/14B                        | phi       |
+| [Qwen](https://huggingface.co/Qwen)                       | 1.8B/7B/14B/72B                  | qwen      |
+| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen)         | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen      |
+| [Qwen2 (MoE)](https://huggingface.co/Qwen)                | 0.5B/1.5B/7B/57B/72B             | qwen      |
+| [StarCoder2](https://huggingface.co/bigcode)              | 3B/7B/15B                        | -         |
+| [XVERSE](https://huggingface.co/xverse)                   | 7B/13B/65B                       | xverse    |
+| [Yi (1/1.5)](https://huggingface.co/01-ai)                | 6B/9B/34B                        | yi        |
+| [Yi-VL](https://huggingface.co/01-ai)                     | 6B/34B                           | yi_vl     |
+| [Yuan](https://huggingface.co/IEITYuan)                   | 2B/51B/102B                      | yuan      |
 
 > [!NOTE]
 > 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**。
@@ -259,6 +259,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
 - [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
 - [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
+- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
+- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
+- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
 - [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)
@@ -335,7 +338,7 @@ huggingface-cli login
 ```bash
 git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
 cd LLaMA-Factory
-pip install -e '.[torch,metrics]'
+pip install -e ".[torch,metrics]"
 ```
 
 可选的额外依赖项：torch、torch_npu、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
@@ -405,9 +408,9 @@ Docker 镜像：
 下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调**、**推理**和**合并**。
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 ```
 
 高级用法请参考 [examples/README_zh.md](examples/README_zh.md)（包括多 GPU 微调）。
@@ -417,30 +420,38 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_s
 
 ### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
 
-#### 使用本地环境
-
 ```bash
-CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
+llamafactory-cli webui
 ```
 
+### 构建 Docker
+
 #### 使用 Docker
 
 ```bash
-docker build -f ./Dockerfile -t llama-factory:latest .
-docker run --gpus=all \
+docker build -f ./Dockerfile \
+    --build-arg INSTALL_BNB=false \
+    --build-arg INSTALL_VLLM=false \
+    --build-arg INSTALL_DEEPSPEED=false \
+    --build-arg PIP_INDEX=https://pypi.org/simple \
+    -t llamafactory:latest .
+
+docker run -it --gpus=all \
     -v ./hf_cache:/root/.cache/huggingface/ \
     -v ./data:/app/data \
     -v ./output:/app/output \
     -p 7860:7860 \
+    -p 8000:8000 \
     --shm-size 16G \
-    --name llama_factory \
-    -d llama-factory:latest
+    --name llamafactory \
+    llamafactory:latest
 ```
 
 #### 使用 Docker Compose
 
 ```bash
-docker compose -f ./docker-compose.yml up -d
+docker-compose up -d
+docker-compose exec llamafactory bash
 ```
 
 <details><summary>数据卷详情</summary>
@@ -454,7 +465,7 @@ docker compose -f ./docker-compose.yml up -d
 ### 利用 vLLM 部署 OpenAI API
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
+API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
 ```
 
 > [!TIP]
@@ -472,7 +483,7 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
 
 ### 使用 W&B 面板
 
-若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请添加下面的参数。
+若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请在 yaml 文件中添加下面的参数。
 
 ```yaml
 report_to: wandb

docker-compose.yml

@@ -1,18 +1,25 @@
-version: '3.8'
-
 services:
-  llama-factory:
+  llamafactory:
     build:
       dockerfile: Dockerfile
       context: .
-    container_name: llama_factory
+      args:
+        INSTALL_BNB: false
+        INSTALL_VLLM: false
+        INSTALL_DEEPSPEED: false
+        PIP_INDEX: https://pypi.org/simple
+    container_name: llamafactory
     volumes:
       - ./hf_cache:/root/.cache/huggingface/
       - ./data:/app/data
       - ./output:/app/output
     ports:
       - "7860:7860"
+      - "8000:8000"
     ipc: host
+    tty: true
+    stdin_open: true
+    command: bash
     deploy:
       resources:
         reservations:

evaluation/ceval/ceval.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import os
 
 import datasets

evaluation/cmmlu/cmmlu.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import os
 
 import datasets

evaluation/mmlu/mmlu.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 import os
 
 import datasets

examples/README.md

@@ -4,59 +4,59 @@ Make sure to execute these commands in the `LLaMA-Factory` directory.
 
 ## Table of Contents
 
-- [LoRA Fine-Tuning on A Single GPU](#lora-fine-tuning-on-a-single-gpu)
-- [QLoRA Fine-Tuning on a Single GPU](#qlora-fine-tuning-on-a-single-gpu)
-- [LoRA Fine-Tuning on Multiple GPUs](#lora-fine-tuning-on-multiple-gpus)
-- [LoRA Fine-Tuning on Multiple NPUs](#lora-fine-tuning-on-multiple-npus)
-- [Full-Parameter Fine-Tuning on Multiple GPUs](#full-parameter-fine-tuning-on-multiple-gpus)
+- [LoRA Fine-Tuning](#lora-fine-tuning)
+- [QLoRA Fine-Tuning](#qlora-fine-tuning)
+- [Full-Parameter Fine-Tuning](#full-parameter-fine-tuning)
 - [Merging LoRA Adapters and Quantization](#merging-lora-adapters-and-quantization)
 - [Inferring LoRA Fine-Tuned Models](#inferring-lora-fine-tuned-models)
 - [Extras](#extras)
 
+Use `CUDA_VISIBLE_DEVICES` (GPU) or `ASCEND_RT_VISIBLE_DEVICES` (NPU) to choose computing devices.
+
 ## Examples
 
-### LoRA Fine-Tuning on A Single GPU
+### LoRA Fine-Tuning
 
 #### (Continuous) Pre-Training
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml
 ```
 
 #### Supervised Fine-Tuning
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```
 
 #### Multimodal Supervised Fine-Tuning
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llava1_5_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llava1_5_lora_sft.yaml
 ```
 
 #### Reward Modeling
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_reward.yaml
 ```
 
 #### PPO Training
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_ppo.yaml
 ```
 
 #### DPO/ORPO/SimPO Training
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
 ```
 
 #### KTO Training
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml
 ```
 
 #### Preprocess Dataset
@@ -64,95 +64,79 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lo
 It is useful for large dataset, use `tokenized_path` in config to load the preprocessed dataset.
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_preprocess.yaml
+llamafactory-cli train examples/train_lora/llama3_preprocess.yaml
 ```
 
 #### Evaluating on MMLU/CMMLU/C-Eval Benchmarks
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/llama3_lora_eval.yaml
+llamafactory-cli eval examples/train_lora/llama3_lora_eval.yaml
 ```
 
 #### Batch Predicting and Computing BLEU and ROUGE Scores
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_predict.yaml
-```
-
-### QLoRA Fine-Tuning on a Single GPU
-
-#### Supervised Fine-Tuning with 4/8-bit Bitsandbytes Quantization (Recommended)
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
-```
-
-#### Supervised Fine-Tuning with 4/8-bit GPTQ Quantization
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
-```
-
-#### Supervised Fine-Tuning with 4-bit AWQ Quantization
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
-```
-
-#### Supervised Fine-Tuning with 2-bit AQLM Quantization
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
-```
-
-### LoRA Fine-Tuning on Multiple GPUs
-
-#### Supervised Fine-Tuning on Single Node
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
 ```
 
 #### Supervised Fine-Tuning on Multiple Nodes
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```
 
 #### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml
 ```
 
-### LoRA Fine-Tuning on Multiple NPUs
+### QLoRA Fine-Tuning
 
-#### Supervised Fine-Tuning with DeepSpeed ZeRO-0
+#### Supervised Fine-Tuning with 4/8-bit Bitsandbytes Quantization (Recommended)
 
 ```bash
-ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_bitsandbytes.yaml
 ```
 
-### Full-Parameter Fine-Tuning on Multiple GPUs
+#### Supervised Fine-Tuning with 4/8-bit GPTQ Quantization
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_gptq.yaml
+```
+
+#### Supervised Fine-Tuning with 4-bit AWQ Quantization
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_awq.yaml
+```
+
+#### Supervised Fine-Tuning with 2-bit AQLM Quantization
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_aqlm.yaml
+```
+
+### Full-Parameter Fine-Tuning
 
 #### Supervised Fine-Tuning on Single Node
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
 ```
 
 #### Supervised Fine-Tuning on Multiple Nodes
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
 ```
 
 #### Batch Predicting and Computing BLEU and ROUGE Scores
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_predict.yaml
+llamafactory-cli train examples/train_full/llama3_full_predict.yaml
 ```
 
 ### Merging LoRA Adapters and Quantization
@@ -162,35 +146,33 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llam
 Note: DO NOT use quantized model or `quantization_bit` when merging LoRA adapters.
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
+llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 ```
 
 #### Quantizing Model using AutoGPTQ
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
+llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
 ```
 
 ### Inferring LoRA Fine-Tuned Models
 
-Use `CUDA_VISIBLE_DEVICES=0,1` to infer models on multiple devices.
-
 #### Use CLI
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
 ```
 
 #### Use Web UI
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
 ```
 
 #### Launch OpenAI-style API
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.yaml
+llamafactory-cli api examples/inference/llama3_lora_sft.yaml
 ```
 
 ### Extras
@@ -198,36 +180,42 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.y
 #### Full-Parameter Fine-Tuning using GaLore
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
 ```
 
 #### Full-Parameter Fine-Tuning using BAdam
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
 ```
 
 #### LoRA+ Fine-Tuning
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
+llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
+```
+
+#### PiSSA Fine-Tuning
+
+```bash
+llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml
 ```
 
 #### Mixture-of-Depths Fine-Tuning
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
 ```
 
 #### LLaMA-Pro Fine-Tuning
 
 ```bash
 bash examples/extras/llama_pro/expand.sh
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
+llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```
 
 #### FSDP+QLoRA Fine-Tuning
 
 ```bash
-bash examples/extras/fsdp_qlora/single_node.sh
+bash examples/extras/fsdp_qlora/train.sh
 ```

examples/README_zh.md

@@ -4,59 +4,59 @@
 
 ## 目录
 
-- [单 GPU LoRA 微调](#单-gpu-lora-微调)
-- [单 GPU QLoRA 微调](#单-gpu-qlora-微调)
-- [多 GPU LoRA 微调](#多-gpu-lora-微调)
-- [多 NPU LoRA 微调](#多-npu-lora-微调)
-- [多 GPU 全参数微调](#多-gpu-全参数微调)
+- [LoRA 微调](#lora-微调)
+- [QLoRA 微调](#qlora-微调)
+- [全参数微调](#全参数微调)
 - [合并 LoRA 适配器与模型量化](#合并-lora-适配器与模型量化)
 - [推理 LoRA 模型](#推理-lora-模型)
 - [杂项](#杂项)
 
+使用 `CUDA_VISIBLE_DEVICES`（GPU）或 `ASCEND_RT_VISIBLE_DEVICES`（NPU）选择计算设备。
+
 ## 示例
 
-### 单 GPU LoRA 微调
+### LoRA 微调
 
 #### （增量）预训练
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml
 ```
 
 #### 指令监督微调
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```
 
 #### 多模态指令监督微调
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llava1_5_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llava1_5_lora_sft.yaml
 ```
 
 #### 奖励模型训练
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_reward.yaml
 ```
 
 #### PPO 训练
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_ppo.yaml
 ```
 
 #### DPO/ORPO/SimPO 训练
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
 ```
 
 #### KTO 训练
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml
 ```
 
 #### 预处理数据集
@@ -64,95 +64,79 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lo
 对于大数据集有帮助，在配置中使用 `tokenized_path` 以加载预处理后的数据集。
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_preprocess.yaml
+llamafactory-cli train examples/train_lora/llama3_preprocess.yaml
 ```
 
 #### 在 MMLU/CMMLU/C-Eval 上评估
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/llama3_lora_eval.yaml
+llamafactory-cli eval examples/train_lora/llama3_lora_eval.yaml
 ```
 
 #### 批量预测并计算 BLEU 和 ROUGE 分数
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_predict.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
 ```
 
-### 单 GPU QLoRA 微调
-
-#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调（推荐）
+#### 多机指令监督微调
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
-```
-
-#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
-```
-
-#### 基于 4 比特 AWQ 量化进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
-```
-
-#### 基于 2 比特 AQLM 量化进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
-```
-
-### 多 GPU LoRA 微调
-
-#### 在单机上进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
-```
-
-#### 在多机上进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```
 
 #### 使用 DeepSpeed ZeRO-3 平均分配显存
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml
 ```
 
-### 多 NPU LoRA 微调
+### QLoRA 微调
 
-#### 使用 DeepSpeed ZeRO-0 进行指令监督微调
+#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调（推荐）
 
 ```bash
-ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_bitsandbytes.yaml
 ```
 
-### 多 GPU 全参数微调
+#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_gptq.yaml
+```
+
+#### 基于 4 比特 AWQ 量化进行指令监督微调
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_awq.yaml
+```
+
+#### 基于 2 比特 AQLM 量化进行指令监督微调
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_aqlm.yaml
+```
+
+### 全参数微调
 
 #### 在单机上进行指令监督微调
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
 ```
 
 #### 在多机上进行指令监督微调
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
 ```
 
 #### 批量预测并计算 BLEU 和 ROUGE 分数
 
 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_predict.yaml
+llamafactory-cli train examples/train_full/llama3_full_predict.yaml
 ```
 
 ### 合并 LoRA 适配器与模型量化
@@ -162,35 +146,33 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llam
 注：请勿使用量化后的模型或 `quantization_bit` 参数来合并 LoRA 适配器。
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
+llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 ```
 
 #### 使用 AutoGPTQ 量化模型
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
+llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
 ```
 
 ### 推理 LoRA 模型
 
-使用 `CUDA_VISIBLE_DEVICES=0,1` 进行多卡推理。
-
 #### 使用命令行接口
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
 ```
 
 #### 使用浏览器界面
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
 ```
 
 #### 启动 OpenAI 风格 API
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.yaml
+llamafactory-cli api examples/inference/llama3_lora_sft.yaml
 ```
 
 ### 杂项
@@ -198,36 +180,42 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.y
 #### 使用 GaLore 进行全参数训练
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
 ```
 
 #### 使用 BAdam 进行全参数训练
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
 ```
 
 #### LoRA+ 微调
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
+llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
+```
+
+#### PiSSA 微调
+
+```bash
+llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml
 ```
 
 #### 深度混合微调
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
 ```
 
 #### LLaMA-Pro 微调
 
 ```bash
 bash examples/extras/llama_pro/expand.sh
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
+llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```
 
 #### FSDP+QLoRA 微调
 
 ```bash
-bash examples/extras/fsdp_qlora/single_node.sh
+bash examples/extras/fsdp_qlora/train.sh
 ```

examples/extras/fsdp_qlora/llama3_lora_sft.yaml

@@ -8,9 +8,6 @@ do_train: true
 finetuning_type: lora
 lora_target: all
 
-### ddp
-ddp_timeout: 180000000
-
 ### dataset
 dataset: identity,alpaca_en_demo
 template: llama3
@@ -34,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/extras/llama_pro/llama3_freeze_sft.yaml

@@ -32,6 +32,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/extras/loraplus/llama3_lora_sft.yaml

@@ -31,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/extras/mod/llama3_full_sft.yaml

@@ -31,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 pure_bf16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/extras/pissa/llama3_lora_sft.yaml

@@ -6,9 +6,9 @@ stage: sft
 do_train: true
 finetuning_type: lora
 lora_target: all
-
-### ddp
-ddp_timeout: 180000000
+pissa_init: true
+pissa_iter: 4
+pissa_convert: true
 
 ### dataset
 dataset: identity,alpaca_en_demo
@@ -27,12 +27,13 @@ overwrite_output_dir: true
 
 ### train
 per_device_train_batch_size: 1
-gradient_accumulation_steps: 2
+gradient_accumulation_steps: 8
 learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_full/llama3_full_sft_ds3.yaml

@@ -5,9 +5,6 @@ model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
 stage: sft
 do_train: true
 finetuning_type: full
-
-### ddp
-ddp_timeout: 180000000
 deepspeed: examples/deepspeed/ds_z3_config.json
 
 ### dataset
@@ -33,6 +30,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llama3_lora_dpo.yaml

@@ -32,6 +32,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llama3_lora_kto.yaml

@@ -6,6 +6,7 @@ stage: kto
 do_train: true
 finetuning_type: lora
 lora_target: all
+pref_beta: 0.1
 
 ### dataset
 dataset: kto_en_demo
@@ -30,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llama3_lora_ppo.yaml

@@ -31,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### generate
 max_new_tokens: 512

examples/train_lora/llama3_lora_predict.yaml

@@ -22,3 +22,4 @@ overwrite_output_dir: true
 ### eval
 per_device_eval_batch_size: 1
 predict_with_generate: true
+ddp_timeout: 180000000

examples/train_lora/llama3_lora_pretrain.yaml

@@ -29,6 +29,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llama3_lora_reward.yaml

@@ -30,6 +30,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llama3_lora_sft.yaml

@@ -30,6 +30,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llama3_lora_sft_ds0.yaml

@@ -6,9 +6,6 @@ stage: sft
 do_train: true
 finetuning_type: lora
 lora_target: all
-
-### ddp
-ddp_timeout: 180000000
 deepspeed: examples/deepspeed/ds_z0_config.json
 
 ### dataset
@@ -34,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llama3_lora_sft_ds3.yaml

@@ -6,9 +6,6 @@ stage: sft
 do_train: true
 finetuning_type: lora
 lora_target: all
-
-### ddp
-ddp_timeout: 180000000
 deepspeed: examples/deepspeed/ds_z3_config.json
 
 ### dataset
@@ -34,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_lora/llava1_5_lora_sft.yaml

@@ -31,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_qlora/llama3_lora_sft_aqlm.yaml

@@ -30,6 +30,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_qlora/llama3_lora_sft_awq.yaml

@@ -30,6 +30,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_qlora/llama3_lora_sft_bitsandbytes.yaml

@@ -31,6 +31,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

examples/train_qlora/llama3_lora_sft_gptq.yaml

@@ -30,6 +30,7 @@ num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
 fp16: true
+ddp_timeout: 180000000
 
 ### eval
 val_size: 0.1

requirements.txt

@@ -4,6 +4,7 @@ accelerate>=0.30.1
 peft>=0.11.1
 trl>=0.8.6
 gradio>=4.0.0
+pandas>=2.0.0
 scipy
 einops
 sentencepiece

scripts/cal_flops.py

@@ -1,7 +1,20 @@
 # coding=utf-8
-# Calculates the flops of pre-trained models.
-# Usage: python cal_flops.py --model_name_or_path path_to_model --batch_size 1 --seq_length 512
-# Inspired by: https://www.deepspeed.ai/tutorials/flops-profiler/
+# Copyright 2024 Microsoft Corporation and the LlamaFactory team.
+#
+# This code is inspired by the Microsoft's DeepSpeed library.
+# https://www.deepspeed.ai/tutorials/flops-profiler/
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import fire
 import torch
@@ -17,6 +30,10 @@ def calculate_flops(
     seq_length: int = 256,
     flash_attn: str = "auto",
 ):
+    r"""
+    Calculates the flops of pre-trained models.
+    Usage: python cal_flops.py --model_name_or_path path_to_model --batch_size 1 --seq_length 512
+    """
     with get_accelerator().device(0):
         chat_model = ChatModel(dict(model_name_or_path=model_name_or_path, template="empty", flash_attn=flash_attn))
         fake_input = torch.ones((batch_size, seq_length), dtype=torch.long, device=chat_model.model.device)

scripts/cal_lr.py

@@ -1,7 +1,20 @@
 # coding=utf-8
-# Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
-# Usage: python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en --cutoff_len 1024 --batch_size 16
-# Inspired by: https://github.com/imoneoi/openchat/blob/master/ochat/training_deepspeed/train.py
+# Copyright 2024 imoneoi and the LlamaFactory team.
+#
+# This code is inspired by the imoneoi's OpenChat library.
+# https://github.com/imoneoi/openchat/blob/3.6.0/ochat/training_deepspeed/train.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import math
 from typing import Literal
@@ -32,6 +45,10 @@ def calculate_lr(
     cutoff_len: int = 1024,  # i.e. maximum input length during training
     is_mistral: bool = False,  # mistral model uses a smaller learning rate,
 ):
+    r"""
+    Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
+    Usage: python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en --cutoff_len 1024 --batch_size 16
+    """
     model_args, data_args, training_args, _, _ = get_train_args(
         dict(
             stage=stage,

scripts/cal_ppl.py

@@ -1,6 +1,17 @@
 # coding=utf-8
-# Calculates the ppl on the dataset of the pre-trained models.
-# Usage: python cal_ppl.py --model_name_or_path path_to_model --save_name ppl.json
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import json
 from dataclasses import dataclass
@@ -56,6 +67,10 @@ def cal_ppl(
     max_samples: Optional[int] = None,
     train_on_prompt: bool = False,
 ):
+    r"""
+    Calculates the ppl on the dataset of the pre-trained models.
+    Usage: python cal_ppl.py --model_name_or_path path_to_model --save_name ppl.json
+    """
     model_args, data_args, training_args, finetuning_args, _ = get_train_args(
         dict(
             stage=stage,

scripts/length_cdf.py

@@ -1,6 +1,17 @@
 # coding=utf-8
-# Calculates the distribution of the input lengths in the dataset.
-# Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en --template default
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from collections import defaultdict
 
@@ -19,6 +30,10 @@ def length_cdf(
     template: str = "default",
     interval: int = 1000,
 ):
+    r"""
+    Calculates the distribution of the input lengths in the dataset.
+    Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en --template default
+    """
     model_args, data_args, training_args, _, _ = get_train_args(
         dict(
             stage="sft",

scripts/llama_pro.py

@@ -1,7 +1,20 @@
 # coding=utf-8
-# Performs block expansion for LLaMA, Mistral, Qwen1.5 or Yi models.
-# Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
-# Inspired by: https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
+# Copyright 2024 Tencent Inc. and the LlamaFactory team.
+#
+# This code is inspired by the Tencent's LLaMA-Pro library.
+# https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import json
 import os
@@ -37,6 +50,10 @@ def block_expansion(
     shard_size: Optional[str] = "2GB",
     save_safetensors: Optional[bool] = False,
 ):
+    r"""
+    Performs block expansion for LLaMA, Mistral, Qwen1.5 or Yi models.
+    Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
+    """
     config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path)
     num_layers = getattr(config, "num_hidden_layers")
     setattr(config, "num_hidden_layers", num_layers + num_expand)
@@ -103,7 +120,7 @@ def block_expansion(
             json.dump(index, f, indent=2, sort_keys=True)
         print("Model weights saved in {}".format(output_dir))
 
-    print("Fine-tune this model with:")
+    print("- Fine-tune this model with:")
     print("model_name_or_path: {}".format(output_dir))
     print("finetuning_type: freeze")
     print("freeze_trainable_layers: {}".format(num_expand))

scripts/llamafy_baichuan2.py

@@ -1,8 +1,17 @@
 # coding=utf-8
-# Converts the Baichuan2-7B model in the same format as LLaMA2-7B.
-# Usage: python llamafy_baichuan2.py --input_dir input --output_dir output
-# Inspired by: https://huggingface.co/fireballoon/baichuan-llama-7b/blob/main/convert_baichuan_to_llama.py
-# Converted model: https://huggingface.co/hiyouga/Baichuan2-7B-Base-LLaMAfied
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import json
 import os
@@ -79,6 +88,11 @@ def save_config(input_dir: str, output_dir: str):
 def llamafy_baichuan2(
     input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
 ):
+    r"""
+    Converts the Baichuan2-7B model in the same format as LLaMA2-7B.
+    Usage: python llamafy_baichuan2.py --input_dir input --output_dir output
+    Converted model: https://huggingface.co/hiyouga/Baichuan2-7B-Base-LLaMAfied
+    """
     try:
         os.makedirs(output_dir, exist_ok=False)
     except Exception as e:

scripts/llamafy_qwen.py

@@ -1,7 +1,17 @@
 # coding=utf-8
-# Converts the Qwen models in the same format as LLaMA2.
-# Usage: python llamafy_qwen.py --input_dir input --output_dir output
-# Converted model: https://huggingface.co/hiyouga/Qwen-14B-Chat-LLaMAfied
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import json
 import os
@@ -131,6 +141,11 @@ def save_config(input_dir: str, output_dir: str, torch_dtype: str):
 def llamafy_qwen(
     input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
 ):
+    r"""
+    Converts the Qwen models in the same format as LLaMA2.
+    Usage: python llamafy_qwen.py --input_dir input --output_dir output
+    Converted model: https://huggingface.co/hiyouga/Qwen-14B-Chat-LLaMAfied
+    """
     try:
         os.makedirs(output_dir, exist_ok=False)
     except Exception as e:

scripts/loftq_init.py

@@ -1,14 +1,25 @@
 # coding=utf-8
-# Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
-# Usage: python loftq_init.py --model_name_or_path path_to_model --save_dir output_dir
-# Inspired by: https://github.com/huggingface/peft/blob/main/examples/loftq_finetuning/quantize_save_load.py
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is based on the HuggingFace's PEFT library.
+# https://github.com/huggingface/peft/blob/v0.10.0/examples/loftq_finetuning/quantize_save_load.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 import os
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import fire
-import torch
-import torch.nn as nn
 from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -17,38 +28,21 @@ if TYPE_CHECKING:
     from transformers import PreTrainedModel
 
 
-class Shell(nn.Module):
-    def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
-        super().__init__()
-        self.weight = nn.Parameter(weight, requires_grad=False)
-        if bias is not None:
-            self.bias = nn.Parameter(bias, requires_grad=False)
-
-
-def unwrap_model(model: nn.Module, pattern=".base_layer") -> None:
-    for name in {k.split(pattern)[0] for k, _ in model.named_modules() if pattern in k}:
-        parent_name = ".".join(name.split(".")[:-1])
-        child_name = name.split(".")[-1]
-        parent_module = model.get_submodule(parent_name)
-        child_module = getattr(parent_module, child_name)
-        base_layer = getattr(child_module, "base_layer")
-        weight = getattr(base_layer, "weight", None)
-        bias = getattr(base_layer, "bias", None)
-        setattr(parent_module, child_name, Shell(weight, bias))
-
-    print("Model unwrapped.")
-
-
 def quantize_loftq(
     model_name_or_path: str,
-    save_dir: str,
-    loftq_bits: Optional[int] = 4,
-    loftq_iter: Optional[int] = 1,
-    lora_alpha: Optional[int] = None,
-    lora_rank: Optional[int] = 16,
-    lora_target: Optional[str] = "q_proj,v_proj",
-    save_safetensors: Optional[bool] = False,
+    output_dir: str,
+    loftq_bits: int = 4,
+    loftq_iter: int = 4,
+    lora_alpha: int = None,
+    lora_rank: int = 16,
+    lora_dropout: float = 0,
+    lora_target: str = "q_proj,v_proj",
+    save_safetensors: bool = True,
 ):
+    r"""
+    Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
+    Usage: python loftq_init.py --model_name_or_path path_to_model --output_dir output_dir
+    """
     tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
     loftq_config = LoftQConfig(loftq_bits=loftq_bits, loftq_iter=loftq_iter)
@@ -57,25 +51,34 @@ def quantize_loftq(
         inference_mode=True,
         r=lora_rank,
         lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
-        lora_dropout=0.1,
+        lora_dropout=lora_dropout,
         target_modules=[name.strip() for name in lora_target.split(",")],
         init_lora_weights="loftq",
         loftq_config=loftq_config,
     )
 
     # Init LoftQ model
-    lora_model = get_peft_model(model, lora_config)
-    base_model: "PreTrainedModel" = lora_model.get_base_model()
+    print("Initializing LoftQ weights, it may be take several minutes, wait patiently.")
+    peft_model = get_peft_model(model, lora_config)
+    loftq_dir = os.path.join(output_dir, "loftq_init")
 
     # Save LoftQ model
-    setattr(lora_model.base_model.peft_config["default"], "base_model_name_or_path", save_dir)
-    setattr(lora_model.base_model.peft_config["default"], "init_lora_weights", True)
-    lora_model.save_pretrained(os.path.join(save_dir, "adapters"), safe_serialization=save_safetensors)
+    setattr(peft_model.peft_config["default"], "base_model_name_or_path", output_dir)
+    setattr(peft_model.peft_config["default"], "init_lora_weights", True)  # don't apply loftq again
+    peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
+    print("Adapter weights saved in {}".format(loftq_dir))
 
     # Save base model
-    unwrap_model(base_model)
-    base_model.save_pretrained(save_dir, safe_serialization=save_safetensors)
-    tokenizer.save_pretrained(save_dir)
+    base_model: "PreTrainedModel" = peft_model.unload()
+    base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
+    tokenizer.save_pretrained(output_dir)
+    print("Model weights saved in {}".format(output_dir))
+
+    print("- Fine-tune this model with:")
+    print("model_name_or_path: {}".format(output_dir))
+    print("adapter_name_or_path: {}".format(loftq_dir))
+    print("finetuning_type: lora")
+    print("quantization_bit: {}".format(loftq_bits))
 
 
 if __name__ == "__main__":

scripts/pissa_init.py

@@ -0,0 +1,82 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is based on the HuggingFace's PEFT library.
+# https://github.com/huggingface/peft/blob/v0.11.0/examples/pissa_finetuning/preprocess.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import TYPE_CHECKING
+
+import fire
+from peft import LoraConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedModel
+
+
+def quantize_pissa(
+    model_name_or_path: str,
+    output_dir: str,
+    pissa_iter: int = 4,
+    lora_alpha: int = None,
+    lora_rank: int = 16,
+    lora_dropout: float = 0,
+    lora_target: str = "q_proj,v_proj",
+    save_safetensors: bool = True,
+):
+    r"""
+    Initializes LoRA weights with Principal Singular values and Singular vectors Adaptation (PiSSA)
+    Usage: python pissa_init.py --model_name_or_path path_to_model --output_dir output_dir
+    """
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
+    lora_config = LoraConfig(
+        task_type=TaskType.CAUSAL_LM,
+        r=lora_rank,
+        lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
+        lora_dropout=lora_dropout,
+        target_modules=[name.strip() for name in lora_target.split(",")],
+        init_lora_weights="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter),
+    )
+
+    # Init PiSSA model
+    peft_model = get_peft_model(model, lora_config)
+    pissa_dir = os.path.join(output_dir, "pissa_init")
+
+    # Save PiSSA model
+    setattr(peft_model.peft_config["default"], "init_lora_weights", True)  # don't apply pissa again
+    peft_model.save_pretrained(pissa_dir, safe_serialization=save_safetensors)
+    print("Adapter weights saved in {}".format(pissa_dir))
+
+    # Save base model
+    base_model: "PreTrainedModel" = peft_model.unload()
+    base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
+    tokenizer.save_pretrained(output_dir)
+    print("Model weights saved in {}".format(output_dir))
+
+    print("- Fine-tune this model with:")
+    print("model_name_or_path: {}".format(output_dir))
+    print("adapter_name_or_path: {}".format(pissa_dir))
+    print("finetuning_type: lora")
+    print("pissa_init: false")
+    print("pissa_convert: true")
+    print("- and optionally with:")
+    print("quantization_bit: 4")
+
+
+if __name__ == "__main__":
+    fire.Fire(quantize_pissa)

scripts/test_toolcall.py

@@ -1,3 +1,18 @@
+# coding=utf-8
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import os
 from typing import Sequence

setup.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 import re
 
@@ -23,7 +37,7 @@ extra_require = {
     "torch": ["torch>=1.13.1"],
     "torch-npu": ["torch==2.1.0", "torch-npu==2.1.0.post3", "decorator"],
     "metrics": ["nltk", "jieba", "rouge-chinese"],
-    "deepspeed": ["deepspeed>=0.10.0,<=0.14.0"],
+    "deepspeed": ["deepspeed>=0.10.0"],
     "bitsandbytes": ["bitsandbytes>=0.39.0"],
     "vllm": ["vllm>=0.4.3"],
     "galore": ["galore-torch"],

src/api.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 
 import uvicorn

src/llamafactory/__init__.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 # Level: api, webui > chat, eval, train > data, model > hparams > extras
 
 from .cli import VERSION

src/llamafactory/api/app.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 from contextlib import asynccontextmanager
 from typing import Optional

src/llamafactory/api/chat.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import base64
 import io
 import json
@@ -78,9 +92,11 @@ def _process_request(
             raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
 
         if message.role == Role.ASSISTANT and isinstance(message.tool_calls, list) and len(message.tool_calls):
-            name = message.tool_calls[0].function.name
-            arguments = message.tool_calls[0].function.arguments
-            content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
+            tool_calls = [
+                {"name": tool_call.function.name, "argument": tool_call.function.arguments}
+                for tool_call in message.tool_calls
+            ]
+            content = json.dumps(tool_calls, ensure_ascii=False)
             input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
         elif isinstance(message.content, list):
             for input_item in message.content:
@@ -104,7 +120,7 @@ def _process_request(
     if isinstance(tool_list, list) and len(tool_list):
         try:
             tools = json.dumps([dictify(tool.function) for tool in tool_list], ensure_ascii=False)
-        except Exception:
+        except json.JSONDecodeError:
             raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
     else:
         tools = None
@@ -146,15 +162,17 @@ async def create_chat_completion_response(
     choices = []
     for i, response in enumerate(responses):
         if tools:
-            result = chat_model.engine.template.format_tools.extract(response.response_text)
+            result = chat_model.engine.template.extract_tool(response.response_text)
         else:
             result = response.response_text
 
-        if isinstance(result, tuple):
-            name, arguments = result
-            function = Function(name=name, arguments=arguments)
-            tool_call = FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function)
-            response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=[tool_call])
+        if isinstance(result, list):
+            tool_calls = []
+            for tool in result:
+                function = Function(name=tool[0], arguments=tool[1])
+                tool_calls.append(FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function))
+
+            response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=tool_calls)
             finish_reason = Finish.TOOL
         else:
             response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)

src/llamafactory/api/common.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 from typing import TYPE_CHECKING, Any, Dict
 

src/llamafactory/api/protocol.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import time
 from enum import Enum, unique
 from typing import Any, Dict, List, Optional, Union

src/llamafactory/chat/__init__.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from .base_engine import BaseEngine
 from .chat_model import ChatModel
 

src/llamafactory/chat/base_engine.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Optional, Sequence, Union
@@ -36,11 +50,6 @@ class BaseEngine(ABC):
         generating_args: "GeneratingArguments",
     ) -> None: ...
 
-    @abstractmethod
-    async def start(
-        self,
-    ) -> None: ...
-
     @abstractmethod
     async def chat(
         self,

src/llamafactory/chat/chat_model.py

@@ -1,3 +1,20 @@
+# Copyright 2024 THUDM and the LlamaFactory team.
+#
+# This code is inspired by the THUDM's ChatGLM implementation.
+# https://github.com/THUDM/ChatGLM-6B/blob/main/cli_demo.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import asyncio
 from threading import Thread
 from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, Generator, List, Optional, Sequence
@@ -14,7 +31,7 @@ if TYPE_CHECKING:
     from .base_engine import BaseEngine, Response
 
 
-def _start_background_loop(loop: asyncio.AbstractEventLoop) -> None:
+def _start_background_loop(loop: "asyncio.AbstractEventLoop") -> None:
     asyncio.set_event_loop(loop)
     loop.run_forever()
 
@@ -32,7 +49,6 @@ class ChatModel:
         self._loop = asyncio.new_event_loop()
         self._thread = Thread(target=_start_background_loop, args=(self._loop,), daemon=True)
         self._thread.start()
-        asyncio.run_coroutine_threadsafe(self.engine.start(), self._loop)
 
     def chat(
         self,

src/llamafactory/chat/hf_engine.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import asyncio
 import concurrent.futures
 import os
@@ -45,6 +59,14 @@ class HuggingfaceEngine(BaseEngine):
             self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
         )  # must after fixing tokenizer to resize vocab
         self.generating_args = generating_args.to_dict()
+        try:
+            asyncio.get_event_loop()
+        except RuntimeError:
+            logger.warning("There is no current event loop, creating a new one.")
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+
+        self.semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", "1")))
 
     @staticmethod
     def _process_args(
@@ -245,9 +267,6 @@ class HuggingfaceEngine(BaseEngine):
 
         return scores
 
-    async def start(self) -> None:
-        self._semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", 1)))
-
     async def chat(
         self,
         messages: Sequence[Dict[str, str]],
@@ -272,7 +291,7 @@ class HuggingfaceEngine(BaseEngine):
             image,
             input_kwargs,
         )
-        async with self._semaphore:
+        async with self.semaphore:
             with concurrent.futures.ThreadPoolExecutor() as pool:
                 return await loop.run_in_executor(pool, self._chat, *input_args)
 
@@ -300,7 +319,7 @@ class HuggingfaceEngine(BaseEngine):
             image,
             input_kwargs,
         )
-        async with self._semaphore:
+        async with self.semaphore:
             with concurrent.futures.ThreadPoolExecutor() as pool:
                 stream = self._stream_chat(*input_args)
                 while True:
@@ -319,6 +338,6 @@ class HuggingfaceEngine(BaseEngine):
 
         loop = asyncio.get_running_loop()
         input_args = (self.model, self.tokenizer, batch_input, input_kwargs)
-        async with self._semaphore:
+        async with self.semaphore:
             with concurrent.futures.ThreadPoolExecutor() as pool:
                 return await loop.run_in_executor(pool, self._get_scores, *input_args)

src/llamafactory/chat/vllm_engine.py

@@ -1,10 +1,24 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import uuid
 from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence, Union
 
 from ..data import get_template_and_fix_tokenizer
 from ..extras.logging import get_logger
 from ..extras.misc import get_device_count
-from ..extras.packages import is_vllm_available
+from ..extras.packages import is_vllm_available, is_vllm_version_greater_than_0_5
 from ..model import load_config, load_tokenizer
 from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
 from .base_engine import BaseEngine, Response
@@ -13,7 +27,11 @@ 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 is_vllm_version_greater_than_0_5():
+        from vllm.multimodal.image import ImagePixelData
+    else:
+        from vllm.sequence import MultiModalData
 
 
 if TYPE_CHECKING:
@@ -48,7 +66,7 @@ class VllmEngine(BaseEngine):
             "model": model_args.model_name_or_path,
             "trust_remote_code": True,
             "download_dir": model_args.cache_dir,
-            "dtype": model_args.vllm_dtype,
+            "dtype": model_args.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,
@@ -106,7 +124,10 @@ class VllmEngine(BaseEngine):
         if self.processor is not None and image is not None:  # add image features
             image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
             pixel_values = image_processor(image, return_tensors="pt")["pixel_values"]
-            multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
+            if is_vllm_version_greater_than_0_5():
+                multi_modal_data = ImagePixelData(image=pixel_values)
+            else:  # TODO: remove vllm 0.4.3 support
+                multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
         else:
             multi_modal_data = None
 
@@ -162,9 +183,6 @@ class VllmEngine(BaseEngine):
         )
         return result_generator
 
-    async def start(self) -> None:
-        pass
-
     async def chat(
         self,
         messages: Sequence[Dict[str, str]],

src/llamafactory/cli.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 import random
 import subprocess

src/llamafactory/data/__init__.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from .collator import KTODataCollatorWithPadding, PairwiseDataCollatorWithPadding
 from .data_utils import Role, split_dataset
 from .loader import get_dataset

src/llamafactory/data/aligner.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 from functools import partial
 from typing import TYPE_CHECKING, Any, Dict, List, Union
@@ -10,6 +24,7 @@ from .data_utils import Role
 
 if TYPE_CHECKING:
     from datasets import Dataset, IterableDataset
+    from transformers import Seq2SeqTrainingArguments
 
     from ..hparams import DataArguments
     from .parser import DatasetAttr
@@ -175,7 +190,10 @@ def convert_sharegpt(
 
 
 def align_dataset(
-    dataset: Union["Dataset", "IterableDataset"], dataset_attr: "DatasetAttr", data_args: "DataArguments"
+    dataset: Union["Dataset", "IterableDataset"],
+    dataset_attr: "DatasetAttr",
+    data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
 ) -> Union["Dataset", "IterableDataset"]:
     r"""
     Aligned dataset:
@@ -208,7 +226,7 @@ def align_dataset(
     if not data_args.streaming:
         kwargs = dict(
             num_proc=data_args.preprocessing_num_workers,
-            load_from_cache_file=(not data_args.overwrite_cache),
+            load_from_cache_file=(not data_args.overwrite_cache) or (training_args.local_process_index != 0),
             desc="Converting format of dataset",
         )
 

src/llamafactory/data/collator.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from dataclasses import dataclass
 from typing import Any, Dict, Sequence
 

src/llamafactory/data/data_utils.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from enum import Enum, unique
 from typing import TYPE_CHECKING, Dict, List, Tuple, Union
 

src/llamafactory/data/formatter.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import re
 from abc import ABC, abstractmethod
@@ -8,21 +22,23 @@ from typing import Any, Dict, List, Literal, Optional, Sequence, Set, Tuple, Uni
 SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
 
 
-JSON_FORMAT_PROMPT = (
-    """, in a JSON format representing the kwargs (e.g. ```{"input": "hello world", "num_beams": 5}```)"""
-)
-
-
-TOOL_SYSTEM_PROMPT = (
+DEFAULT_TOOL_PROMPT = (
     "You have access to the following tools:\n{tool_text}"
     "Use the following format if using a tool:\n"
     "```\n"
     "Action: tool name (one of [{tool_names}]).\n"
-    "Action Input: the input to the tool{format_prompt}.\n"
+    "Action Input: the input to the tool, in a JSON format representing the kwargs "
+    """(e.g. ```{{"input": "hello world", "num_beams": 5}}```).\n"""
     "```\n"
 )
 
 
+GLM4_TOOL_PROMPT = (
+    "你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，"
+    "你的任务是针对用户的问题和要求提供适当的答复和支持。{tool_text}"
+)
+
+
 def default_tool_formatter(tools: List[Dict[str, Any]]) -> str:
     tool_text = ""
     tool_names = []
@@ -48,36 +64,60 @@ def default_tool_formatter(tools: List[Dict[str, Any]]) -> str:
         )
         tool_names.append(tool["name"])
 
-    return TOOL_SYSTEM_PROMPT.format(
-        tool_text=tool_text, tool_names=", ".join(tool_names), format_prompt=JSON_FORMAT_PROMPT
-    )
+    return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names))
 
 
-def default_tool_extractor(content: str) -> Union[str, Tuple[str, str]]:
-    regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+).*?Action Input:\s*(.*)", re.DOTALL)
-    action_match = re.search(regex, content)
+def default_tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]:
+    regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL)
+    action_match: List[Tuple[str, str]] = re.findall(regex, content)
     if not action_match:
         return content
 
-    tool_name = action_match.group(1).strip()
-    tool_input = action_match.group(2).strip().strip('"').strip("```")
+    results = []
+    for match in action_match:
+        tool_name = match[0].strip()
+        tool_input = match[1].strip().strip('"').strip("```")
+        try:
+            arguments = json.loads(tool_input)
+            results.append((tool_name, json.dumps(arguments, ensure_ascii=False)))
+        except json.JSONDecodeError:
+            return content
+
+    return results
+
+
+def glm4_tool_formatter(tools: List[Dict[str, Any]]) -> str:
+    tool_text = ""
+    for tool in tools:
+        tool_text += "\n\n## {name}\n\n{body}\n在调用上述函数时，请使用 Json 格式表示调用的参数。".format(
+            name=tool["name"], body=json.dumps(tool, indent=4, ensure_ascii=False)
+        )
+
+    return GLM4_TOOL_PROMPT.format(tool_text=tool_text)
+
+
+def glm4_tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]:
+    if "\n" not in content:
+        return content
+
+    tool_name, tool_input = content.split("\n", maxsplit=1)
     try:
         arguments = json.loads(tool_input)
     except json.JSONDecodeError:
         return content
 
-    return tool_name, json.dumps(arguments, ensure_ascii=False)
+    return [(tool_name, json.dumps(arguments, ensure_ascii=False))]
 
 
 @dataclass
 class Formatter(ABC):
     slots: SLOTS = field(default_factory=list)
-    tool_format: Optional[Literal["default"]] = None
+    tool_format: Optional[Literal["default", "glm4"]] = None
 
     @abstractmethod
     def apply(self, **kwargs) -> SLOTS: ...
 
-    def extract(self, content: str) -> Union[str, Tuple[str, str]]:
+    def extract(self, content: str) -> Union[str, List[Tuple[str, str]]]:
         raise NotImplementedError
 
 
@@ -140,22 +180,28 @@ class FunctionFormatter(Formatter):
 
     def apply(self, **kwargs) -> SLOTS:
         content = kwargs.pop("content")
+        functions: List[Tuple[str, str]] = []
         try:
-            function = json.loads(content)
-            name = function["name"]
-            arguments = json.dumps(function["arguments"], ensure_ascii=False)
-        except Exception:
-            name, arguments = "", ""
+            tool_calls = json.loads(content)
+            if not isinstance(tool_calls, list):  # parallel function call
+                tool_calls = [tool_calls]
+
+            for tool_call in tool_calls:
+                functions.append((tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False)))
+
+        except json.JSONDecodeError:
+            functions = []
 
         elements = []
-        for slot in self.slots:
-            if isinstance(slot, str):
-                slot = slot.replace("{{name}}", name).replace("{{arguments}}", arguments)
-                elements.append(slot)
-            elif isinstance(slot, (dict, set)):
-                elements.append(slot)
-            else:
-                raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
+        for name, arguments in functions:
+            for slot in self.slots:
+                if isinstance(slot, str):
+                    slot = slot.replace("{{name}}", name).replace("{{arguments}}", arguments)
+                    elements.append(slot)
+                elif isinstance(slot, (dict, set)):
+                    elements.append(slot)
+                else:
+                    raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
 
         return elements
 
@@ -163,25 +209,22 @@ class FunctionFormatter(Formatter):
 @dataclass
 class ToolFormatter(Formatter):
     def __post_init__(self):
-        if self.tool_format is None:
+        if self.tool_format == "default":
+            self._tool_formatter = default_tool_formatter
+            self._tool_extractor = default_tool_extractor
+        elif self.tool_format == "glm4":
+            self._tool_formatter = glm4_tool_formatter
+            self._tool_extractor = glm4_tool_extractor
+        else:
             raise ValueError("Tool format was not found.")
 
     def apply(self, **kwargs) -> SLOTS:
         content = kwargs.pop("content")
         try:
             tools = json.loads(content)
-            if not len(tools):
-                return [""]
-
-            if self.tool_format == "default":
-                return [default_tool_formatter(tools)]
-            else:
-                raise NotImplementedError
-        except Exception:
+            return [self._tool_formatter(tools) if len(tools) != 0 else ""]
+        except json.JSONDecodeError:
             return [""]
 
-    def extract(self, content: str) -> Union[str, Tuple[str, str]]:
-        if self.tool_format == "default":
-            return default_tool_extractor(content)
-        else:
-            raise NotImplementedError
+    def extract(self, content: str) -> Union[str, List[Tuple[str, str]]]:
+        return self._tool_extractor(content)

src/llamafactory/data/loader.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import inspect
 import os
 import sys
@@ -18,8 +32,7 @@ from .template import get_template_and_fix_tokenizer
 
 if TYPE_CHECKING:
     from datasets import Dataset, IterableDataset
-    from transformers import ProcessorMixin, Seq2SeqTrainingArguments
-    from transformers.tokenization_utils import PreTrainedTokenizer
+    from transformers import PreTrainedTokenizer, ProcessorMixin, Seq2SeqTrainingArguments
 
     from ..hparams import DataArguments, ModelArguments
     from .parser import DatasetAttr
@@ -32,6 +45,7 @@ def load_single_dataset(
     dataset_attr: "DatasetAttr",
     model_args: "ModelArguments",
     data_args: "DataArguments",
+    training_args: "Seq2SeqTrainingArguments",
 ) -> Union["Dataset", "IterableDataset"]:
     logger.info("Loading dataset {}...".format(dataset_attr))
     data_path, data_name, data_dir, data_files = None, None, None, None
@@ -123,7 +137,7 @@ def load_single_dataset(
         max_samples = min(data_args.max_samples, len(dataset))
         dataset = dataset.select(range(max_samples))
 
-    return align_dataset(dataset, dataset_attr, data_args)
+    return align_dataset(dataset, dataset_attr, data_args, training_args)
 
 
 def get_dataset(
@@ -157,7 +171,8 @@ def get_dataset(
             if (stage == "rm" and dataset_attr.ranking is False) or (stage != "rm" and dataset_attr.ranking is True):
                 raise ValueError("The dataset is not applicable in the current training stage.")
 
-            all_datasets.append(load_single_dataset(dataset_attr, model_args, data_args))
+            all_datasets.append(load_single_dataset(dataset_attr, model_args, data_args, training_args))
+
         dataset = merge_dataset(all_datasets, data_args, training_args)
 
     with training_args.main_process_first(desc="pre-process dataset"):
@@ -169,7 +184,7 @@ def get_dataset(
         if not data_args.streaming:
             kwargs = dict(
                 num_proc=data_args.preprocessing_num_workers,
-                load_from_cache_file=(not data_args.overwrite_cache),
+                load_from_cache_file=(not data_args.overwrite_cache) or (training_args.local_process_index != 0),
                 desc="Running tokenizer on dataset",
             )
 

src/llamafactory/data/parser.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import os
 from dataclasses import dataclass

src/llamafactory/data/preprocess.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from functools import partial
 from typing import TYPE_CHECKING, Callable, Literal, Optional, Tuple
 
@@ -13,8 +27,7 @@ from .processors.unsupervised import preprocess_unsupervised_dataset, print_unsu
 
 
 if TYPE_CHECKING:
-    from transformers import ProcessorMixin, Seq2SeqTrainingArguments
-    from transformers.tokenization_utils import PreTrainedTokenizer
+    from transformers import PreTrainedTokenizer, ProcessorMixin, Seq2SeqTrainingArguments
 
     from ..hparams import DataArguments
     from .template import Template

src/llamafactory/data/processors/feedback.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 
 from ...extras.constants import IGNORE_INDEX
@@ -6,8 +20,7 @@ from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
 
 
 if TYPE_CHECKING:
-    from transformers import ProcessorMixin
-    from transformers.tokenization_utils import PreTrainedTokenizer
+    from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
     from ..template import Template

src/llamafactory/data/processors/pairwise.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 
 from ...extras.constants import IGNORE_INDEX
@@ -6,8 +20,7 @@ from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
 
 
 if TYPE_CHECKING:
-    from transformers import ProcessorMixin
-    from transformers.tokenization_utils import PreTrainedTokenizer
+    from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
     from ..template import Template

src/llamafactory/data/processors/pretrain.py

@@ -1,9 +1,26 @@
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from itertools import chain
 from typing import TYPE_CHECKING, Any, Dict, List
 
 
 if TYPE_CHECKING:
-    from transformers.tokenization_utils import PreTrainedTokenizer
+    from transformers import PreTrainedTokenizer
 
     from ...hparams import DataArguments
 
@@ -12,7 +29,8 @@ def preprocess_pretrain_dataset(
     examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
 ) -> Dict[str, List[List[int]]]:
     # build grouped texts with format `X1 X2 X3 ...` if packing is enabled
-    text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
+    eos_token = "<|end_of_text|>" if data_args.template == "llama3" else tokenizer.eos_token
+    text_examples = [messages[0]["content"] + eos_token for messages in examples["prompt"]]
 
     if not data_args.packing:
         if data_args.template == "gemma":

src/llamafactory/data/processors/processor_utils.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import bisect
 from typing import TYPE_CHECKING, List, Sequence
 

src/llamafactory/data/processors/supervised.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from collections import defaultdict
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 
@@ -7,8 +21,7 @@ from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, gre
 
 
 if TYPE_CHECKING:
-    from transformers import ProcessorMixin
-    from transformers.tokenization_utils import PreTrainedTokenizer
+    from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
     from ..template import Template

src/llamafactory/data/processors/unsupervised.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
 
 from ...extras.logging import get_logger
@@ -6,8 +20,7 @@ from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
 
 
 if TYPE_CHECKING:
-    from transformers import ProcessorMixin
-    from transformers.tokenization_utils import PreTrainedTokenizer
+    from transformers import PreTrainedTokenizer, ProcessorMixin
 
     from ...hparams import DataArguments
     from ..template import Template

src/llamafactory/data/template.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
 
@@ -24,12 +38,12 @@ class Template:
     format_observation: "Formatter"
     format_tools: "Formatter"
     format_separator: "Formatter"
+    format_prefix: "Formatter"
     default_system: str
     stop_words: List[str]
     image_token: str
     efficient_eos: bool
     replace_eos: bool
-    force_system: bool
 
     def encode_oneturn(
         self,
@@ -65,6 +79,12 @@ class Template:
         """
         return self._encode(tokenizer, messages, system, tools, cutoff_len, reserved_label_len)
 
+    def extract_tool(self, content: str) -> Union[str, List[Tuple[str, str]]]:
+        r"""
+        Extracts tool message.
+        """
+        return self.format_tools.extract(content)
+
     def _encode(
         self,
         tokenizer: "PreTrainedTokenizer",
@@ -83,10 +103,15 @@ class Template:
         encoded_messages = []
         for i, message in enumerate(messages):
             elements = []
-            if i == 0 and (system or tools or self.force_system):
+
+            if i == 0:
+                elements += self.format_prefix.apply()
+
+            if i == 0 and (system or tools):
                 tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
                 elements += self.format_system.apply(content=(system + tool_text))
-            elif i > 0 and i % 2 == 0:
+
+            if i > 0 and i % 2 == 0:
                 elements += self.format_separator.apply()
 
             if message["role"] == Role.USER.value:
@@ -173,11 +198,16 @@ class Llama2Template(Template):
         encoded_messages = []
         for i, message in enumerate(messages):
             elements = []
+
+            if i == 0:
+                elements += self.format_prefix.apply()
+
             system_text = ""
-            if i == 0 and (system or tools or self.force_system):
+            if i == 0 and (system or tools):
                 tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
                 system_text = self.format_system.apply(content=(system + tool_text))[0]
-            elif i > 0 and i % 2 == 0:
+
+            if i > 0 and i % 2 == 0:
                 elements += self.format_separator.apply()
 
             if message["role"] == Role.USER.value:
@@ -208,12 +238,12 @@ def _register_template(
     format_observation: Optional["Formatter"] = None,
     format_tools: Optional["Formatter"] = None,
     format_separator: Optional["Formatter"] = None,
+    format_prefix: Optional["Formatter"] = None,
     default_system: str = "",
     stop_words: List[str] = [],
     image_token: str = "<image>",
     efficient_eos: bool = False,
     replace_eos: bool = False,
-    force_system: bool = False,
 ) -> None:
     r"""
     Registers a chat template.
@@ -245,9 +275,12 @@ def _register_template(
     template_class = Llama2Template if name.startswith("llama2") else Template
     default_user_formatter = StringFormatter(slots=["{{content}}"])
     default_assistant_formatter = StringFormatter(slots=["{{content}}"] + eos_slots)
-    default_function_formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}"] + eos_slots)
+    default_function_formatter = FunctionFormatter(
+        slots=["Action: {{name}}\nAction Input: {{arguments}}\n"] + eos_slots
+    )
     default_tool_formatter = ToolFormatter(tool_format="default")
     default_separator_formatter = EmptyFormatter()
+    default_prefix_formatter = EmptyFormatter()
     TEMPLATES[name] = template_class(
         format_user=format_user or default_user_formatter,
         format_assistant=format_assistant or default_assistant_formatter,
@@ -256,12 +289,12 @@ def _register_template(
         format_observation=format_observation or format_user or default_user_formatter,
         format_tools=format_tools or default_tool_formatter,
         format_separator=format_separator or default_separator_formatter,
+        format_prefix=format_prefix or default_prefix_formatter,
         default_system=default_system,
         stop_words=stop_words,
         image_token=image_token,
         efficient_eos=efficient_eos,
         replace_eos=replace_eos,
-        force_system=force_system,
     )
 
 
@@ -307,6 +340,10 @@ def _convert_slots_to_jinja(slots: "SLOTS", tokenizer: "PreTrainedTokenizer", pl
 def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer") -> str:
     jinja_template = ""
 
+    prefix = _convert_slots_to_jinja(template.format_prefix.apply(), tokenizer)
+    if prefix:
+        jinja_template += "{{ " + prefix + " }}"
+
     if template.default_system:
         jinja_template += "{% set system_message = '" + _jinja_escape(template.default_system) + "' %}"
 
@@ -315,11 +352,7 @@ def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer")
     )
 
     system_message = _convert_slots_to_jinja(template.format_system.apply(), tokenizer, placeholder="system_message")
-    if isinstance(template, Llama2Template):
-        pass
-    elif template.force_system:
-        jinja_template += "{{ " + system_message + " }}"
-    else:
+    if not isinstance(template, Llama2Template):
         jinja_template += "{% if system_message is defined %}{{ " + system_message + " }}{% endif %}"
 
     jinja_template += "{% for message in messages %}"
@@ -435,9 +468,8 @@ _register_template(
 _register_template(
     name="belle",
     format_user=StringFormatter(slots=["Human: {{content}}\n\nBelle: "]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
     format_separator=EmptyFormatter(slots=["\n\n"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
 )
 
 
@@ -450,11 +482,7 @@ _register_template(
 _register_template(
     name="breeze",
     format_user=StringFormatter(slots=["[INST] {{content}} [/INST] "]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
-    default_system=(
-        "You are a helpful AI assistant built by MediaTek Research. "
-        "The user you are helping speaks Traditional Chinese and comes from Taiwan."
-    ),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     efficient_eos=True,
 )
 
@@ -462,10 +490,9 @@ _register_template(
 _register_template(
     name="chatglm2",
     format_user=StringFormatter(slots=["[Round {{idx}}]\n\n问：{{content}}\n\n答："]),
-    format_system=StringFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}, "{{content}}"]),
     format_separator=EmptyFormatter(slots=["\n\n"]),
+    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
     efficient_eos=True,
-    force_system=True,
 )
 
 
@@ -473,14 +500,14 @@ _register_template(
     name="chatglm3",
     format_user=StringFormatter(slots=[{"token": "<|user|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]),
     format_assistant=StringFormatter(slots=["\n", "{{content}}"]),
-    format_system=StringFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}, "{{content}}"]),
+    format_system=StringFormatter(slots=[{"token": "<|system|>"}, "\n", "{{content}}"]),
     format_function=FunctionFormatter(slots=["{{name}}\n{{arguments}}"]),
     format_observation=StringFormatter(
         slots=[{"token": "<|observation|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]
     ),
+    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
     stop_words=["<|user|>", "<|observation|>"],
     efficient_eos=True,
-    force_system=True,
 )
 
 
@@ -488,13 +515,12 @@ _register_template(
     name="chatglm3_system",
     format_user=StringFormatter(slots=[{"token": "<|user|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]),
     format_assistant=StringFormatter(slots=["\n", "{{content}}"]),
-    format_system=StringFormatter(
-        slots=[{"token": "[gMASK]"}, {"token": "sop"}, {"token": "<|system|>"}, "\n", "{{content}}"]
-    ),
+    format_system=StringFormatter(slots=[{"token": "<|system|>"}, "\n", "{{content}}"]),
     format_function=FunctionFormatter(slots=["{{name}}\n{{arguments}}"]),
     format_observation=StringFormatter(
         slots=[{"token": "<|observation|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]
     ),
+    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
     default_system=(
         "You are ChatGLM3, a large language model trained by Zhipu.AI. "
         "Follow the user's instructions carefully. Respond using markdown."
@@ -529,8 +555,7 @@ _register_template(
 
 _register_template(
     name="codegeex2",
-    format_system=StringFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}, "{{content}}"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
 )
 
 
@@ -544,21 +569,15 @@ _register_template(
             )
         ]
     ),
-    format_system=StringFormatter(
-        slots=[{"bos_token"}, "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"]
-    ),
-    default_system=(
-        "You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users "
-        "by providing thorough responses. You are trained by Cohere."
-    ),
+    format_system=StringFormatter(slots=["<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
 )
 
 
 _register_template(
     name="cpm",
     format_user=StringFormatter(slots=["<用户>{{content}}<AI>"]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
 )
 
 
@@ -591,8 +610,7 @@ _register_template(
 _register_template(
     name="deepseek",
     format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
 )
 
 
@@ -622,11 +640,8 @@ _register_template(
 
 _register_template(
     name="empty",
-    format_user=StringFormatter(slots=["{{content}}"]),
-    format_assistant=StringFormatter(slots=["{{content}}"]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     efficient_eos=True,
-    force_system=True,
 )
 
 
@@ -648,13 +663,12 @@ _register_template(
 _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"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     efficient_eos=True,
-    force_system=True,
 )
 
 
@@ -662,36 +676,33 @@ _register_template(
     name="glm4",
     format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
     format_assistant=StringFormatter(slots=["\n{{content}}"]),
-    format_system=StringFormatter(slots=["[gMASK]<sop>{{content}}"]),
+    format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
     format_function=FunctionFormatter(slots=["{{name}}\n{{arguments}}"]),
     format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
+    format_tools=ToolFormatter(tool_format="glm4"),
+    format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
     stop_words=["<|user|>", "<|observation|>"],
     efficient_eos=True,
-    force_system=True,
 )
 
 
 _register_template(
     name="intern",
-    format_user=StringFormatter(slots=["<|User|>:{{content}}", {"token": "<eoh>"}, "\n<|Bot|>:"]),
-    format_separator=EmptyFormatter(slots=[{"token": "<eoa>"}, "\n"]),
+    format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
+    format_system=StringFormatter(slots=["<|System|>:{{content}}\n"]),
+    format_separator=EmptyFormatter(slots=["<eoa>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     stop_words=["<eoa>"],
-    efficient_eos=True,
+    efficient_eos=True,  # internlm tokenizer cannot set eos_token_id
 )
 
 
 _register_template(
     name="intern2",
     format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "<|im_start|>system\n{{content}}<|im_end|>\n"]),
-    format_separator=EmptyFormatter(slots=["\n"]),
-    default_system=(
-        "You are an AI assistant whose name is InternLM (书生·浦语).\n"
-        "- InternLM (书生·浦语) is a conversational language model that is developed "
-        "by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
-        "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen "
-        "by the user such as English and 中文."
-    ),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_separator=EmptyFormatter(slots=["<|im_end|>\n"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     stop_words=["<|im_end|>"],
     efficient_eos=True,  # internlm2 tokenizer cannot set eos_token_id
 )
@@ -700,7 +711,6 @@ _register_template(
 _register_template(
     name="llama2",
     format_user=StringFormatter(slots=[{"bos_token"}, "[INST] {{content}} [/INST]"]),
-    format_assistant=StringFormatter(slots=[" {{content}} ", {"eos_token"}]),
     format_system=StringFormatter(slots=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
 )
 
@@ -723,9 +733,7 @@ _register_template(
             )
         ]
     ),
-    format_system=StringFormatter(
-        slots=[{"bos_token"}, "<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]
-    ),
+    format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
     format_observation=StringFormatter(
         slots=[
             (
@@ -734,7 +742,7 @@ _register_template(
             )
         ]
     ),
-    default_system="You are a helpful assistant.",
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     stop_words=["<|eot_id|>"],
     replace_eos=True,
 )
@@ -743,24 +751,21 @@ _register_template(
 _register_template(
     name="mistral",
     format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
 )
 
 
 _register_template(
     name="olmo",
     format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>\n"]),
-    format_system=StringFormatter(slots=[{"eos_token"}, "{{content}}"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"eos_token"}]),
 )
 
 
 _register_template(
     name="openchat",
     format_user=StringFormatter(slots=["GPT4 Correct User: {{content}}", {"eos_token"}, "GPT4 Correct Assistant:"]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
 )
 
 
@@ -774,27 +779,25 @@ _register_template(
             )
         ]
     ),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     stop_words=["<|eot_id|>"],
     replace_eos=True,
-    force_system=True,
 )
 
 
 _register_template(
     name="orion",
     format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: ", {"eos_token"}]),
-    format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
-    force_system=True,
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
 )
 
 
 _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_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
     format_separator=EmptyFormatter(slots=["\n"]),
-    default_system="You are a helpful AI assistant.",
+    format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
     stop_words=["<|end|>"],
     replace_eos=True,
 )
@@ -827,7 +830,6 @@ _register_template(
     format_separator=EmptyFormatter(slots=["\n"]),
     stop_words=["<|end|>"],
     replace_eos=True,
-    force_system=True,
 )
 
 

src/llamafactory/eval/evaluator.py

@@ -1,4 +1,41 @@
-# Inspired by: https://github.com/hendrycks/test/blob/master/evaluate_flan.py
+# Copyright 2024 the LlamaFactory team.
+#
+# This code is inspired by the Dan's test library.
+# https://github.com/hendrycks/test/blob/master/evaluate_flan.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# MIT License
+#
+# Copyright (c) 2020 Dan Hendrycks
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
 
 import inspect
 import json
@@ -26,9 +63,7 @@ class Evaluator:
         self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args.template)
         self.model = load_model(self.tokenizer, self.model_args, finetuning_args)
         self.eval_template = get_eval_template(self.eval_args.lang)
-        self.choice_inputs = [
-            self.tokenizer.encode(self.eval_template.prefix + ch, add_special_tokens=False)[-1] for ch in CHOICES
-        ]
+        self.choice_inputs = [self.tokenizer.encode(ch, add_special_tokens=False)[-1] for ch in CHOICES]
 
     @torch.inference_mode()
     def batch_inference(self, batch_input: Dict[str, torch.Tensor]) -> List[str]:

src/llamafactory/eval/template.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from dataclasses import dataclass
 from typing import Dict, List, Sequence, Tuple
 
@@ -10,7 +24,6 @@ class EvalTemplate:
     system: str
     choice: str
     answer: str
-    prefix: str
 
     def _parse_example(self, example: Dict[str, str]) -> Tuple[str, str]:
         r"""
@@ -42,8 +55,8 @@ class EvalTemplate:
 eval_templates: Dict[str, "EvalTemplate"] = {}
 
 
-def _register_eval_template(name: str, system: str, choice: str, answer: str, prefix: str) -> None:
-    eval_templates[name] = EvalTemplate(system=system, choice=choice, answer=answer, prefix=prefix)
+def _register_eval_template(name: str, system: str, choice: str, answer: str) -> None:
+    eval_templates[name] = EvalTemplate(system=system, choice=choice, answer=answer)
 
 
 def get_eval_template(name: str) -> "EvalTemplate":
@@ -56,8 +69,7 @@ _register_eval_template(
     name="en",
     system="The following are multiple choice questions (with answers) about {subject}.\n\n",
     choice="\n{choice}. {content}",
-    answer="\nAnswer: ",
-    prefix=" ",
+    answer="\nAnswer:",
 )
 
 
@@ -66,5 +78,4 @@ _register_eval_template(
     system="以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n",
     choice="\n{choice}. {content}",
     answer="\n答案：",
-    prefix=" ",
 )

src/llamafactory/extras/callbacks.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import logging
 import os

src/llamafactory/extras/constants.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from collections import OrderedDict, defaultdict
 from enum import Enum
 from typing import Dict, Optional
@@ -389,6 +403,18 @@ register_model_group(
             DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Chat",
             DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Chat",
         },
+        "DeepSeek-MoE-Coder-16B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
+        },
+        "DeepSeek-MoE-Coder-236B-Base": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Base",
+        },
+        "DeepSeek-MoE-Coder-16B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
+        },
+        "DeepSeek-MoE-Coder-236B-Chat": {
+            DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Instruct",
+        },
     },
     template="deepseek",
 )
@@ -668,6 +694,21 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "MiniCPM-2B-SFT-Chat": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-2B-sft-bf16",
+            DownloadSource.MODELSCOPE: "OpenBMB/miniCPM-bf16",
+        },
+        "MiniCPM-2B-DPO-Chat": {
+            DownloadSource.DEFAULT: "openbmb/MiniCPM-2B-dpo-bf16",
+            DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-2B-dpo-bf16",
+        },
+    },
+    template="cpm",
+)
+
+
 register_model_group(
     models={
         "Mistral-7B-v0.1": {

src/llamafactory/extras/env.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import platform
 
 import accelerate
@@ -6,13 +20,10 @@ import peft
 import torch
 import transformers
 import trl
-from transformers.integrations import is_deepspeed_available
-from transformers.utils import is_bitsandbytes_available, is_torch_cuda_available, is_torch_npu_available
-
-from .packages import is_vllm_available
+from transformers.utils import is_torch_cuda_available, is_torch_npu_available
 
 
-VERSION = "0.8.0"
+VERSION = "0.8.2"
 
 
 def print_env() -> None:
@@ -37,19 +48,25 @@ def print_env() -> None:
         info["NPU type"] = torch.npu.get_device_name()
         info["CANN version"] = torch.version.cann
 
-    if is_deepspeed_available():
+    try:
         import deepspeed  # type: ignore
 
         info["DeepSpeed version"] = deepspeed.__version__
+    except Exception:
+        pass
 
-    if is_bitsandbytes_available():
+    try:
         import bitsandbytes
 
         info["Bitsandbytes version"] = bitsandbytes.__version__
+    except Exception:
+        pass
 
-    if is_vllm_available():
+    try:
         import vllm
 
         info["vLLM version"] = vllm.__version__
+    except Exception:
+        pass
 
     print("\n" + "\n".join(["- {}: {}".format(key, value) for key, value in info.items()]) + "\n")

src/llamafactory/extras/logging.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import logging
 import os
 import sys

src/llamafactory/extras/misc.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import gc
 import os
 from typing import TYPE_CHECKING, Dict, Tuple
@@ -8,6 +22,7 @@ from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList, PreTr
 from transformers.utils import (
     SAFE_WEIGHTS_NAME,
     WEIGHTS_NAME,
+    is_safetensors_available,
     is_torch_bf16_gpu_available,
     is_torch_cuda_available,
     is_torch_mps_available,
@@ -20,6 +35,11 @@ from .constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
 from .logging import get_logger
 
 
+if is_safetensors_available():
+    from safetensors import safe_open
+    from safetensors.torch import save_file
+
+
 _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
 try:
     _is_bf16_available = is_torch_bf16_gpu_available()
@@ -114,9 +134,6 @@ def fix_valuehead_checkpoint(
         return
 
     if safe_serialization:
-        from safetensors import safe_open
-        from safetensors.torch import save_file
-
         path_to_checkpoint = os.path.join(output_dir, SAFE_WEIGHTS_NAME)
         with safe_open(path_to_checkpoint, framework="pt", device="cpu") as f:
             state_dict: Dict[str, torch.Tensor] = {key: f.get_tensor(key) for key in f.keys()}

src/llamafactory/extras/packages.py

@@ -1,5 +1,23 @@
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/import_utils.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import importlib.metadata
 import importlib.util
+from functools import lru_cache
 from typing import TYPE_CHECKING
 
 from packaging import version
@@ -24,10 +42,6 @@ def is_fastapi_available():
     return _is_package_available("fastapi")
 
 
-def is_flash_attn2_available():
-    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")
 
@@ -36,18 +50,10 @@ def is_gradio_available():
     return _is_package_available("gradio")
 
 
-def is_jieba_available():
-    return _is_package_available("jieba")
-
-
 def is_matplotlib_available():
     return _is_package_available("matplotlib")
 
 
-def is_nltk_available():
-    return _is_package_available("nltk")
-
-
 def is_pillow_available():
     return _is_package_available("PIL")
 
@@ -60,10 +66,6 @@ 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")
 
@@ -74,3 +76,8 @@ def is_uvicorn_available():
 
 def is_vllm_available():
     return _is_package_available("vllm")
+
+
+@lru_cache
+def is_vllm_version_greater_than_0_5():
+    return _get_package_version("vllm") >= version.parse("0.5.0")

src/llamafactory/extras/ploting.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import json
 import math
 import os

src/llamafactory/hparams/__init__.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from .data_args import DataArguments
 from .evaluation_args import EvaluationArguments
 from .finetuning_args import FinetuningArguments

src/llamafactory/hparams/data_args.py

@@ -1,3 +1,20 @@
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from dataclasses import dataclass, field
 from typing import Literal, Optional
 

src/llamafactory/hparams/evaluation_args.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import os
 from dataclasses import dataclass, field
 from typing import Literal, Optional

src/llamafactory/hparams/finetuning_args.py

@@ -1,5 +1,19 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from dataclasses import dataclass, field
-from typing import Literal, Optional
+from typing import List, Literal, Optional
 
 
 @dataclass
@@ -94,6 +108,18 @@ class LoraArguments:
         default=False,
         metadata={"help": "Whether or not to use the weight-decomposed lora method (DoRA)."},
     )
+    pissa_init: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to initialize a PiSSA adapter."},
+    )
+    pissa_iter: int = field(
+        default=4,
+        metadata={"help": "The number of iteration steps performed by FSVD in PiSSA. Use -1 to disable it."},
+    )
+    pissa_convert: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to convert the PiSSA adapter to a normal LoRA adapter."},
+    )
     create_new_adapter: bool = field(
         default=False,
         metadata={"help": "Whether or not to create a new adapter with randomly initialized weight."},
@@ -319,20 +345,19 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
                 return [item.strip() for item in arg.split(",")]
             return arg
 
-        self.freeze_trainable_modules = split_arg(self.freeze_trainable_modules)
-        self.freeze_extra_modules = split_arg(self.freeze_extra_modules)
-        self.lora_alpha = self.lora_alpha or self.lora_rank * 2
-        self.lora_target = split_arg(self.lora_target)
-        self.additional_target = split_arg(self.additional_target)
-        self.galore_target = split_arg(self.galore_target)
+        self.freeze_trainable_modules: List[str] = split_arg(self.freeze_trainable_modules)
+        self.freeze_extra_modules: Optional[List[str]] = split_arg(self.freeze_extra_modules)
+        self.lora_alpha: int = self.lora_alpha or self.lora_rank * 2
+        self.lora_target: List[str] = split_arg(self.lora_target)
+        self.additional_target: Optional[List[str]] = split_arg(self.additional_target)
+        self.galore_target: List[str] = split_arg(self.galore_target)
         self.freeze_vision_tower = self.freeze_vision_tower or self.train_mm_proj_only
+        self.use_ref_model = self.stage == "dpo" and self.pref_loss not in ["orpo", "simpo"]
 
         assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
         assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
         assert self.reward_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
 
-        self.use_ref_model = self.pref_loss not in ["orpo", "simpo"]
-
         if self.stage == "ppo" and self.reward_model is None:
             raise ValueError("`reward_model` is necessary for PPO training.")
 
@@ -354,5 +379,11 @@ class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments, GaloreA
         if self.loraplus_lr_ratio is not None and self.finetuning_type != "lora":
             raise ValueError("`loraplus_lr_ratio` is only valid for LoRA training.")
 
+        if self.pissa_convert and self.finetuning_type != "lora":
+            raise ValueError("`pissa_convert` is only valid for LoRA training.")
+
+        if self.pissa_convert and (self.stage in ["rm", "ppo", "kto"] or self.use_ref_model):
+            raise ValueError("Cannot use PiSSA for current training stage.")
+
         if self.train_mm_proj_only and self.finetuning_type != "full":
             raise ValueError("`train_mm_proj_only` is only valid for full training.")

src/llamafactory/hparams/generating_args.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from dataclasses import asdict, dataclass, field
 from typing import Any, Dict, Optional
 

src/llamafactory/hparams/model_args.py

@@ -1,5 +1,28 @@
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from dataclasses import asdict, dataclass, field
-from typing import Any, Dict, Literal, Optional
+from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Union
+
+from typing_extensions import Self
+
+
+if TYPE_CHECKING:
+    import torch
 
 
 @dataclass
@@ -22,6 +45,10 @@ class ModelArguments:
             )
         },
     )
+    adapter_folder: Optional[str] = field(
+        default=None,
+        metadata={"help": "The folder containing the adapter weights to load."},
+    )
     cache_dir: Optional[str] = field(
         default=None,
         metadata={"help": "Where to store the pre-trained models downloaded from huggingface.co or modelscope.cn."},
@@ -127,13 +154,9 @@ class ModelArguments:
         metadata={"help": "Whether or not to disable CUDA graph in the vLLM engine."},
     )
     vllm_max_lora_rank: int = field(
-        default=8,
+        default=32,
         metadata={"help": "Maximum rank of all LoRAs in the vLLM engine."},
     )
-    vllm_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
-        default="auto",
-        metadata={"help": "Data type for model weights and activations in the vLLM engine."},
-    )
     offload_folder: str = field(
         default="offload",
         metadata={"help": "Path to offload model weights."},
@@ -142,6 +165,10 @@ class ModelArguments:
         default=True,
         metadata={"help": "Whether or not to use KV cache in generation."},
     )
+    infer_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
+        default="auto",
+        metadata={"help": "Data type for model weights and activations at inference."},
+    )
     hf_hub_token: Optional[str] = field(
         default=None,
         metadata={"help": "Auth token to log in with Hugging Face Hub."},
@@ -192,9 +219,9 @@ class ModelArguments:
     )
 
     def __post_init__(self):
-        self.compute_dtype = None
-        self.device_map = None
-        self.model_max_length = None
+        self.compute_dtype: Optional["torch.dtype"] = None
+        self.device_map: Optional[Union[str, Dict[str, Any]]] = None
+        self.model_max_length: Optional[int] = None
 
         if self.split_special_tokens and self.use_fast_tokenizer:
             raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
@@ -216,3 +243,13 @@ class ModelArguments:
 
     def to_dict(self) -> Dict[str, Any]:
         return asdict(self)
+
+    @classmethod
+    def copyfrom(cls, old_arg: Self, **kwargs) -> Self:
+        arg_dict = old_arg.to_dict()
+        arg_dict.update(**kwargs)
+        new_arg = cls(**arg_dict)
+        new_arg.compute_dtype = old_arg.compute_dtype
+        new_arg.device_map = old_arg.device_map
+        new_arg.model_max_length = old_arg.model_max_length
+        return new_arg

src/llamafactory/hparams/parser.py

@@ -1,3 +1,20 @@
+# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
+#
+# This code is inspired by the HuggingFace's transformers library.
+# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import logging
 import os
 import sys
@@ -8,6 +25,7 @@ import transformers
 from transformers import HfArgumentParser, Seq2SeqTrainingArguments
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.trainer_utils import get_last_checkpoint
+from transformers.training_args import ParallelMode
 from transformers.utils import is_torch_bf16_gpu_available
 from transformers.utils.versions import require_version
 
@@ -72,6 +90,9 @@ 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 finetuning_args.pissa_init:
+            raise ValueError("Please use scripts/pissa_init.py to initialize PiSSA for a quantized model.")
+
         if model_args.resize_vocab:
             raise ValueError("Cannot resize embedding layers of a quantized model.")
 
@@ -162,6 +183,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     ):
         raise ValueError("PPO only accepts wandb or tensorboard logger.")
 
+    if training_args.parallel_mode == ParallelMode.NOT_DISTRIBUTED:
+        raise ValueError("Please launch distributed training with `llamafactory-cli` or `torchrun`.")
+
     if training_args.max_steps == -1 and data_args.streaming:
         raise ValueError("Please specify `max_steps` in streaming mode.")
 
@@ -171,9 +195,6 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     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.")
-
     if finetuning_args.pure_bf16:
         if not is_torch_bf16_gpu_available():
             raise ValueError("This device does not support `pure_bf16`.")
@@ -184,14 +205,14 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if (
         finetuning_args.use_galore
         and finetuning_args.galore_layerwise
-        and training_args.parallel_mode.value == "distributed"
+        and training_args.parallel_mode == ParallelMode.DISTRIBUTED
     ):
         raise ValueError("Distributed training does not support layer-wise GaLore.")
 
     if (
         finetuning_args.use_badam
         and finetuning_args.badam_mode == "layer"
-        and training_args.parallel_mode.value == "distributed"
+        and training_args.parallel_mode == ParallelMode.DISTRIBUTED
     ):
         raise ValueError("Layer-wise BAdam does not yet support distributed training, use ratio-wise BAdam.")
 
@@ -233,7 +254,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
 
     # Post-process training arguments
     if (
-        training_args.parallel_mode.value == "distributed"
+        training_args.parallel_mode == ParallelMode.DISTRIBUTED
         and training_args.ddp_find_unused_parameters is None
         and finetuning_args.finetuning_type == "lora"
     ):
@@ -293,7 +314,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
             training_args.local_rank,
             training_args.device,
             training_args.n_gpu,
-            training_args.parallel_mode.value == "distributed",
+            training_args.parallel_mode == ParallelMode.DISTRIBUTED,
             str(model_args.compute_dtype),
         )
     )
@@ -332,6 +353,7 @@ def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
 
     if model_args.export_dir is not None and model_args.export_device == "cpu":
         model_args.device_map = {"": torch.device("cpu")}
+        model_args.model_max_length = data_args.cutoff_len
     else:
         model_args.device_map = "auto"
 

src/llamafactory/launcher.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from llamafactory.train.tuner import run_exp
 
 

src/llamafactory/model/__init__.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from .loader import load_config, load_model, load_tokenizer
 from .model_utils.misc import find_all_linear_modules
 from .model_utils.valuehead import load_valuehead_params

src/llamafactory/model/adapter.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import re
 from typing import TYPE_CHECKING
 
@@ -25,8 +39,12 @@ def _setup_full_tuning(
     model: "PreTrainedModel",
     model_args: "ModelArguments",
     finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
     cast_trainable_params_to_fp32: bool,
 ) -> None:
+    if not is_trainable:
+        return
+
     logger.info("Fine-tuning method: Full")
     forbidden_modules = set()
     if model_args.visual_inputs and finetuning_args.freeze_vision_tower:
@@ -47,8 +65,12 @@ def _setup_freeze_tuning(
     model: "PreTrainedModel",
     model_args: "ModelArguments",
     finetuning_args: "FinetuningArguments",
+    is_trainable: bool,
     cast_trainable_params_to_fp32: bool,
 ) -> None:
+    if not is_trainable:
+        return
+
     logger.info("Fine-tuning method: Freeze")
     if model_args.visual_inputs:
         config = model.config.text_config
@@ -132,7 +154,9 @@ def _setup_lora_tuning(
     is_trainable: bool,
     cast_trainable_params_to_fp32: bool,
 ) -> "PeftModel":
-    logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
+    if is_trainable:
+        logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
+
     adapter_to_resume = None
 
     if model_args.adapter_name_or_path is not None:
@@ -155,8 +179,16 @@ def _setup_lora_tuning(
         else:
             adapter_to_merge = model_args.adapter_name_or_path
 
+        init_kwargs = {
+            "subfolder": model_args.adapter_folder,
+            "offload_folder": model_args.offload_folder,
+            "cache_dir": model_args.cache_dir,
+            "revision": model_args.model_revision,
+            "token": model_args.hf_hub_token,
+        }
+
         for adapter in adapter_to_merge:
-            model: "LoraModel" = PeftModel.from_pretrained(model, adapter, offload_folder=model_args.offload_folder)
+            model: "LoraModel" = PeftModel.from_pretrained(model, adapter, **init_kwargs)
             model = model.merge_and_unload()
 
         if len(adapter_to_merge) > 0:
@@ -166,12 +198,9 @@ def _setup_lora_tuning(
             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,
-                )
+                model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
+
+        logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
 
     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":
@@ -209,16 +238,24 @@ def _setup_lora_tuning(
             "lora_alpha": finetuning_args.lora_alpha,
             "lora_dropout": finetuning_args.lora_dropout,
             "use_rslora": finetuning_args.use_rslora,
+            "use_dora": finetuning_args.use_dora,
             "modules_to_save": finetuning_args.additional_target,
         }
 
         if model_args.use_unsloth:
             model = get_unsloth_peft_model(model, model_args, peft_kwargs)
         else:
+            if finetuning_args.pissa_init:
+                if finetuning_args.pissa_iter == -1:
+                    logger.info("Using PiSSA initialization.")
+                    peft_kwargs["init_lora_weights"] = "pissa"
+                else:
+                    logger.info("Using PiSSA initialization with FSVD steps {}.".format(finetuning_args.pissa_iter))
+                    peft_kwargs["init_lora_weights"] = "pissa_niter_{}".format(finetuning_args.pissa_iter)
+
             lora_config = LoraConfig(
                 task_type=TaskType.CAUSAL_LM,
                 inference_mode=False,
-                use_dora=finetuning_args.use_dora,
                 **peft_kwargs,
             )
             model = get_peft_model(model, lora_config)
@@ -227,9 +264,6 @@ def _setup_lora_tuning(
         for param in filter(lambda p: p.requires_grad, model.parameters()):
             param.data = param.data.to(torch.float32)
 
-    if model_args.adapter_name_or_path is not None:
-        logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
-
     return model
 
 
@@ -247,29 +281,37 @@ def init_adapter(
 
     Note that the trainable parameters must be cast to float32.
     """
-    if (not is_trainable) and model_args.adapter_name_or_path is None:
-        logger.info("Adapter is not found at evaluation, load the base model.")
-        return model
+    if is_trainable and getattr(model, "quantization_method", None) is not None:
+        if finetuning_args.finetuning_type != "lora":
+            raise ValueError("Quantized models can only be used for the LoRA tuning.")
 
-    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.pissa_init:
+            raise ValueError("Cannot initialize PiSSA adapter on quantized models.")
 
-    if is_deepspeed_zero3_enabled() or is_fsdp_enabled() or finetuning_args.pure_bf16 or finetuning_args.use_badam:
+    # cast trainable parameters to float32 if:
+    # 1. is_trainable and quantization_bit is not None (qlora)
+    # 2. is_trainable and not deepspeed zero3 and not fsdp (zero3 or fsdp already in float32)
+    # 3. is_trainable and not pure_bf16 and not badam
+    if not is_trainable:
+        cast_trainable_params_to_fp32 = False
+    elif model_args.quantization_bit is None and (
+        is_deepspeed_zero3_enabled() or is_fsdp_enabled() or finetuning_args.pure_bf16 or finetuning_args.use_badam
+    ):
         logger.info("ZeRO3/FSDP/PureBF16/BAdam detected, remaining trainable params as their original precision.")
         cast_trainable_params_to_fp32 = False
     else:
         logger.info("Upcasting trainable params to float32.")
         cast_trainable_params_to_fp32 = True
 
-    if is_trainable and finetuning_args.finetuning_type == "full":
-        _setup_full_tuning(model, model_args, finetuning_args, cast_trainable_params_to_fp32)
-
-    if is_trainable and finetuning_args.finetuning_type == "freeze":
-        _setup_freeze_tuning(model, model_args, finetuning_args, cast_trainable_params_to_fp32)
-
-    if finetuning_args.finetuning_type == "lora":
+    if finetuning_args.finetuning_type == "full":
+        _setup_full_tuning(model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32)
+    elif finetuning_args.finetuning_type == "freeze":
+        _setup_freeze_tuning(model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32)
+    elif finetuning_args.finetuning_type == "lora":
         model = _setup_lora_tuning(
             config, model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32
         )
+    else:
+        raise NotImplementedError("Unknown finetuning type: {}.".format(finetuning_args.finetuning_type))
 
     return model

src/llamafactory/model/loader.py

@@ -1,3 +1,17 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict
 
 from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer

src/llamafactory/model/model_utils/attention.py

@@ -1,7 +1,22 @@
+# Copyright 2024 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import TYPE_CHECKING
 
+from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
+
 from ...extras.logging import get_logger
-from ...extras.packages import is_flash_attn2_available, is_sdpa_available
 
 
 if TYPE_CHECKING:
@@ -21,13 +36,13 @@ def configure_attn_implementation(config: "PretrainedConfig", model_args: "Model
         requested_attn_implementation = "eager"
 
     elif model_args.flash_attn == "sdpa":
-        if not is_sdpa_available():
+        if not is_torch_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():
+        if not is_flash_attn_2_available():
             logger.warning("FlashAttention-2 is not installed.")
             return