release v0.8.1

Former-commit-id: 875a34f492701d1c644facbe9ede411af2931513

.dockerignore

@@ -0,0 +1,13 @@
+.vscode
+.git
+.github
+.venv
+cache
+data
+hf_cache
+output
+examples
+.dockerignore
+.gitattributes
+.gitignore
+Dockerfile

.github/CODE_OF_CONDUCT.md

@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+`hoshihiyouga AT gmail DOT com`.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.

.github/CONTRIBUTING.md

@@ -0,0 +1,21 @@
+# Contributing to LLaMA Factory
+
+Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable.
+
+It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you.
+
+However you choose to contribute, please be mindful and respect our [code of conduct](CODE_OF_CONDUCT.md).
+
+**This guide was heavily inspired by [transformers guide to contributing](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md).**
+
+## Ways to contribute
+
+There are several ways you can contribute to LLaMA Factory:
+
+* Fix outstanding issues with the existing code.
+* Submit issues related to bugs or desired new features.
+* Contribute to the examples or to the documentation.
+
+### Style guide
+
+LLaMA Factory follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html), check it for details.

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -0,0 +1,58 @@
+name: "\U0001F41B Bug / Help"
+description: Create a report to help us improve the LLaMA Factory
+body:
+  - type: checkboxes
+    id: reminder
+    attributes:
+      label: Reminder
+      description: |
+        Please ensure you have read the README carefully and searched the existing issues.
+        请确保您已经认真阅读了 README 并且搜索过现有的 Issue。
+
+      options:
+        - label: I have read the README and searched the existing issues.
+          required: true
+
+  - type: textarea
+    id: system-info
+    validations:
+      required: true
+    attributes:
+      label: System Info
+      description: |
+        Please share your system info with us. You can run the command **llamafactory-cli env** and copy-paste its output below.
+        请提供您的系统信息。您可以在命令行运行 **llamafactory-cli env** 并将其输出复制到该文本框中。
+
+      placeholder: llamafactory version, platform, python version, ...
+
+  - type: textarea
+    id: reproduction
+    validations:
+      required: true
+    attributes:
+      label: Reproduction
+      description: |
+        Please provide code snippets, error messages and stack traces that reproduces the problem.
+        请提供运行参数，错误信息以及异常堆栈以便于我们复现该问题。
+        Remember to use Markdown tags to correctly format your code.
+        请合理使用 Markdown 标签来格式化您的文本。
+
+      placeholder: |
+        llamafactory-cli train ...
+
+  - type: textarea
+    id: expected-behavior
+    validations:
+      required: false
+    attributes:
+      label: Expected behavior
+      description: |
+        Please provide a clear and concise description of what you would expect to happen.
+        请提供您原本的目的，即这段代码的期望行为。
+
+  - type: textarea
+    id: others
+    validations:
+      required: false
+    attributes:
+      label: Others

.github/PULL_REQUEST_TEMPLATE.md

@@ -0,0 +1,7 @@
+# What does this PR do?
+
+Fixes # (issue)
+
+## Before submitting
+
+- [ ] Did you read the [contributor guideline](https://github.com/hiyouga/LLaMA-Factory/blob/main/.github/CONTRIBUTING.md)?

.github/SECURITY.md

@@ -0,0 +1,7 @@
+# Reporting Security Issues
+
+To report a security issue, please use the GitHub Security Advisory ["Report a Vulnerability"](https://github.com/hiyouga/LLaMA-Factory/security/advisories/new) tab.
+
+We will send a response indicating the next steps in handling your report. After the initial reply to your report, the security team will keep you informed of the progress towards a fix and full announcement, and may ask for additional information or guidance.
+
+Report security bugs in third-party modules to the person or team maintaining the module.

.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/tests.yml

@@ -0,0 +1,47 @@
+name: tests
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "**.py"
+      - "requirements.txt"
+      - ".github/workflows/*.yml"
+  pull_request:
+    types:
+      - review_requested
+    branches:
+      - main
+    paths:
+      - "**.py"
+      - "requirements.txt"
+      - ".github/workflows/*.yml"
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+
+    steps:
+      - 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

.gitignore

@@ -157,4 +157,9 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
+
+# custom .gitignore
+user.config
+saves/
+cache/

CITATION.cff

@@ -0,0 +1,37 @@
+cff-version: 1.2.0
+date-released: 2024-03
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Zheng"
+  given-names: "Yaowei"
+- family-names: "Zhang"
+  given-names: "Richong"
+- family-names: "Zhang"
+  given-names: "Junhao"
+- family-names: "Ye"
+  given-names: "Yanhan"
+- family-names: "Luo"
+  given-names: "Zheyan"
+- family-names: "Ma"
+  given-names: "Yongqiang"
+title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
+url: "https://arxiv.org/abs/2403.13372"
+preferred-citation:
+  type: article
+  authors:
+    - family-names: "Zheng"
+      given-names: "Yaowei"
+    - family-names: "Zhang"
+      given-names: "Richong"
+    - family-names: "Zhang"
+      given-names: "Junhao"
+    - family-names: "Ye"
+      given-names: "Yanhan"
+    - family-names: "Luo"
+      given-names: "Zheyan"
+    - family-names: "Ma"
+      given-names: "Yongqiang"
+  journal: "arXiv preprint arXiv:2403.13372"
+  title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
+  url: "https://arxiv.org/abs/2403.13372"
+  year: 2024

Dockerfile

@@ -0,0 +1,44 @@
+# 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 config set global.index-url $PIP_INDEX
+RUN python -m pip install --upgrade pip
+RUN python -m pip install -r requirements.txt
+
+# Copy the rest of the application into the image
+COPY . /app/
+
+# Install the LLaMA Factory
+RUN EXTRA_PACKAGES="metrics"; \
+    if [ "$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
+
+# 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

Makefile

@@ -0,0 +1,14 @@
+.PHONY: quality style test
+
+check_dirs := scripts src tests
+
+quality:
+	ruff check $(check_dirs)
+	ruff format --check $(check_dirs)
+
+style:
+	ruff check $(check_dirs) --fix
+	ruff format $(check_dirs)
+
+test:
+	pytest tests/

README.md

@@ -1,40 +1,139 @@
-# LLaMA Factory: Training and Evaluating Large Language Models with Minimal Effort
+![# LLaMA Factory](assets/logo.png)
 
 [![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
 [![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
 [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
-[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
-[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
+[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
+[![Citation](https://img.shields.io/badge/citation-44-green)](#projects-using-llama-factory)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
-[![Discord](https://dcbadge.vercel.app/api/server/e73gccsSd?compact=true&style=flat)](https://discord.gg/e73gccsSd)
+[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
+[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
+[![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
+[![Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
+[![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
+
+[![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
 
 👋 Join our [WeChat](assets/wechat.jpg).
 
 \[ English | [中文](README_zh.md) \]
 
-## LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory
+**Fine-tuning a large language model can be easy as...**
 
-Launch **LLaMA Board** via `CUDA_VISIBLE_DEVICES=0 python src/train_web.py`. (multiple GPUs are not supported yet)
+https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89-7ace5698baf6
 
-Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU.
+Choose your path:
 
-https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1
+- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
+- **PAI-DSW**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
+- **Local machine**: Please refer to [usage](#getting-started)
+
+## Table of Contents
+
+- [Features](#features)
+- [Benchmark](#benchmark)
+- [Changelog](#changelog)
+- [Supported Models](#supported-models)
+- [Supported Training Approaches](#supported-training-approaches)
+- [Provided Datasets](#provided-datasets)
+- [Requirement](#requirement)
+- [Getting Started](#getting-started)
+- [Projects using LLaMA Factory](#projects-using-llama-factory)
+- [License](#license)
+- [Citation](#citation)
+- [Acknowledgement](#acknowledgement)
+
+## Features
+
+- **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.
+- **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.
+
+## Benchmark
+
+Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA Factory's LoRA tuning offers up to **3.7 times faster** training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.
+
+![benchmark](assets/benchmark.svg)
+
+<details><summary>Definitions</summary>
+
+- **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024)
+- **Rouge Score**: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024)
+- **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024)
+- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA Factory's LoRA tuning.
+
+</details>
 
 ## Changelog
 
-[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neft_alpha` argument to activate NEFTune, e.g., `--neft_alpha 5`.
+[24/06/07] We supported fine-tuning the **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** series models.
 
-[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention.
+[24/06/05] We supported fine-tuning the **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** models.
 
-[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models.
+[24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
 
-[23/09/10] We supported using **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)** for the LLaMA models. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
+<details><summary>Full Changelog</summary>
 
-[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings.
+[24/05/20] We supported fine-tuning the **PaliGemma** series models. Note that the PaliGemma models are pre-trained models, you need to fine-tune them with `gemma` template for chat completion.
 
-[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models.
+[24/05/18] We supported **[KTO](https://arxiv.org/abs/2402.01306)** algorithm for preference learning. See [examples](examples/README.md) for usage.
 
-[23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
+[24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.
+
+[24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage.
+
+[24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
+
+[24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See [examples](examples/README.md) for usage.
+
+[24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)**. See [examples](examples/README.md) for usage.
+
+[24/04/16] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves **117%** speed and **50%** memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison).
+
+[24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See [examples](examples/README.md) for usage.
+
+[24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv!
+
+[24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See [examples](examples/README.md) for usage.
+
+[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See [examples](examples/README.md) for usage.
+
+[24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. See [examples](examples/README.md) for usage.
+
+[24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `infer_backend: vllm` to enjoy **270%** inference speed.
+
+[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `use_dora: true` to activate DoRA training.
+
+[24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See [examples](examples/README.md) for usage.
+
+[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details.
+
+[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall_en`.
+
+[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `use_unsloth: true` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
+
+[23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).
+
+[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage.
+
+[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.
+
+[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `shift_attn: true` argument to enable shift short attention.
+
+[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [examples](examples/README.md) for usage.
+
+[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `flash_attn: fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
+
+[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `rope_scaling: linear` argument in training and `rope_scaling: dynamic` argument at inference to extrapolate the position embeddings.
+
+[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [examples](examples/README.md) for usage.
+
+[23/07/31] We supported **dataset streaming**. Try `streaming: true` and `max_steps: 10000` arguments to load your dataset in streaming mode.
 
 [23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details.
 
@@ -46,45 +145,63 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 
 [23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**.
 
-[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models.
+[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). See [examples](examples/README.md) for usage.
+
+</details>
 
 ## Supported Models
 
-| Model                                                    | Model size                  | Default module    | Template  |
-| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
-| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B                      | W_pack            | baichuan  |
-| [Baichuan2](https://github.com/baichuan-inc/Baichuan2)   | 7B/13B                      | W_pack            | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [ChatGLM3](https://github.com/THUDM/ChatGLM3)            | 6B                          | query_key_value   | chatglm3  |
-| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B/180B                 | query_key_value   | -         |
-| [InternLM](https://github.com/InternLM/InternLM)         | 7B/20B                      | q_proj,v_proj     | intern    |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2    |
-| [Mistral](https://huggingface.co/mistralai)              | 7B                          | q_proj,v_proj     | mistral   |
-| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5)      | 1.3B                        | Wqkv              | -         |
-| [Qwen](https://github.com/QwenLM/Qwen)                   | 7B/14B                      | c_attn            | qwen      |
-| [XVERSE](https://github.com/xverse-ai)                   | 7B/13B/65B                  | q_proj,v_proj     | xverse    |
+| 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      |
 
 > [!NOTE]
-> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules.
+> 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.
 >
-> For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "chat" models.
+> Remember to use the **SAME** template in training and inference.
 
-Please refer to [template.py](src/llmtuner/extras/template.py) for a full list of models we supported.
+Please refer to [constants.py](src/llamafactory/extras/constants.py) for a full list of models we supported.
+
+You also can add a custom chat template to [template.py](src/llamafactory/data/template.py).
 
 ## Supported Training Approaches
 
-| Approach               |   Full-parameter   | Partial-parameter  |       LoRA         |       QLoRA        |
+| Approach               |     Full-tuning    |    Freeze-tuning   |       LoRA         |       QLoRA        |
 | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
 | Pre-Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| Reward Modeling        |                    |                    | :white_check_mark: | :white_check_mark: |
-| PPO Training           |                    |                    | :white_check_mark: | :white_check_mark: |
-| DPO Training           | :white_check_mark: |                    | :white_check_mark: | :white_check_mark: |
-
-> [!NOTE]
-> Use `--quantization_bit 4/8` argument to enable QLoRA.
+| Reward Modeling        | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| PPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| DPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| KTO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| ORPO Training          | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| SimPO Training         | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 
 ## Provided Datasets
 
@@ -97,6 +214,8 @@ Please refer to [template.py](src/llmtuner/extras/template.py) for a full list o
 - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
 - [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
 - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
+- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
+- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
 - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
 - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
 
@@ -104,12 +223,12 @@ Please refer to [template.py](src/llmtuner/extras/template.py) for a full list o
 
 <details><summary>Supervised fine-tuning datasets</summary>
 
+- [Identity (en&zh)](data/identity.json)
 - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
-- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
-- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
-- [Self-cognition (zh)](data/self_cognition.json)
-- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
+- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
+- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
+- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
 - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
 - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
@@ -118,34 +237,53 @@ Please refer to [template.py](src/llmtuner/extras/template.py) for a full list o
 - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
 - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
 - [UltraChat (en)](https://github.com/thunlp/UltraChat)
-- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
 - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
 - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
+- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
 - [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
 - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
 - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
 - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
-- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
+- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
+- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
+- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
 - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
 - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
 - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
 - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
 - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
+- [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)
+- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
+- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
+- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
+- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
+- [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de)
+- [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de)
+- [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de)
+- [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de)
+- [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de)
+- [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)
 
 </details>
 
 <details><summary>Preference datasets</summary>
 
+- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
+- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
+- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
-- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
+- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
+- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
 
 </details>
 
-Please refer to [data/README.md](data/README.md) for details.
-
 Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
 
 ```bash
@@ -155,346 +293,272 @@ huggingface-cli login
 
 ## Requirement
 
-- Python 3.8+ and PyTorch 1.13.1+
-- 🤗Transformers, Datasets, Accelerate, PEFT and TRL
-- sentencepiece, protobuf and tiktoken
-- fire, jieba, rouge-chinese and nltk (used at evaluation and predict)
-- gradio and matplotlib (used in web UI)
-- uvicorn, fastapi and sse-starlette (used in API)
+| Mandatory    | Minimum | Recommend |
+| ------------ | ------- | --------- |
+| python       | 3.8     | 3.11      |
+| torch        | 1.13.1  | 2.3.0     |
+| transformers | 4.41.2  | 4.41.2    |
+| datasets     | 2.16.0  | 2.19.2    |
+| accelerate   | 0.30.1  | 0.30.1    |
+| peft         | 0.11.1  | 0.11.1    |
+| trl          | 0.8.6   | 0.9.4     |
 
-And **powerful GPUs**!
+| Optional     | Minimum | Recommend |
+| ------------ | ------- | --------- |
+| CUDA         | 11.6    | 12.2      |
+| deepspeed    | 0.10.0  | 0.14.0    |
+| bitsandbytes | 0.39.0  | 0.43.1    |
+| vllm         | 0.4.3   | 0.4.3     |
+| flash-attn   | 2.3.0   | 2.5.9     |
+
+### Hardware Requirement
+
+\* *estimated*
+
+| Method            | Bits |   7B  |  13B  |  30B  |   70B  |  110B  |  8x7B |  8x22B |
+| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
+| Full              | AMP  | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
+| Full              |  16  |  60GB | 120GB | 300GB |  600GB |  900GB | 400GB | 1200GB |
+| Freeze            |  16  |  20GB |  40GB |  80GB |  200GB |  360GB | 160GB |  400GB |
+| LoRA/GaLore/BAdam |  16  |  16GB |  32GB |  64GB |  160GB |  240GB | 120GB |  320GB |
+| QLoRA             |   8  |  10GB |  20GB |  40GB |   80GB |  140GB |  60GB |  160GB |
+| QLoRA             |   4  |   6GB |  12GB |  24GB |   48GB |   72GB |  30GB |   96GB |
+| QLoRA             |   2  |   4GB |   8GB |  16GB |   24GB |   48GB |  18GB |   48GB |
 
 ## Getting Started
 
-### Data Preparation (optional)
-
-Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset.
-
-> [!NOTE]
-> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`.
-
-### Dependence Installation (optional)
-
-```bash
-git clone https://github.com/hiyouga/LLaMA-Factory.git
-conda create -n llama_factory python=3.10
-conda activate llama_factory
-cd LLaMA-Factory
-pip install -r requirements.txt
-```
-
-If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1.
-
-```bash
-pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
-```
-
-### Train on a single GPU
+### Installation
 
 > [!IMPORTANT]
-> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training).
-
-#### Pre-Training
+> Installation is mandatory.
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage pt \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset wiki_demo \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --output_dir path_to_pt_checkpoint \
-    --overwrite_cache \
-    --per_device_train_batch_size 4 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 5e-5 \
-    --num_train_epochs 3.0 \
-    --plot_loss \
-    --fp16
+git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
+cd LLaMA-Factory
+pip install -e ".[torch,metrics]"
 ```
 
-#### Supervised Fine-Tuning
+Extra dependencies available: torch, torch_npu, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
+
+> [!TIP]
+> Use `pip install --no-deps -e .` to resolve package conflicts.
+
+<details><summary>For Windows users</summary>
+
+If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you need to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage sft \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset alpaca_gpt4_en \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --output_dir path_to_sft_checkpoint \
-    --overwrite_cache \
-    --per_device_train_batch_size 4 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 5e-5 \
-    --num_train_epochs 3.0 \
-    --plot_loss \
-    --fp16
+pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
 ```
 
-#### Reward Modeling
+To enable FlashAttention-2 on the Windows platform, you need to install the precompiled `flash-attn` library, which supports CUDA 12.1 to 12.2. Please download the corresponding version from [flash-attention](https://github.com/bdashore3/flash-attention/releases) based on your requirements.
+
+</details>
+
+<details><summary>For Ascend NPU users</summary>
+
+Join [NPU user group](assets/wechat_npu.jpg).
+
+To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e '.[torch-npu,metrics]'`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage rm \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset comparison_gpt4_en \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --resume_lora_training False \
-    --checkpoint_dir path_to_sft_checkpoint \
-    --output_dir path_to_rm_checkpoint \
-    --per_device_train_batch_size 2 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 1e-6 \
-    --num_train_epochs 1.0 \
-    --plot_loss \
-    --fp16
+# replace the url according to your CANN version and devices
+# install CANN Toolkit
+wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run
+bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
+
+# install CANN Kernels
+wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
+bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
+
+# set env variables
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
 ```
 
-#### PPO Training
+| Requirement  | Minimum | Recommend   |
+| ------------ | ------- | ----------- |
+| CANN         | 8.0.RC1 | 8.0.RC1     |
+| torch        | 2.1.0   | 2.1.0       |
+| torch-npu    | 2.1.0   | 2.1.0.post3 |
+| deepspeed    | 0.13.2  | 0.13.2      |
+
+Docker image:
+
+- 32GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
+- 64GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
+
+Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
+
+If you cannot infer model on NPU devices, try setting `do_sample: false` in the configurations.
+
+</details>
+
+### Data Preparation
+
+Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
+
+> [!NOTE]
+> Please update `data/dataset_info.json` to use your custom dataset.
+
+### Quickstart
+
+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 python src/train_bash.py \
-    --stage ppo \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset alpaca_gpt4_en \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --resume_lora_training False \
-    --checkpoint_dir path_to_sft_checkpoint \
-    --reward_model path_to_rm_checkpoint \
-    --output_dir path_to_ppo_checkpoint \
-    --per_device_train_batch_size 2 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 1e-5 \
-    --num_train_epochs 1.0 \
-    --plot_loss \
-    --fp16
+llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
+llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 ```
 
-#### DPO Training
+See [examples/README.md](examples/README.md) for advanced usage (including distributed training).
+
+> [!TIP]
+> Use `llamafactory-cli help` to show help information.
+
+### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage dpo \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset comparison_gpt4_en \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --resume_lora_training False \
-    --checkpoint_dir path_to_sft_checkpoint \
-    --output_dir path_to_dpo_checkpoint \
-    --per_device_train_batch_size 2 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 1e-5 \
-    --num_train_epochs 1.0 \
-    --plot_loss \
-    --fp16
+llamafactory-cli webui
 ```
 
-### Distributed Training
-
-#### Use Huggingface Accelerate
+### Build Docker
 
 ```bash
-accelerate config # configure the environment
-accelerate launch src/train_bash.py # arguments (same as above)
+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 llamafactory \
+    llamafactory:latest
 ```
 
-<details><summary>Example config for LoRA training</summary>
+> [!TIP]
+> Use Docker Compose to build image via `docker compose up -d`.
+
+<details><summary>Details about volume</summary>
+
+- hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
+- data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
+- output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
+
+</details>
+
+### Deploy with OpenAI-style API and vLLM
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
+```
+
+> [!TIP]
+> Visit https://platform.openai.com/docs/api-reference/chat/create for API document.
+
+### Download from ModelScope Hub
+
+If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.
+
+```bash
+export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
+```
+
+Train the model by specifying a model ID of the ModelScope Hub as the `model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.
+
+### Use W&B Logger
+
+To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments.
 
 ```yaml
-compute_environment: LOCAL_MACHINE
-distributed_type: MULTI_GPU
-downcast_bf16: 'no'
-gpu_ids: all
-machine_rank: 0
-main_training_function: main
-mixed_precision: fp16
-num_machines: 1
-num_processes: 4
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false
+report_to: wandb
+run_name: test_run # optional
 ```
 
-</details>
-
-#### Use DeepSpeed
-
-```bash
-deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
-    --deepspeed ds_config.json \
-    ... # arguments (same as above)
-```
-
-<details><summary>Example config for full-parameter training with DeepSpeed ZeRO-2</summary>
-
-```json
-{
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "gradient_accumulation_steps": "auto",
-  "gradient_clipping": "auto",
-  "zero_allow_untested_optimizer": true,
-  "fp16": {
-    "enabled": "auto",
-    "loss_scale": 0,
-    "initial_scale_power": 16,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },  
-  "zero_optimization": {
-    "stage": 2,
-    "allgather_partitions": true,
-    "allgather_bucket_size": 5e8,
-    "reduce_scatter": true,
-    "reduce_bucket_size": 5e8,
-    "overlap_comm": false,
-    "contiguous_gradients": true
-  }
-}
-```
-
-</details>
-
-### Export model
-
-```bash
-python src/export_model.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint \
-    --export_dir path_to_export
-```
-
-### API Demo
-
-```bash
-python src/api_demo.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint
-```
-
-> [!NOTE]
-> Visit `http://localhost:8000/docs` for API documentation.
-
-### CLI Demo
-
-```bash
-python src/cli_demo.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint
-```
-
-### Web Demo
-
-```bash
-python src/web_demo.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint
-```
-
-### Evaluation
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
-    --model_name_or_path path_to_llama_model \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint \
-    --template vanilla \
-    --task mmlu \
-    --split test \
-    --lang en \
-    --n_shot 5 \
-    --batch_size 4
-```
-
-### Predict
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage sft \
-    --model_name_or_path path_to_llama_model \
-    --do_predict \
-    --dataset alpaca_gpt4_en \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_predict_result \
-    --per_device_eval_batch_size 8 \
-    --max_samples 100 \
-    --predict_with_generate
-```
-
-> [!NOTE]
-> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict.
+Set `WANDB_API_KEY` to [your key](https://wandb.ai/authorize) when launching training tasks to log in with your W&B account.
 
 ## Projects using LLaMA Factory
 
-- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
-- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
-- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
-- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
+If you have a project that should be incorporated, please contact via email or create a pull request.
+
+<details><summary>Click to show</summary>
+
+1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
+1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
+1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
+1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)
+1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)
+1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
+1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
+1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)
+1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)
+1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)
+1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
+1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
+1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
+1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. 2024. [[arxiv]](https://arxiv.org/abs/2402.11809)
+1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
+1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
+1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
+1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
+1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
+1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
+1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
+1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
+1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
+1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
+1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
+1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
+1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
+1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
+1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
+1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
+1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
+1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
+1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
+1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
+1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
+1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2404.17140)
+1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
+1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
+1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
+1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
+1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
+1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
+1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
+1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
+
+</details>
 
 ## License
 
 This repository is licensed under the [Apache-2.0 License](LICENSE).
 
-Please follow the model licenses to use the corresponding model weights: [Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
+Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
 
 ## Citation
 
 If this work is helpful, please kindly cite as:
 
 ```bibtex
-@Misc{llama-factory,
-  title = {LLaMA Factory},
-  author = {hiyouga},
-  howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
-  year = {2023}
+@article{zheng2024llamafactory,
+  title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
+  author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
+  journal={arXiv preprint arXiv:2403.13372},
+  year={2024},
+  url={http://arxiv.org/abs/2403.13372}
 }
 ```
 
 ## Acknowledgement
 
-This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works.
+This repo benefits from [PEFT](https://github.com/huggingface/peft), [TRL](https://github.com/huggingface/trl), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works.
 
 ## Star History
 

README_zh.md

@@ -1,40 +1,139 @@
-# LLaMA Factory: 轻松的大模型训练与评估
+![# LLaMA Factory](assets/logo.png)
 
 [![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
 [![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
 [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
-[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
-[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
+[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
+[![Citation](https://img.shields.io/badge/citation-44-green)](#使用了-llama-factory-的项目)
 [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
-[![Discord](https://dcbadge.vercel.app/api/server/e73gccsSd?compact=true&style=flat)](https://discord.gg/e73gccsSd)
+[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
+[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)
+[![Open in DSW](https://gallery.pai-ml.com/assets/open-in-dsw.svg)](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)
+[![Spaces](https://img.shields.io/badge/🤗-Open%20in%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
+[![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
+
+[![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
 
 👋 加入我们的[微信群](assets/wechat.jpg)。
 
 \[ [English](README.md) | 中文 \]
 
-## LLaMA Board: 通过一站式网页界面快速上手 LLaMA Factory
+**微调大模型可以像这样轻松…**
 
-使用 `CUDA_VISIBLE_DEVICES=0 python src/train_web.py` 启动 **LLaMA Board**。（该界面目前仅支持单卡训练）
+https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd-d76c6d0a6594
 
-下面是使用单张 GPU 在 10 分钟内更改对话式大型语言模型自我认知的示例。
+选择你的打开方式：
 
-https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1
+- **Colab**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
+- **PAI-DSW**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
+- **本地机器**：请见[如何使用](#如何使用)
+
+## 目录
+
+- [项目特色](#项目特色)
+- [性能指标](#性能指标)
+- [更新日志](#更新日志)
+- [模型](#模型)
+- [训练方法](#训练方法)
+- [数据集](#数据集)
+- [软硬件依赖](#软硬件依赖)
+- [如何使用](#如何使用)
+- [使用了 LLaMA Factory 的项目](#使用了-llama-factory-的项目)
+- [协议](#协议)
+- [引用](#引用)
+- [致谢](#致谢)
+
+## 项目特色
+
+- **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。
+- **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。
+
+## 性能指标
+
+与 ChatGLM 官方的 [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning) 微调相比，LLaMA Factory 的 LoRA 微调提供了 **3.7 倍**的加速比，同时在广告文案生成任务上取得了更高的 Rouge 分数。结合 4 比特量化技术，LLaMA Factory 的 QLoRA 微调进一步降低了 GPU 显存消耗。
+
+![benchmark](assets/benchmark.svg)
+
+<details><summary>变量定义</summary>
+
+- **Training Speed**: 训练阶段每秒处理的样本数量。（批处理大小=4，截断长度=1024）
+- **Rouge Score**: [广告文案生成](https://aclanthology.org/D19-1321.pdf)任务验证集上的 Rouge-2 分数。（批处理大小=4，截断长度=1024）
+- **GPU Memory**: 4 比特量化训练的 GPU 显存峰值。（批处理大小=1，截断长度=1024）
+- 我们在 ChatGLM 的 P-Tuning 中采用 `pre_seq_len=128`，在 LLaMA Factory 的 LoRA 微调中采用 `lora_rank=32`。
+
+</details>
 
 ## 更新日志
 
-[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `--neft_alpha` 参数启用 NEFTune，例如 `--neft_alpha 5`。
+[24/06/07] 我们支持了 **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** 系列模型的微调。
 
-[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `--shift_attn` 参数以启用该功能。
+[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。
 
-[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。
+[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)。
 
-[23/09/10] 我们针对 LLaMA 模型支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn` 参数以启用 FlashAttention-2。
+<details><summary>展开日志</summary>
 
-[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。
+[24/05/20] 我们支持了 **PaliGemma** 系列模型的微调。注意 PaliGemma 是预训练模型，你需要使用 `gemma` 模板进行微调使其获得对话能力。
 
-[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。使用方法请参阅[此示例](#dpo-训练)。
+[24/05/18] 我们支持了 **[KTO](https://arxiv.org/abs/2402.01306)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
 
-[23/07/31] 我们支持了**数据流式加载**。请尝试使用 `--streaming` 和 `--max_steps 10000` 参数来流式加载数据集。
+[24/05/14] 我们支持了昇腾 NPU 设备的训练和推理。详情请查阅[安装](#安装-llama-factory)部分。
+
+[24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 **[Colab 笔记本](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型，详情请见 [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) 和 [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese)。
+
+[24/04/21] 我们基于 [AstraMindAI 的仓库](https://github.com/astramind-ai/Mixture-of-depths)支持了 **[混合深度训练](https://arxiv.org/abs/2404.02258)**。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/04/16] 我们支持了 **[BAdam](https://arxiv.org/abs/2404.02827)**。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/04/16] 我们支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的长序列训练（24GB 可训练 Llama-2-7B-56k）。该方法相比 FlashAttention-2 提供了 **117%** 的训练速度和 **50%** 的显存节约。更多数据请见[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
+
+[24/03/31] 我们支持了 **[ORPO](https://arxiv.org/abs/2403.07691)**。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/03/21] 我们的论文 "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" 可在 arXiv 上查看！
+
+[24/03/20] 我们支持了能在 2x24GB GPU 上微调 70B 模型的 **FSDP+QLoRA**。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/03/13] 我们支持了 **[LoRA+](https://arxiv.org/abs/2402.12354)**。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/03/07] 我们支持了梯度低秩投影（**[GaLore](https://arxiv.org/abs/2403.03507)**）算法。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/03/07] 我们集成了 **[vLLM](https://github.com/vllm-project/vllm)** 以实现极速并发推理。请使用 `infer_backend: vllm` 来获得 **270%** 的推理速度。
+
+[24/02/28] 我们支持了 **[DoRA](https://arxiv.org/abs/2402.09353)** 微调。请使用 `use_dora: true` 参数进行 DoRA 微调。
+
+[24/02/15] 我们支持了 [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro) 提出的**块扩展**方法。详细用法请参照 [examples](examples/README_zh.md)。
+
+[24/02/05] Qwen1.5（Qwen2 测试版）系列模型已在 LLaMA-Factory 中实现微调支持。详情请查阅该[博客页面](https://qwenlm.github.io/zh/blog/qwen1.5/)。
+
+[24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**，微调时指定 `dataset: glaive_toolcall_zh` 即可使模型获得工具调用能力。
+
+[23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `use_unsloth: true` 参数启用 unsloth 优化。该方法可提供 **170%** 的训练速度，详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
+
+[23/12/12] 我们支持了微调最新的混合专家模型 **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)**。硬件需求请查阅[此处](#硬件依赖)。
+
+[23/12/01] 我们支持了从 **[魔搭社区](https://modelscope.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔搭社区下载)。
+
+[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `neftune_noise_alpha: 5` 参数启用 NEFTune。
+
+[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `shift_attn: true` 参数以启用该功能。
+
+[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。详细用法请参照 [examples](examples/README_zh.md)。
+
+[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `flash_attn: fa2` 参数以启用 FlashAttention-2。
+
+[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `rope_scaling: linear` 参数训练模型或使用 `rope_scaling: dynamic` 参数评估模型。
+
+[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。详细用法请参照 [examples](examples/README_zh.md)。
+
+[23/07/31] 我们支持了**数据流式加载**。请使用 `streaming: true` 和 `max_steps: 10000` 参数来流式加载数据集。
 
 [23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目（[LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)）。
 
@@ -46,32 +145,50 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 
 [23/06/22] 我们对齐了[示例 API](src/api_demo.py) 与 [OpenAI API](https://platform.openai.com/docs/api-reference/chat) 的格式，您可以将微调模型接入**任意基于 ChatGPT 的应用**中。
 
-[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。请使用 `--quantization_bit 4` 参数进行 4 比特量化微调。
+[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。详细用法请参照 [examples](examples/README_zh.md)。
+
+</details>
 
 ## 模型
 
-| 模型名                                                   | 模型大小                     | 默认模块           | Template  |
-| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
-| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B                      | W_pack            | baichuan  |
-| [Baichuan2](https://github.com/baichuan-inc/Baichuan2)   | 7B/13B                      | W_pack            | baichuan2 |
-| [BLOOM](https://huggingface.co/bigscience/bloom)         | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [BLOOMZ](https://huggingface.co/bigscience/bloomz)       | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value   | -         |
-| [ChatGLM3](https://github.com/THUDM/ChatGLM3)            | 6B                          | query_key_value   | chatglm3  |
-| [Falcon](https://huggingface.co/tiiuae/falcon-7b)        | 7B/40B/180B                 | query_key_value   | -         |
-| [InternLM](https://github.com/InternLM/InternLM)         | 7B/20B                      | q_proj,v_proj     | intern    |
-| [LLaMA](https://github.com/facebookresearch/llama)       | 7B/13B/33B/65B              | q_proj,v_proj     | -         |
-| [LLaMA-2](https://huggingface.co/meta-llama)             | 7B/13B/70B                  | q_proj,v_proj     | llama2    |
-| [Mistral](https://huggingface.co/mistralai)              | 7B                          | q_proj,v_proj     | mistral   |
-| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5)      | 1.3B                        | Wqkv              | -         |
-| [Qwen](https://github.com/QwenLM/Qwen)                   | 7B/14B                      | c_attn            | qwen      |
-| [XVERSE](https://github.com/xverse-ai)                   | 7B/13B/65B                  | q_proj,v_proj     | xverse    |
+| 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      |
 
 > [!NOTE]
-> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块。
+> 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**。
 >
-> 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Chat）模型请务必使用**对应的模板**。
+> 请务必在训练和推理时采用**完全一致**的模板。
 
-项目所支持模型的完整列表请参阅 [template.py](src/llmtuner/extras/template.py)。
+项目所支持模型的完整列表请参阅 [constants.py](src/llamafactory/extras/constants.py)。
+
+您也可以在 [template.py](src/llamafactory/data/template.py) 中添加自己的对话模板。
 
 ## 训练方法
 
@@ -79,12 +196,12 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
 | 预训练                 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | 指令监督微调            | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| 奖励模型训练            |                    |                    | :white_check_mark: | :white_check_mark: |
-| PPO 训练               |                    |                    | :white_check_mark: | :white_check_mark: |
-| DPO 训练               | :white_check_mark: |                    | :white_check_mark: | :white_check_mark: |
-
-> [!NOTE]
-> 请使用 `--quantization_bit 4/8` 参数来启用 QLoRA 训练。
+| 奖励模型训练            | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| PPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| DPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| KTO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| ORPO 训练              | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
+| SimPO 训练             | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 
 ## 数据集
 
@@ -97,6 +214,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
 - [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
 - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
+- [FineWeb (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
+- [FineWeb-Edu (en)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu)
 - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
 - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
 
@@ -104,12 +223,12 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 
 <details><summary>指令微调数据集</summary>
 
+- [Identity (en&zh)](data/identity.json)
 - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
-- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
-- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
-- [Self-cognition (zh)](data/self_cognition.json)
-- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
+- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
+- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
+- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
 - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
 - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
@@ -118,34 +237,53 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
 - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
 - [UltraChat (en)](https://github.com/thunlp/UltraChat)
-- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
 - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
 - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
 - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
+- [SlimOrca (en)](https://huggingface.co/datasets/Open-Orca/SlimOrca)
 - [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
 - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+- [Wiki QA (en)](https://huggingface.co/datasets/wiki_qa)
 - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
 - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
-- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
+- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
+- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
+- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
 - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
 - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
 - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
 - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
 - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
 - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
+- [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)
+- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
+- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
+- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
+- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
+- [OpenSchnabeltier (de)](https://huggingface.co/datasets/mayflowergmbh/openschnabeltier_de)
+- [Evol Instruct (de)](https://huggingface.co/datasets/mayflowergmbh/evol-instruct_de)
+- [Dolphin (de)](https://huggingface.co/datasets/mayflowergmbh/dolphin_de)
+- [Booksum (de)](https://huggingface.co/datasets/mayflowergmbh/booksum_de)
+- [Airoboros (de)](https://huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de)
+- [Ultrachat (de)](https://huggingface.co/datasets/mayflowergmbh/ultra-chat_de)
 
 </details>
 
 <details><summary>偏好数据集</summary>
 
+- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
+- [UltraFeedback (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)
+- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
 - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
-- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
+- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
+- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
 
 </details>
 
-使用方法请参考 [data/README_zh.md](data/README_zh.md) 文件。
-
 部分数据集的使用需要确认，我们推荐使用下述命令登录您的 Hugging Face 账户。
 
 ```bash
@@ -153,347 +291,274 @@ pip install --upgrade huggingface_hub
 huggingface-cli login
 ```
 
-## 软件依赖
+## 软硬件依赖
 
-- Python 3.8+ 和 PyTorch 1.13.1+
-- 🤗Transformers, Datasets, Accelerate, PEFT 和 TRL
-- sentencepiece, protobuf 和 tiktoken
-- fire, jieba, rouge-chinese 和 nltk (用于评估及预测)
-- gradio 和 matplotlib (用于网页端交互)
-- uvicorn, fastapi 和 sse-starlette (用于 API)
+| 必需项       | 至少     | 推荐      |
+| ------------ | ------- | --------- |
+| python       | 3.8     | 3.11      |
+| torch        | 1.13.1  | 2.3.0     |
+| transformers | 4.41.2  | 4.41.2    |
+| datasets     | 2.16.0  | 2.19.2    |
+| accelerate   | 0.30.1  | 0.30.1    |
+| peft         | 0.11.1  | 0.11.1    |
+| trl          | 0.8.6   | 0.9.4     |
 
-以及 **强而有力的 GPU**！
+| 可选项       | 至少     | 推荐      |
+| ------------ | ------- | --------- |
+| CUDA         | 11.6    | 12.2      |
+| deepspeed    | 0.10.0  | 0.14.0    |
+| bitsandbytes | 0.39.0  | 0.43.1    |
+| vllm         | 0.4.3   | 0.4.3     |
+| flash-attn   | 2.3.0   | 2.5.9     |
+
+### 硬件依赖
+
+\* *估算值*
+
+| 方法               | 精度 |   7B  |  13B  |  30B  |   70B  |  110B  |  8x7B |  8x22B |
+| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
+| Full              | AMP  | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
+| Full              |  16  |  60GB | 120GB | 300GB |  600GB |  900GB | 400GB | 1200GB |
+| Freeze            |  16  |  20GB |  40GB |  80GB |  200GB |  360GB | 160GB |  400GB |
+| LoRA/GaLore/BAdam |  16  |  16GB |  32GB |  64GB |  160GB |  240GB | 120GB |  320GB |
+| QLoRA             |   8  |  10GB |  20GB |  40GB |   80GB |  140GB |  60GB |  160GB |
+| QLoRA             |   4  |   6GB |  12GB |  24GB |   48GB |   72GB |  30GB |   96GB |
+| QLoRA             |   2  |   4GB |   8GB |  16GB |   24GB |   48GB |  18GB |   48GB |
 
 ## 如何使用
 
-### 数据准备（可跳过）
-
-关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。构建自定义数据集时，既可以使用单个 `.json` 文件，也可以使用一个[数据加载脚本](https://huggingface.co/docs/datasets/dataset_script)和多个文件。
-
-> [!NOTE]
-> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件，该文件的格式请参考 `data/README_zh.md`。
-
-### 环境搭建（可跳过）
-
-```bash
-git clone https://github.com/hiyouga/LLaMA-Factory.git
-conda create -n llama_factory python=3.10
-conda activate llama_factory
-cd LLaMA-Factory
-pip install -r requirements.txt
-```
-
-如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.1.
-
-```bash
-pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
-```
-
-### 单 GPU 训练
+### 安装 LLaMA Factory
 
 > [!IMPORTANT]
-> 如果您使用多张 GPU 训练模型，请移步[多 GPU 分布式训练](#多-gpu-分布式训练)部分。
-
-#### 预训练
+> 此步骤为必需。
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage pt \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset wiki_demo \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --output_dir path_to_pt_checkpoint \
-    --overwrite_cache \
-    --per_device_train_batch_size 4 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 5e-5 \
-    --num_train_epochs 3.0 \
-    --plot_loss \
-    --fp16
+git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
+cd LLaMA-Factory
+pip install -e ".[torch,metrics]"
 ```
 
-#### 指令监督微调
+可选的额外依赖项：torch、torch_npu、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
+
+> [!TIP]
+> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
+
+<details><summary>Windows 用户指南</summary>
+
+如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.2, 请根据您的 CUDA 版本情况选择适合的[发布版本](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels)。
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage sft \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset alpaca_gpt4_zh \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --output_dir path_to_sft_checkpoint \
-    --overwrite_cache \
-    --per_device_train_batch_size 4 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 5e-5 \
-    --num_train_epochs 3.0 \
-    --plot_loss \
-    --fp16
+pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
 ```
 
-#### 奖励模型训练
+如果要在 Windows 平台上开启 FlashAttention-2，需要安装预编译的 `flash-attn` 库，支持 CUDA 12.1 到 12.2，请根据需求到 [flash-attention](https://github.com/bdashore3/flash-attention/releases) 下载对应版本安装。
+
+</details>
+
+<details><summary>昇腾 NPU 用户指南</summary>
+
+加入 [NPU 用户群](assets/wechat_npu.jpg)。
+
+在昇腾 NPU 设备上安装 LLaMA Factory 时，需要指定额外依赖项，使用 `pip install -e '.[torch-npu,metrics]'` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage rm \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset comparison_gpt4_zh \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --resume_lora_training False \
-    --checkpoint_dir path_to_sft_checkpoint \
-    --output_dir path_to_rm_checkpoint \
-    --per_device_train_batch_size 2 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 1e-6 \
-    --num_train_epochs 1.0 \
-    --plot_loss \
-    --fp16
+# 请替换 URL 为 CANN 版本和设备型号对应的 URL
+# 安装 CANN Toolkit
+wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run
+bash Ascend-cann-toolkit_8.0.RC1.alpha001_linux-"$(uname -i)".run --install
+
+# 安装 CANN Kernels
+wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C17SPC701/Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run
+bash Ascend-cann-kernels-910b_8.0.RC1.alpha001_linux.run --install
+
+# 设置环境变量
+source /usr/local/Ascend/ascend-toolkit/set_env.sh
 ```
 
-#### PPO 训练
+| 依赖项       | 至少     | 推荐        |
+| ------------ | ------- | ----------- |
+| CANN         | 8.0.RC1 | 8.0.RC1     |
+| torch        | 2.1.0   | 2.1.0       |
+| torch-npu    | 2.1.0   | 2.1.0.post3 |
+| deepspeed    | 0.13.2  | 0.13.2      |
+
+Docker 镜像：
+
+- 32GB：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
+- 64GB：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
+
+请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
+
+如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
+
+</details>
+
+### 数据准备
+
+关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
+
+> [!NOTE]
+> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
+
+### 快速开始
+
+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 python src/train_bash.py \
-    --stage ppo \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset alpaca_gpt4_zh \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --resume_lora_training False \
-    --checkpoint_dir path_to_sft_checkpoint \
-    --reward_model path_to_rm_checkpoint \
-    --output_dir path_to_ppo_checkpoint \
-    --per_device_train_batch_size 2 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 1e-5 \
-    --num_train_epochs 1.0 \
-    --plot_loss
+llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
+llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 ```
 
-#### DPO 训练
+高级用法请参考 [examples/README_zh.md](examples/README_zh.md)（包括多 GPU 微调）。
+
+> [!TIP]
+> 使用 `llamafactory-cli help` 显示帮助信息。
+
+### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
 
 ```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage dpo \
-    --model_name_or_path path_to_llama_model \
-    --do_train \
-    --dataset comparison_gpt4_zh \
-    --template default \
-    --finetuning_type lora \
-    --lora_target q_proj,v_proj \
-    --resume_lora_training False \
-    --checkpoint_dir path_to_sft_checkpoint \
-    --output_dir path_to_dpo_checkpoint \
-    --per_device_train_batch_size 2 \
-    --gradient_accumulation_steps 4 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --save_steps 1000 \
-    --learning_rate 1e-5 \
-    --num_train_epochs 1.0 \
-    --plot_loss \
-    --fp16
+llamafactory-cli webui
 ```
 
-### 多 GPU 分布式训练
-
-#### 使用 Huggingface Accelerate
+### 构建 Docker
 
 ```bash
-accelerate config # 首先配置分布式环境
-accelerate launch src/train_bash.py # 参数同上
+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 llamafactory \
+    llamafactory:latest
 ```
 
-<details><summary>LoRA 训练的 Accelerate 配置示例</summary>
+> [!TIP]
+> 通过 `docker compose up -d` 使用 Docker Compose 构建镜像。
+
+<details><summary>数据卷详情</summary>
+
+- hf_cache：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
+- data：宿主机中存放数据集的文件夹路径。
+- output：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
+
+</details>
+
+### 利用 vLLM 部署 OpenAI API
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
+```
+
+> [!TIP]
+> API 文档请查阅 https://platform.openai.com/docs/api-reference/chat/create。
+
+### 从魔搭社区下载
+
+如果您在 Hugging Face 模型和数据集的下载中遇到了问题，可以通过下述方法使用魔搭社区。
+
+```bash
+export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
+```
+
+将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `LLM-Research/Meta-Llama-3-8B-Instruct`。
+
+### 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。
 
 ```yaml
-compute_environment: LOCAL_MACHINE
-distributed_type: MULTI_GPU
-downcast_bf16: 'no'
-gpu_ids: all
-machine_rank: 0
-main_training_function: main
-mixed_precision: fp16
-num_machines: 1
-num_processes: 4
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false
+report_to: wandb
+run_name: test_run # 可选
 ```
 
-</details>
-
-#### 使用 DeepSpeed
-
-```bash
-deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
-    --deepspeed ds_config.json \
-    ... # 参数同上
-```
-
-<details><summary>使用 DeepSpeed ZeRO-2 进行全参数训练的 DeepSpeed 配置示例</summary>
-
-```json
-{
-  "train_batch_size": "auto",
-  "train_micro_batch_size_per_gpu": "auto",
-  "gradient_accumulation_steps": "auto",
-  "gradient_clipping": "auto",
-  "zero_allow_untested_optimizer": true,
-  "fp16": {
-    "enabled": "auto",
-    "loss_scale": 0,
-    "initial_scale_power": 16,
-    "loss_scale_window": 1000,
-    "hysteresis": 2,
-    "min_loss_scale": 1
-  },  
-  "zero_optimization": {
-    "stage": 2,
-    "allgather_partitions": true,
-    "allgather_bucket_size": 5e8,
-    "reduce_scatter": true,
-    "reduce_bucket_size": 5e8,
-    "overlap_comm": false,
-    "contiguous_gradients": true
-  }
-}
-```
-
-</details>
-
-### 导出微调后的完整模型
-
-```bash
-python src/export_model.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint \
-    --export_dir path_to_export
-```
-
-### API 服务
-
-```bash
-python src/api_demo.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint
-```
-
-> [!NOTE]
-> 关于 API 文档请见 `http://localhost:8000/docs`。
-
-### 命令行测试
-
-```bash
-python src/cli_demo.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint
-```
-
-### 浏览器测试
-
-```bash
-python src/web_demo.py \
-    --model_name_or_path path_to_llama_model \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint
-```
-
-### 模型评估
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
-    --model_name_or_path path_to_llama_model \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint \
-    --template vanilla \
-    --task ceval \
-    --split validation \
-    --lang zh \
-    --n_shot 5 \
-    --batch_size 4
-```
-
-### 模型预测
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
-    --stage sft \
-    --model_name_or_path path_to_llama_model \
-    --do_predict \
-    --dataset alpaca_gpt4_zh \
-    --template default \
-    --finetuning_type lora \
-    --checkpoint_dir path_to_checkpoint \
-    --output_dir path_to_predict_result \
-    --per_device_eval_batch_size 8 \
-    --max_samples 100 \
-    --predict_with_generate
-```
-
-> [!NOTE]
-> 我们建议在量化模型的预测中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128`。
+在启动训练任务时，将 `WANDB_API_KEY` 设置为[密钥](https://wandb.ai/authorize)来登录 W&B 账户。
 
 ## 使用了 LLaMA Factory 的项目
 
-- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
-- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
-- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
-- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
+如果您有项目希望添加至下述列表，请通过邮件联系或者创建一个 PR。
+
+<details><summary>点击显示</summary>
+
+1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
+1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
+1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
+1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)
+1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)
+1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
+1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
+1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)
+1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)
+1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)
+1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
+1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
+1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
+1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. 2024. [[arxiv]](https://arxiv.org/abs/2402.11809)
+1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
+1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
+1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
+1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
+1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
+1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
+1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
+1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
+1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
+1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
+1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
+1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
+1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
+1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
+1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
+1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
+1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
+1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
+1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
+1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
+1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
+1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2404.17140)
+1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
+1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
+1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
+1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
+1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
+1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**：MBTI性格大模型项目，根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
+1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**：一个用于生成 Stable Diffusion 提示词的大型语言模型。[[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
+1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**：中文多模态医学大模型，基于 LLaVA-1.5-7B 在中文多模态医疗数据上微调而得。
+
+</details>
 
 ## 协议
 
 本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
 
-使用模型权重时，请遵循对应的模型协议：[Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
+使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [GLM4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
 
 ## 引用
 
 如果您觉得此项目有帮助，请考虑以下列格式引用
 
 ```bibtex
-@Misc{llama-factory,
-  title = {LLaMA Factory},
-  author = {hiyouga},
-  howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
-  year = {2023}
+@article{zheng2024llamafactory,
+  title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models}, 
+  author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
+  journal={arXiv preprint arXiv:2403.13372},
+  year={2024},
+  url={http://arxiv.org/abs/2403.13372}
 }
 ```
 
 ## 致谢
 
-本项目受益于 [PEFT](https://github.com/huggingface/peft)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat)，感谢以上诸位作者的付出。
+本项目受益于 [PEFT](https://github.com/huggingface/peft)、[TRL](https://github.com/huggingface/trl)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat)，感谢以上诸位作者的付出。
 
 ## Star History
 

data/README.md

@@ -1,77 +1,214 @@
-If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.json`.
+The [dataset_info.json](dataset_info.json) contains all available datasets. If you are using a custom dataset, please **make sure** to add a *dataset description* in `dataset_info.json` and specify `dataset: dataset_name` before training to use it.
+
+Currently we support datasets in **alpaca** and **sharegpt** format.
 
 ```json
 "dataset_name": {
-  "hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore below 3 arguments)",
-  "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore below 2 arguments)",
-  "file_name": "the name of the dataset file in the this directory. (required if above are not specified)",
-  "file_sha1": "the SHA-1 hash value of the dataset file. (optional, does not affect training)",
-  "subset": "the name of the subset. (optional, default: None)",
-  "ranking": "whether the dataset is a preference dataset or not. (default: false)",
+  "hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url and file_name)",
+  "ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url and file_name)",
+  "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name)",
+  "file_name": "the name of the dataset folder or dataset file in this directory. (required if above are not specified)",
   "formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
-  "columns": {
-    "prompt": "the column name in the dataset containing the prompts. (default: instruction, for alpaca)",
-    "query": "the column name in the dataset containing the queries. (default: input, for alpaca)",
-    "response": "the column name in the dataset containing the responses. (default: output, for alpaca)",
-    "history": "the column name in the dataset containing the histories. (default: None, for alpaca)",
-    "messages": "the column name in the dataset containing the messages. (default: conversations, for sharegpt)",
-    "role": "the key in the message represents the identity. (default: from, for sharegpt)",
-    "content": "the key in the message represents the content. (default: value, for sharegpt)"
+  "ranking": "whether the dataset is a preference dataset or not. (default: False)",
+  "subset": "the name of the subset. (optional, default: None)",
+  "folder": "the name of the folder of the dataset repository on the Hugging Face hub. (optional, default: None)",
+  "num_samples": "the number of samples in the dataset used for training. (optional, default: None)",
+  "columns (optional)": {
+    "prompt": "the column name in the dataset containing the prompts. (default: instruction)",
+    "query": "the column name in the dataset containing the queries. (default: input)",
+    "response": "the column name in the dataset containing the responses. (default: output)",
+    "history": "the column name in the dataset containing the histories. (default: None)",
+    "messages": "the column name in the dataset containing the messages. (default: conversations)",
+    "system": "the column name in the dataset containing the system prompts. (default: None)",
+    "tools": "the column name in the dataset containing the tool description. (default: None)",
+    "images": "the column name in the dataset containing the image inputs. (default: None)",
+    "chosen": "the column name in the dataset containing the chosen answers. (default: None)",
+    "rejected": "the column name in the dataset containing the rejected answers. (default: None)",
+    "kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
+  },
+  "tags (optional, used for the sharegpt format)": {
+    "role_tag": "the key in the message represents the identity. (default: from)",
+    "content_tag": "the key in the message represents the content. (default: value)",
+    "user_tag": "the value of the role_tag represents the user. (default: human)",
+    "assistant_tag": "the value of the role_tag represents the assistant. (default: gpt)",
+    "observation_tag": "the value of the role_tag represents the tool results. (default: observation)",
+    "function_tag": "the value of the role_tag represents the function call. (default: function_call)",
+    "system_tag": "the value of the role_tag represents the system prompt. (default: system, can override system column)"
   }
 }
 ```
 
-Given above, you can use the custom dataset via specifying `--dataset dataset_name`.
+## Alpaca Format
 
-Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format:
+### Supervised Fine-Tuning Dataset
+
+* [Example dataset](alpaca_en_demo.json)
+
+In supervised fine-tuning, the `instruction` column will be concatenated with the `input` column and used as the human prompt, then the human prompt would be `instruction\ninput`. The `output` column represents the model response.
+
+The `system` column will be used as the system prompt if specified.
+
+The `history` column is a list consisting of string tuples representing prompt-response pairs in the history messages. Note that the responses in the history **will also be learned by the model** in supervised fine-tuning.
 
 ```json
 [
   {
-    "instruction": "user instruction (required)",
-    "input": "user input (optional)",
+    "instruction": "human instruction (required)",
+    "input": "human input (optional)",
     "output": "model response (required)",
+    "system": "system prompt (optional)",
     "history": [
-      ["user instruction in the first round (optional)", "model response in the first round (optional)"],
-      ["user instruction in the second round (optional)", "model response in the second round (optional)"]
+      ["human instruction in the first round (optional)", "model response in the first round (optional)"],
+      ["human instruction in the second round (optional)", "model response in the second round (optional)"]
     ]
   }
 ]
 ```
 
-Regarding the above dataset, the `columns` in `dataset_info.json` should be:
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 
 ```json
 "dataset_name": {
+  "file_name": "data.json",
   "columns": {
     "prompt": "instruction",
     "query": "input",
     "response": "output",
+    "system": "system",
     "history": "history"
   }
 }
 ```
 
-where the `prompt` and `response` columns should contain non-empty values, represent instruction and response respectively. The `query` column will be concatenated with the `prompt` column and used as input for the model.
+### Pre-training Dataset
 
-The `history` column is a list consisting string tuples representing query-response pairs in history. Note that the responses **in each round will be used for training**.
+- [Example dataset](c4_demo.json)
 
-For the pre-training datasets, only the `prompt` column will be used for training.
-
-For the preference datasets, the `response` column should be a string list whose length is 2, with the preferred answers appearing first, for example:
+In pre-training, only the `text` column will be used for model learning.
 
 ```json
-{
-  "instruction": "user instruction",
-  "input": "user input",
-  "output": [
-    "chosen answer",
-    "rejected answer"
-  ]
+[
+  {"text": "document"},
+  {"text": "document"}
+]
+```
+
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "file_name": "data.json",
+  "columns": {
+    "prompt": "text"
+  }
 }
 ```
 
-The dataset in sharegpt format should follow the below format:
+### Preference Dataset
+
+Preference datasets are used for reward modeling, DPO training and ORPO training.
+
+It requires a better response in `chosen` column and a worse response in `rejected` column.
+
+```json
+[
+  {
+    "instruction": "human instruction (required)",
+    "input": "human input (optional)",
+    "chosen": "chosen answer (required)",
+    "rejected": "rejected answer (required)"
+  }
+]
+```
+
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "file_name": "data.json",
+  "ranking": true,
+  "columns": {
+    "prompt": "instruction",
+    "query": "input",
+    "chosen": "chosen",
+    "rejected": "rejected"
+  }
+}
+```
+
+### KTO Dataset
+
+- [Example dataset](kto_en_demo.json)
+
+KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
+
+```json
+[
+  {
+    "instruction": "human instruction (required)",
+    "input": "human input (optional)",
+    "output": "model response (required)",
+    "kto_tag": "human feedback [true/false] (required)"
+  }
+]
+```
+
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "file_name": "data.json",
+  "columns": {
+    "prompt": "instruction",
+    "query": "input",
+    "response": "output",
+    "kto_tag": "kto_tag"
+  }
+}
+```
+
+### Multimodal Dataset
+
+- [Example dataset](mllm_demo.json)
+
+Multimodal datasets require a `images` column containing the paths to the input images. Currently we only support one image.
+
+```json
+[
+  {
+    "instruction": "human instruction (required)",
+    "input": "human input (optional)",
+    "output": "model response (required)",
+    "images": [
+      "image path (required)"
+    ]
+  }
+]
+```
+
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "file_name": "data.json",
+  "columns": {
+    "prompt": "instruction",
+    "query": "input",
+    "response": "output",
+    "images": "images"
+  }
+}
+```
+
+## Sharegpt Format
+
+### Supervised Fine-Tuning Dataset
+
+- [Example dataset](glaive_toolcall_en_demo.json)
+
+Compared to the alpaca format, the sharegpt format allows the datasets have **more roles**, such as human, gpt, observation and function. They are presented in a list of objects in the `conversations` column.
+
+Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions.
 
 ```json
 [
@@ -79,29 +216,135 @@ The dataset in sharegpt format should follow the below format:
     "conversations": [
       {
         "from": "human",
-        "value": "user instruction"
+        "value": "human instruction"
+      },
+      {
+        "from": "function_call",
+        "value": "tool arguments"
+      },
+      {
+        "from": "observation",
+        "value": "tool result"
       },
       {
         "from": "gpt",
         "value": "model response"
       }
+    ],
+    "system": "system prompt (optional)",
+    "tools": "tool description (optional)"
+  }
+]
+```
+
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
+  "columns": {
+    "messages": "conversations",
+    "system": "system",
+    "tools": "tools"
+  }
+}
+```
+
+### Preference Dataset
+
+- [Example dataset](dpo_en_demo.json)
+
+Preference datasets in sharegpt format also require a better message in `chosen` column and a worse message in `rejected` column.
+
+```json
+[
+  {
+    "conversations": [
+      {
+        "from": "human",
+        "value": "human instruction"
+      },
+      {
+        "from": "gpt",
+        "value": "model response"
+      },
+      {
+        "from": "human",
+        "value": "human instruction"
+      }
+    ],
+    "chosen": {
+      "from": "gpt",
+      "value": "chosen answer (required)"
+    },
+    "rejected": {
+      "from": "gpt",
+      "value": "rejected answer (required)"
+    }
+  }
+]
+```
+
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
+
+```json
+"dataset_name": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
+  "ranking": true,
+  "columns": {
+    "messages": "conversations",
+    "chosen": "chosen",
+    "rejected": "rejected"
+  }
+}
+```
+
+### OpenAI Format
+
+The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
+
+```json
+[
+  {
+    "messages": [
+      {
+        "role": "system",
+        "content": "system prompt (optional)"
+      },
+      {
+        "role": "user",
+        "content": "human instruction"
+      },
+      {
+        "role": "assistant",
+        "content": "model response"
+      }
     ]
   }
 ]
 ```
 
-Regarding the above dataset, the `columns` in `dataset_info.json` should be:
+Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
 
 ```json
 "dataset_name": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
   "columns": {
-    "messages": "conversations",
-    "role": "from",
-    "content": "value"
+    "messages": "messages"
+  },
+  "tags": {
+    "role_tag": "role",
+    "content_tag": "content",
+    "user_tag": "user",
+    "assistant_tag": "assistant",
+    "system_tag": "system"
   }
 }
 ```
 
-where the `messages` column should be a list whose length is even, and follow the `u/a/u/a/u/a` order.
+The KTO datasets and multimodal datasets in sharegpt format are similar to the alpaca format.
 
-Pre-training datasets and preference datasets are incompatible with the sharegpt format yet.
+Pre-training datasets are **incompatible** with the sharegpt format.

data/README_zh.md

@@ -1,36 +1,62 @@
-如果您使用自定义数据集，请务必在 `dataset_info.json` 文件中按照以下格式提供数据集定义。
+[dataset_info.json](dataset_info.json) 包含了所有可用的数据集。如果您希望使用自定义数据集，请**务必**在 `dataset_info.json` 文件中添加*数据集描述*，并通过修改 `dataset: 数据集名称` 配置来使用数据集。
+
+目前我们支持 **alpaca** 格式和 **sharegpt** 格式的数据集。
 
 ```json
 "数据集名称": {
-  "hf_hub_url": "Hugging Face 上的项目地址（若指定，则忽略下列三个参数）",
-  "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略下列两个参数）",
-  "file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
-  "file_sha1": "数据集文件的SHA-1哈希值（可选，留空不影响训练）",
-  "subset": "数据集子集的名称（可选，默认：None）",
-  "ranking": "是否为偏好数据集（可选，默认：False）",
+  "hf_hub_url": "Hugging Face 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
+  "ms_hub_url": "ModelScope 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
+  "script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略 file_name）",
+  "file_name": "该目录下数据集文件夹或文件的名称（若上述参数未指定，则此项必需）",
   "formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
-  "columns": {
-    "prompt": "数据集代表提示词的表头名称（默认：instruction，用于 alpaca 格式）",
-    "query": "数据集代表请求的表头名称（默认：input，用于 alpaca 格式）",
-    "response": "数据集代表回答的表头名称（默认：output，用于 alpaca 格式）",
-    "history": "数据集代表历史对话的表头名称（默认：None，用于 alpaca 格式）",
-    "messages": "数据集代表消息列表的表头名称（默认：conversations，用于 sharegpt 格式）",
-    "role": "消息中代表发送者身份的键名（默认：from，用于 sharegpt 格式）",
-    "content": "消息中代表文本内容的键名（默认：value，用于 sharegpt 格式）"
+  "ranking": "是否为偏好数据集（可选，默认：False）",
+  "subset": "数据集子集的名称（可选，默认：None）",
+  "folder": "Hugging Face 仓库的文件夹名称（可选，默认：None）",
+  "num_samples": "该数据集中用于训练的样本数量。（可选，默认：None）",
+  "columns（可选）": {
+    "prompt": "数据集代表提示词的表头名称（默认：instruction）",
+    "query": "数据集代表请求的表头名称（默认：input）",
+    "response": "数据集代表回答的表头名称（默认：output）",
+    "history": "数据集代表历史对话的表头名称（默认：None）",
+    "messages": "数据集代表消息列表的表头名称（默认：conversations）",
+    "system": "数据集代表系统提示的表头名称（默认：None）",
+    "tools": "数据集代表工具描述的表头名称（默认：None）",
+    "images": "数据集代表图像输入的表头名称（默认：None）",
+    "chosen": "数据集代表更优回答的表头名称（默认：None）",
+    "rejected": "数据集代表更差回答的表头名称（默认：None）",
+    "kto_tag": "数据集代表 KTO 标签的表头名称（默认：None）"
+  },
+  "tags（可选，用于 sharegpt 格式）": {
+    "role_tag": "消息中代表发送者身份的键名（默认：from）",
+    "content_tag": "消息中代表文本内容的键名（默认：value）",
+    "user_tag": "消息中代表用户的 role_tag（默认：human）",
+    "assistant_tag": "消息中代表助手的 role_tag（默认：gpt）",
+    "observation_tag": "消息中代表工具返回结果的 role_tag（默认：observation）",
+    "function_tag": "消息中代表工具调用的 role_tag（默认：function_call）",
+    "system_tag": "消息中代表系统提示的 role_tag（默认：system，会覆盖 system column）"
   }
 }
 ```
 
-添加后可通过指定 `--dataset 数据集名称` 参数使用自定义数据集。
+## Alpaca 格式
 
-该项目目前支持两种格式的数据集：**alpaca** 和 **sharegpt**，其中 alpaca 格式的数据集按照以下方式组织：
+### 指令监督微调数据集
+
+- [样例数据集](alpaca_zh_demo.json)
+
+在指令监督微调时，`instruction` 列对应的内容会与 `input` 列对应的内容拼接后作为人类指令，即人类指令为 `instruction\ninput`。而 `output` 列对应的内容为模型回答。
+
+如果指定，`system` 列对应的内容将被作为系统提示词。
+
+`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮对话的指令和回答。注意在指令监督微调时，历史消息中的回答内容**也会被用于模型学习**。
 
 ```json
 [
   {
-    "instruction": "用户指令（必填）",
-    "input": "用户输入（选填）",
+    "instruction": "人类指令（必填）",
+    "input": "人类输入（选填）",
     "output": "模型回答（必填）",
+    "system": "系统提示词（选填）",
     "history": [
       ["第一轮指令（选填）", "第一轮回答（选填）"],
       ["第二轮指令（选填）", "第二轮回答（选填）"]
@@ -39,39 +65,150 @@
 ]
 ```
 
-对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为：
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 
 ```json
 "数据集名称": {
+  "file_name": "data.json",
   "columns": {
     "prompt": "instruction",
     "query": "input",
     "response": "output",
+    "system": "system",
     "history": "history"
   }
 }
 ```
 
-其中 `prompt` 和 `response` 列应当是非空的字符串，分别代表用户指令和模型回答。`query` 列的内容将会和 `prompt` 列拼接作为模型输入。
+### 预训练数据集
 
-`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮的指令和回答。注意每轮的模型回答**均会被用于训练**。
+- [样例数据集](c4_demo.json)
 
-对于预训练数据集，仅 `prompt` 列中的内容会用于模型训练。
-
-对于偏好数据集，`response` 列应当是一个长度为 2 的字符串列表，排在前面的代表更优的回答，例如：
+在预训练时，只有 `text` 列中的内容会用于模型学习。
 
 ```json
-{
-  "instruction": "用户指令",
-  "input": "用户输入",
-  "output": [
-    "优质回答",
-    "劣质回答"
-  ]
+[
+  {"text": "document"},
+  {"text": "document"}
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
+
+```json
+"数据集名称": {
+  "file_name": "data.json",
+  "columns": {
+    "prompt": "text"
+  }
 }
 ```
 
-而 sharegpt 格式的数据集按照以下方式组织：
+### 偏好数据集
+
+偏好数据集用于奖励模型训练、DPO 训练和 ORPO 训练。
+
+它需要在 `chosen` 列中提供更优的回答，并在 `rejected` 列中提供更差的回答。
+
+```json
+[
+  {
+    "instruction": "人类指令（必填）",
+    "input": "人类输入（选填）",
+    "chosen": "优质回答（必填）",
+    "rejected": "劣质回答（必填）"
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
+
+```json
+"数据集名称": {
+  "file_name": "data.json",
+  "ranking": true,
+  "columns": {
+    "prompt": "instruction",
+    "query": "input",
+    "chosen": "chosen",
+    "rejected": "rejected"
+  }
+}
+```
+
+### KTO 数据集
+
+- [样例数据集](kto_en_demo.json)
+
+KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人类反馈。
+
+```json
+[
+  {
+    "instruction": "人类指令（必填）",
+    "input": "人类输入（选填）",
+    "output": "模型回答（必填）",
+    "kto_tag": "人类反馈 [true/false]（必填）"
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
+
+```json
+"数据集名称": {
+  "file_name": "data.json",
+  "columns": {
+    "prompt": "instruction",
+    "query": "input",
+    "response": "output",
+    "kto_tag": "kto_tag"
+  }
+}
+```
+
+### 多模态数据集
+
+- [样例数据集](mllm_demo.json)
+
+多模态数据集需要额外添加一个 `images` 列，包含输入图像的路径。目前我们仅支持单张图像输入。
+
+```json
+[
+  {
+    "instruction": "人类指令（必填）",
+    "input": "人类输入（选填）",
+    "output": "模型回答（必填）",
+    "images": [
+      "图像路径（必填）"
+    ]
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
+
+```json
+"数据集名称": {
+  "file_name": "data.json",
+  "columns": {
+    "prompt": "instruction",
+    "query": "input",
+    "response": "output",
+    "images": "images"
+  }
+}
+```
+
+## Sharegpt 格式
+
+### 指令监督微调数据集
+
+- [样例数据集](glaive_toolcall_zh_demo.json)
+
+相比 alpaca 格式的数据集，sharegpt 格式支持**更多的角色种类**，例如 human、gpt、observation、function 等等。它们构成一个对象列表呈现在 `conversations` 列中。
+
+注意其中 human 和 observation 必须出现在奇数位置，gpt 和 function 必须出现在偶数位置。
 
 ```json
 [
@@ -79,29 +216,135 @@
     "conversations": [
       {
         "from": "human",
-        "value": "用户指令"
+        "value": "人类指令"
+      },
+      {
+        "from": "function_call",
+        "value": "工具参数"
+      },
+      {
+        "from": "observation",
+        "value": "工具结果"
       },
       {
         "from": "gpt",
         "value": "模型回答"
       }
+    ],
+    "system": "系统提示词（选填）",
+    "tools": "工具描述（选填）"
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
+
+```json
+"数据集名称": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
+  "columns": {
+    "messages": "conversations",
+    "system": "system",
+    "tools": "tools"
+  }
+}
+```
+
+### 偏好数据集
+
+- [样例数据集](dpo_zh_demo.json)
+
+Sharegpt 格式的偏好数据集同样需要在 `chosen` 列中提供更优的消息，并在 `rejected` 列中提供更差的消息。
+
+```json
+[
+  {
+    "conversations": [
+      {
+        "from": "human",
+        "value": "人类指令"
+      },
+      {
+        "from": "gpt",
+        "value": "模型回答"
+      },
+      {
+        "from": "human",
+        "value": "人类指令"
+      }
+    ],
+    "chosen": {
+      "from": "gpt",
+      "value": "优质回答"
+    },
+    "rejected": {
+      "from": "gpt",
+      "value": "劣质回答"
+    }
+  }
+]
+```
+
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
+
+```json
+"数据集名称": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
+  "ranking": true,
+  "columns": {
+    "messages": "conversations",
+    "chosen": "chosen",
+    "rejected": "rejected"
+  }
+}
+```
+
+### OpenAI 格式
+
+OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消息可能是系统提示词。
+
+```json
+[
+  {
+    "messages": [
+      {
+        "role": "system",
+        "content": "系统提示词（选填）"
+      },
+      {
+        "role": "user",
+        "content": "人类指令"
+      },
+      {
+        "role": "assistant",
+        "content": "模型回答"
+      }
     ]
   }
 ]
 ```
 
-对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为：
+对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
 
 ```json
 "数据集名称": {
+  "file_name": "data.json",
+  "formatting": "sharegpt",
   "columns": {
-    "messages": "conversations",
-    "role": "from",
-    "content": "value"
+    "messages": "messages"
+  },
+  "tags": {
+    "role_tag": "role",
+    "content_tag": "content",
+    "user_tag": "user",
+    "assistant_tag": "assistant",
+    "system_tag": "system"
   }
 }
 ```
 
-其中 `messages` 列必须为偶数长度的列表，且符合 `用户/模型/用户/模型/用户/模型` 的顺序。
+Sharegpt 格式中的 KTO 数据集和多模态数据集与 alpaca 格式的类似。
 
-预训练数据集和偏好数据集尚不支持 sharegpt 格式。
+预训练数据集**不支持** sharegpt 格式。

data/alpaca_data_en_52k.json.REMOVED.git-id

@@ -1 +0,0 @@
-3779ddbc040543ab1834ef216c983d6fcc06cc9a

data/alpaca_data_zh_51k.json.REMOVED.git-id

@@ -1 +0,0 @@
-fc9a6a3458caca2af8dafc6181773fe10c6d8657

data/alpaca_gpt4_data_en.json.REMOVED.git-id

@@ -1 +0,0 @@
-25508714b7879a1e5a6764ba7f979a980f549f1a

data/alpaca_gpt4_data_zh.json.REMOVED.git-id

@@ -1 +0,0 @@
-7cb6a7d11455bddc3d495750a2392683d775b184

data/belle_multiturn/belle_multiturn.py

@@ -1,7 +1,11 @@
 import json
+import os
+
 import datasets
 
 
+_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
+
 _DESCRIPTION = "BELLE multiturn chat dataset."
 
 _CITATION = """\
@@ -13,66 +17,51 @@ _CITATION = """\
 }
 """
 
-_HOMEPAGE = "https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M"
+_HOMEPAGE = "{}/datasets/BelleGroup/multiturn_chat_0.8M".format(_HF_ENDPOINT)
 _LICENSE = "gpl-3.0"
-_URL = "https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json"
+_URL = "{}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json".format(_HF_ENDPOINT)
 
 
 class BelleMultiturn(datasets.GeneratorBasedBuilder):
-
     VERSION = datasets.Version("0.0.0")
 
     def _info(self):
-        features = datasets.Features({
-            "instruction": datasets.Value("string"),
-            "output": datasets.Value("string"),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
-        })
+        features = datasets.Features(
+            {"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
+        )
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
         file_path = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": file_path
-                }
-            )
-        ]
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]
 
     def _generate_examples(self, filepath: str):
         with open(filepath, "r", encoding="utf-8") as f:
             for key, row in enumerate(f):
                 data = json.loads(row)
+                conversations = []
                 prompt = data["instruction"].strip()
                 response = data["output"].strip()
 
                 assist_idx = prompt.rfind("Assistant:")
                 human_idx = prompt.rfind("Human:")
-                query = prompt[human_idx+6:assist_idx].strip()
+                query = prompt[human_idx + 6 : assist_idx].strip()
                 prompt = prompt[:human_idx].strip()
-                history = []
+                conversations.insert(0, {"from": "gpt", "value": response})
+                conversations.insert(0, {"from": "human", "value": query})
 
                 while prompt.rfind("Assistant:") != -1:
                     assist_idx = prompt.rfind("Assistant:")
                     human_idx = prompt.rfind("Human:")
                     if human_idx != -1:
-                        old_query = prompt[human_idx+6:assist_idx].strip()
-                        old_resp = prompt[assist_idx+10:].strip()
-                        history.insert(0, (old_query, old_resp))
+                        old_query = prompt[human_idx + 6 : assist_idx].strip()
+                        old_resp = prompt[assist_idx + 10 :].strip()
+                        conversations.insert(0, {"from": "gpt", "value": old_resp})
+                        conversations.insert(0, {"from": "human", "value": old_query})
                     else:
                         break
                     prompt = prompt[:human_idx].strip()
 
-                yield key, {
-                    "instruction": query,
-                    "output": response,
-                    "history": history
-                }
+                yield key, {"conversations": conversations}

data/comparison_gpt4_data_en.json.REMOVED.git-id

@@ -1 +0,0 @@
-f5cb08305ff5dc9c17a09809c54c8c8834aadc70

data/comparison_gpt4_data_zh.json.REMOVED.git-id

@@ -1 +0,0 @@
-aee47b7b443496e37808d7f34ef10403ff99bcc3

data/example_dataset/example_dataset.py

@@ -1,46 +0,0 @@
-import json
-import datasets
-from typing import Any, Dict, List
-
-
-_DESCRIPTION = "An example of dataset."
-_CITATION = ""
-_HOMEPAGE = ""
-_LICENSE = ""
-_URL = "examples.json"
-
-
-class ExampleDataset(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("0.0.0")
-
-    def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features({
-            "instruction": datasets.Value("string"),
-            "input": datasets.Value("string"),
-            "output": datasets.Value("string"),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
-        })
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        file_path = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": file_path
-                }
-            )
-        ]
-
-    def _generate_examples(self, filepath: str) -> Dict[int, Dict[str, Any]]:
-        example_dataset = json.load(open(filepath, "r", encoding="utf-8"))
-        for key, example in enumerate(example_dataset):
-            yield key, example

data/hh_rlhf_en/hh_rlhf_en.py

@@ -1,62 +1,53 @@
 import json
-import datasets
+import os
 from typing import List
 
+import datasets
 
+
+_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
 _DESCRIPTION = "Human preference data about helpfulness and harmlessness."
 _CITATION = ""
-_HOMEPAGE = "https://huggingface.co/datasets/Anthropic/hh-rlhf"
+_HOMEPAGE = "{}/datasets/Anthropic/hh-rlhf".format(_HF_ENDPOINT)
 _LICENSE = "mit"
-_URL = "https://huggingface.co/datasets/Anthropic/hh-rlhf/resolve/main/"
+_URL = "{}/datasets/Anthropic/hh-rlhf/resolve/main/".format(_HF_ENDPOINT)
 _URLS = {
     "train": [
         _URL + "harmless-base/train.jsonl.gz",
         _URL + "helpful-base/train.jsonl.gz",
         _URL + "helpful-online/train.jsonl.gz",
-        _URL + "helpful-rejection-sampled/train.jsonl.gz"
+        _URL + "helpful-rejection-sampled/train.jsonl.gz",
     ],
     "test": [
         _URL + "harmless-base/test.jsonl.gz",
         _URL + "helpful-base/test.jsonl.gz",
         _URL + "helpful-online/test.jsonl.gz",
-        _URL + "helpful-rejection-sampled/test.jsonl.gz"
-    ]
+        _URL + "helpful-rejection-sampled/test.jsonl.gz",
+    ],
 }
 
 
 class HhRlhfEn(datasets.GeneratorBasedBuilder):
-
     VERSION = datasets.Version("0.0.0")
 
     def _info(self) -> datasets.DatasetInfo:
-        features = datasets.Features({
-            "instruction": datasets.Value("string"),
-            "output": datasets.Sequence(datasets.Value("string")),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
-        })
+        features = datasets.Features(
+            {
+                "instruction": datasets.Value("string"),
+                "chosen": datasets.Value("string"),
+                "rejected": datasets.Value("string"),
+                "history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+            }
+        )
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
         file_path = dl_manager.download_and_extract(_URLS)
         return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": file_path["train"]
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepaths": file_path["test"]
-                }
-            )
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_path["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": file_path["test"]}),
         ]
 
     def _generate_examples(self, filepaths: List[str]):
@@ -69,12 +60,12 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
                     rejected = data["rejected"]
 
                     assist_idx = rejected.rfind("\n\nAssistant: ")
-                    r_reject = rejected[assist_idx+13:].strip()
+                    r_reject = rejected[assist_idx + 13 :].strip()
                     assist_idx = chosen.rfind("\n\nAssistant: ")
-                    r_accept = chosen[assist_idx+13:].strip()
+                    r_accept = chosen[assist_idx + 13 :].strip()
 
                     human_idx = chosen.rfind("\n\nHuman: ")
-                    query = chosen[human_idx+9:assist_idx].strip()
+                    query = chosen[human_idx + 9 : assist_idx].strip()
                     prompt = chosen[:human_idx]
                     history = []
 
@@ -82,16 +73,12 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
                         assist_idx = prompt.rfind("\n\nAssistant: ")
                         human_idx = prompt.rfind("\n\nHuman: ")
                         if human_idx != -1:
-                            old_query = prompt[human_idx+9:assist_idx].strip()
-                            old_resp = prompt[assist_idx+13:].strip()
+                            old_query = prompt[human_idx + 9 : assist_idx].strip()
+                            old_resp = prompt[assist_idx + 13 :].strip()
                             history.insert(0, (old_query, old_resp))
                         else:
                             break
                         prompt = prompt[:human_idx]
 
-                    yield key, {
-                        "instruction": query,
-                        "output": [r_accept, r_reject],
-                        "history": history
-                    }
+                    yield key, {"instruction": query, "chosen": r_accept, "rejected": r_reject, "history": history}
                     key += 1

data/oaast_rm.json.REMOVED.git-id

@@ -1 +0,0 @@
-274079ea921762be356de85b18f13fa60b7ba8cb

data/oaast_sft.json.REMOVED.git-id

@@ -1 +0,0 @@
-57fd080be5bffe4153fe3ee26a175e3d56da30f3

data/sharegpt_zh_27k.json.REMOVED.git-id

@@ -1 +0,0 @@
-38c89869c6aeca2a3af9ea1e09afe460f9b46810

data/ultra_chat/ultra_chat.py

@@ -1,7 +1,11 @@
 import json
-import datasets
+import os
 from typing import List
 
+import datasets
+
+
+_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
 
 _DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data."
 
@@ -16,37 +20,25 @@ _CITATION = """\
 }
 """
 
-_HOMEPAGE = "https://huggingface.co/datasets/stingning/ultrachat"
+_HOMEPAGE = "{}/datasets/stingning/ultrachat".format(_HF_ENDPOINT)
 _LICENSE = "cc-by-nc-4.0"
-_BASE_DATA_URL = "https://huggingface.co/datasets/stingning/ultrachat/resolve/main/train_{idx}.jsonl"
+_BASE_DATA_URL = "{}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl".format(_HF_ENDPOINT)
 
 
 class UltraChat(datasets.GeneratorBasedBuilder):
-
     VERSION = datasets.Version("0.0.0")
 
     def _info(self):
-        features = datasets.Features({
-            "conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
-        })
+        features = datasets.Features(
+            {"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
+        )
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION
+            description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
         )
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
-        file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)] # multiple shards
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepaths": file_paths
-                }
-            )
-        ]
+        file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)]  # multiple shards
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_paths})]
 
     def _generate_examples(self, filepaths: List[str]):
         for filepath in filepaths:
@@ -54,7 +46,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
                 for row in f:
                     try:
                         data = json.loads(row)
-                    except:
+                    except Exception:
                         continue
                     key: int = data["id"]
                     content: List[str] = data["data"]
@@ -62,10 +54,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
                         content.pop(-1)
                     if len(content) < 2:
                         continue
-                    conversations = [{
-                        "from": "human" if i % 2 == 0 else "gpt",
-                        "value": content[i]
-                    } for i in range(len(content))]
-                    yield key, {
-                        "conversations": conversations
-                    }
+                    conversations = [
+                        {"from": "human" if i % 2 == 0 else "gpt", "value": content[i]} for i in range(len(content))
+                    ]
+                    yield key, {"conversations": conversations}

docker-compose.yml

@@ -0,0 +1,29 @@
+version: '3.8'
+
+services:
+  llamafactory:
+    build:
+      dockerfile: Dockerfile
+      context: .
+      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
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            count: "all"
+            capabilities: [gpu]
+    restart: unless-stopped

evaluation/ceval/ceval.py

@@ -19,7 +19,7 @@ import pandas as pd
 
 _CITATION = """\
 @article{huang2023ceval,
-  title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models}, 
+  title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
   author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian},
   journal={arXiv preprint arXiv:2305.08322},
   year={2023}
@@ -133,25 +133,19 @@ class Ceval(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "test", f"{task_name}_test.csv"
-                    ),
+                    "filepath": os.path.join(data_dir, "test", f"{task_name}_test.csv"),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "val", f"{task_name}_val.csv"
-                    ),
+                    "filepath": os.path.join(data_dir, "val", f"{task_name}_val.csv"),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "dev", f"{task_name}_dev.csv"
-                    ),
+                    "filepath": os.path.join(data_dir, "dev", f"{task_name}_dev.csv"),
                 },
             ),
         ]

evaluation/cmmlu/cmmlu.py

@@ -37,73 +37,73 @@ _LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Internatio
 _URL = "cmmlu.zip"
 
 task_list = [
-     'agronomy',
-     'anatomy',
-     'ancient_chinese',
-     'arts',
-     'astronomy',
-     'business_ethics',
-     'chinese_civil_service_exam',
-     'chinese_driving_rule',
-     'chinese_food_culture',
-     'chinese_foreign_policy',
-     'chinese_history',
-     'chinese_literature',
-     'chinese_teacher_qualification',
-     'clinical_knowledge',
-     'college_actuarial_science',
-     'college_education',
-     'college_engineering_hydrology',
-     'college_law',
-     'college_mathematics',
-     'college_medical_statistics',
-     'college_medicine',
-     'computer_science',
-     'computer_security',
-     'conceptual_physics',
-     'construction_project_management',
-     'economics',
-     'education',
-     'electrical_engineering',
-     'elementary_chinese',
-     'elementary_commonsense',
-     'elementary_information_and_technology',
-     'elementary_mathematics',
-     'ethnology',
-     'food_science',
-     'genetics',
-     'global_facts',
-     'high_school_biology',
-     'high_school_chemistry',
-     'high_school_geography',
-     'high_school_mathematics',
-     'high_school_physics',
-     'high_school_politics',
-     'human_sexuality',
-     'international_law',
-     'journalism',
-     'jurisprudence',
-     'legal_and_moral_basis',
-     'logical',
-     'machine_learning',
-     'management',
-     'marketing',
-     'marxist_theory',
-     'modern_chinese',
-     'nutrition',
-     'philosophy',
-     'professional_accounting',
-     'professional_law',
-     'professional_medicine',
-     'professional_psychology',
-     'public_relations',
-     'security_study',
-     'sociology',
-     'sports_science',
-     'traditional_chinese_medicine',
-     'virology',
-     'world_history',
-     'world_religions',
+    "agronomy",
+    "anatomy",
+    "ancient_chinese",
+    "arts",
+    "astronomy",
+    "business_ethics",
+    "chinese_civil_service_exam",
+    "chinese_driving_rule",
+    "chinese_food_culture",
+    "chinese_foreign_policy",
+    "chinese_history",
+    "chinese_literature",
+    "chinese_teacher_qualification",
+    "clinical_knowledge",
+    "college_actuarial_science",
+    "college_education",
+    "college_engineering_hydrology",
+    "college_law",
+    "college_mathematics",
+    "college_medical_statistics",
+    "college_medicine",
+    "computer_science",
+    "computer_security",
+    "conceptual_physics",
+    "construction_project_management",
+    "economics",
+    "education",
+    "electrical_engineering",
+    "elementary_chinese",
+    "elementary_commonsense",
+    "elementary_information_and_technology",
+    "elementary_mathematics",
+    "ethnology",
+    "food_science",
+    "genetics",
+    "global_facts",
+    "high_school_biology",
+    "high_school_chemistry",
+    "high_school_geography",
+    "high_school_mathematics",
+    "high_school_physics",
+    "high_school_politics",
+    "human_sexuality",
+    "international_law",
+    "journalism",
+    "jurisprudence",
+    "legal_and_moral_basis",
+    "logical",
+    "machine_learning",
+    "management",
+    "marketing",
+    "marxist_theory",
+    "modern_chinese",
+    "nutrition",
+    "philosophy",
+    "professional_accounting",
+    "professional_law",
+    "professional_medicine",
+    "professional_psychology",
+    "public_relations",
+    "security_study",
+    "sociology",
+    "sports_science",
+    "traditional_chinese_medicine",
+    "virology",
+    "world_history",
+    "world_religions",
 ]
 
 

evaluation/mmlu/mmlu.py

@@ -136,31 +136,25 @@ class MMLU(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "data", "test", f"{task_name}_test.csv"
-                    ),
+                    "filepath": os.path.join(data_dir, "data", "test", f"{task_name}_test.csv"),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "data", "val", f"{task_name}_val.csv"
-                    ),
+                    "filepath": os.path.join(data_dir, "data", "val", f"{task_name}_val.csv"),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filepath": os.path.join(
-                        data_dir, "data", "dev", f"{task_name}_dev.csv"
-                    ),
+                    "filepath": os.path.join(data_dir, "data", "dev", f"{task_name}_dev.csv"),
                 },
             ),
         ]
 
     def _generate_examples(self, filepath):
-        df = pd.read_csv(filepath)
+        df = pd.read_csv(filepath, header=None)
         df.columns = ["question", "A", "B", "C", "D", "answer"]
 
         for i, instance in enumerate(df.to_dict(orient="records")):

examples/README.md

@@ -0,0 +1,233 @@
+We provide diverse examples about fine-tuning LLMs.
+
+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)
+- [Merging LoRA Adapters and Quantization](#merging-lora-adapters-and-quantization)
+- [Inferring LoRA Fine-Tuned Models](#inferring-lora-fine-tuned-models)
+- [Extras](#extras)
+
+## Examples
+
+### LoRA Fine-Tuning on A Single GPU
+
+#### (Continuous) Pre-Training
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
+```
+
+#### Supervised Fine-Tuning
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/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
+```
+
+#### Reward Modeling
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
+```
+
+#### PPO Training
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
+```
+
+#### DPO/ORPO/SimPO Training
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
+```
+
+#### KTO Training
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
+```
+
+#### Preprocess Dataset
+
+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
+```
+
+#### Evaluating on MMLU/CMMLU/C-Eval Benchmarks
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/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
+```
+
+#### 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
+```
+
+#### 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
+```
+
+### LoRA Fine-Tuning on Multiple NPUs
+
+#### Supervised Fine-Tuning with DeepSpeed ZeRO-0
+
+```bash
+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
+```
+
+### Full-Parameter Fine-Tuning on Multiple GPUs
+
+#### 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
+```
+
+#### 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
+```
+
+#### 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
+```
+
+### Merging LoRA Adapters and Quantization
+
+#### Merge LoRA Adapters
+
+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
+```
+
+#### Quantizing Model using AutoGPTQ
+
+```bash
+CUDA_VISIBLE_DEVICES=0 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
+```
+
+#### Use Web UI
+
+```bash
+CUDA_VISIBLE_DEVICES=0 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
+```
+
+### Extras
+
+#### Full-Parameter Fine-Tuning using GaLore
+
+```bash
+CUDA_VISIBLE_DEVICES=0 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
+```
+
+#### LoRA+ Fine-Tuning
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
+```
+
+#### Mixture-of-Depths Fine-Tuning
+
+```bash
+CUDA_VISIBLE_DEVICES=0 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
+```
+
+#### FSDP+QLoRA Fine-Tuning
+
+```bash
+bash examples/extras/fsdp_qlora/single_node.sh
+```

examples/README_zh.md

@@ -0,0 +1,233 @@
+我们提供了多样化的大模型微调示例脚本。
+
+请确保在 `LLaMA-Factory` 目录下执行下述命令。
+
+## 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)
+- [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)
+
+## 示例
+
+### 单 GPU LoRA 微调
+
+#### （增量）预训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/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
+```
+
+#### 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
+```
+
+#### 奖励模型训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
+```
+
+#### PPO 训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/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
+```
+
+#### KTO 训练
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
+```
+
+#### 预处理数据集
+
+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
+```
+
+#### 在 MMLU/CMMLU/C-Eval 上评估
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/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
+```
+
+### 单 GPU QLoRA 微调
+
+#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调（推荐）
+
+```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
+```
+
+#### 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
+```
+
+### 多 NPU LoRA 微调
+
+#### 使用 DeepSpeed ZeRO-0 进行指令监督微调
+
+```bash
+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
+```
+
+### 多 GPU 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
+```
+
+#### 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
+```
+
+#### 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
+```
+
+### 合并 LoRA 适配器与模型量化
+
+#### 合并 LoRA 适配器
+
+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
+```
+
+#### 使用 AutoGPTQ 量化模型
+
+```bash
+CUDA_VISIBLE_DEVICES=0 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
+```
+
+#### 使用浏览器界面
+
+```bash
+CUDA_VISIBLE_DEVICES=0 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
+```
+
+### 杂项
+
+#### 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
+```
+
+#### 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
+```
+
+#### LoRA+ 微调
+
+```bash
+CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/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
+```
+
+#### 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
+```
+
+#### FSDP+QLoRA 微调
+
+```bash
+bash examples/extras/fsdp_qlora/single_node.sh
+```

examples/accelerate/fsdp_config.yaml

@@ -0,0 +1,25 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_backward_prefetch: BACKWARD_PRE
+  fsdp_forward_prefetch: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_offload_params: true # offload may affect training speed
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: true
+machine_rank: 0
+main_training_function: main
+mixed_precision: fp16 # or bf16
+num_machines: 1 # the number of nodes
+num_processes: 2 # the number of GPUs in all nodes
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

examples/extras/badam/llama3_lora_sft.yaml

@@ -0,0 +1,41 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+use_badam: true
+badam_switch_mode: ascending
+badam_switch_interval: 50
+badam_verbose: 2
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/full/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+pure_bf16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/extras/fsdp_qlora/llama3_lora_sft.yaml

@@ -0,0 +1,42 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+quantization_bit: 4
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### ddp
+ddp_timeout: 180000000
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/extras/fsdp_qlora/single_node.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+# DO NOT use GPTQ/AWQ model in FSDP+QLoRA
+
+CUDA_VISIBLE_DEVICES=0,1 accelerate launch \
+    --config_file examples/accelerate/fsdp_config.yaml \
+    src/train.py examples/extras/fsdp_qlora/llama3_lora_sft.yaml

examples/extras/galore/llama3_full_sft.yaml

@@ -0,0 +1,42 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+use_galore: true
+galore_layerwise: true
+galore_target: mlp,self_attn
+galore_rank: 128
+galore_scale: 2.0
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/full/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+pure_bf16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/extras/llama_pro/expand.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+python scripts/llama_pro.py \
+    --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
+    --output_dir models/llama3-8b-instruct-pro \
+    --num_expand 8

examples/extras/llama_pro/llama3_freeze_sft.yaml

@@ -0,0 +1,40 @@
+### model
+model_name_or_path: models/llama3-8b-instruct-pro
+
+### method
+stage: sft
+do_train: true
+finetuning_type: freeze
+freeze_trainable_layers: 8
+freeze_trainable_modules: all
+use_llama_pro: true
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b-instruct-pro/freeze/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/extras/loraplus/llama3_lora_sft.yaml

@@ -0,0 +1,39 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+loraplus_lr_ratio: 16.0
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/extras/mod/llama3_full_sft.yaml

@@ -0,0 +1,39 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+mixture_of_depths: convert
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b-mod/full/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+optim: paged_adamw_8bit
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+pure_bf16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/full_multi_gpu/llama3_full_predict.yaml

@@ -0,0 +1,23 @@
+### model
+model_name_or_path: saves/llama3-8b/full/sft
+
+### method
+stage: sft
+do_predict: true
+finetuning_type: full
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 50
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/full/predict
+overwrite_output_dir: true
+
+### eval
+per_device_eval_batch_size: 1
+predict_with_generate: true

examples/full_multi_gpu/llama3_full_sft.yaml

@@ -0,0 +1,41 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+
+### ddp
+ddp_timeout: 180000000
+deepspeed: examples/deepspeed/ds_z3_config.json
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/full/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/inference/llama3.yaml

@@ -0,0 +1,2 @@
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+template: llama3

examples/inference/llama3_lora_sft.yaml

@@ -0,0 +1,4 @@
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+adapter_name_or_path: saves/llama3-8b/lora/sft
+template: llama3
+finetuning_type: lora

examples/inference/llama3_vllm.yaml

@@ -0,0 +1,4 @@
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+template: llama3
+infer_backend: vllm
+vllm_enforce_eager: true

examples/lora_multi_gpu/llama3_lora_sft.yaml

@@ -0,0 +1,41 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### ddp
+ddp_timeout: 180000000
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_multi_gpu/llama3_lora_sft_ds.yaml

@@ -0,0 +1,42 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### ddp
+ddp_timeout: 180000000
+deepspeed: examples/deepspeed/ds_z3_config.json
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_multi_npu/llama3_lora_sft_ds.yaml

@@ -0,0 +1,42 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### ddp
+ddp_timeout: 180000000
+deepspeed: examples/deepspeed/ds_z0_config.json
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_single_gpu/llama3_lora_dpo.yaml

@@ -0,0 +1,40 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: dpo
+do_train: true
+finetuning_type: lora
+lora_target: all
+pref_beta: 0.1
+pref_loss: sigmoid  # [sigmoid (dpo), orpo, simpo]
+
+### dataset
+dataset: dpo_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/dpo
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 5.0e-6
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_single_gpu/llama3_lora_eval.yaml

@@ -0,0 +1,19 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+adapter_name_or_path: saves/llama3-8b/lora/sft
+
+### method
+finetuning_type: lora
+
+### dataset
+task: mmlu
+split: test
+template: fewshot
+lang: en
+n_shot: 5
+
+### output
+save_dir: saves/llama3-8b/lora/eval
+
+### eval
+batch_size: 4

examples/lora_single_gpu/llama3_lora_kto.yaml

@@ -0,0 +1,38 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: kto
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: kto_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/kto
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 5.0e-6
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_single_gpu/llama3_lora_ppo.yaml

@@ -0,0 +1,38 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+reward_model: saves/llama3-8b/lora/reward
+
+### method
+stage: ppo
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/ppo
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### generate
+max_new_tokens: 512
+top_k: 0
+top_p: 0.9

examples/lora_single_gpu/llama3_lora_predict.yaml

@@ -0,0 +1,24 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+adapter_name_or_path: saves/llama3-8b/lora/sft
+
+### method
+stage: sft
+do_predict: true
+finetuning_type: lora
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 50
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/predict
+overwrite_output_dir: true
+
+### eval
+per_device_eval_batch_size: 1
+predict_with_generate: true

examples/lora_single_gpu/llama3_lora_pretrain.yaml

@@ -0,0 +1,37 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: pt
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: c4_demo
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_single_gpu/llama3_lora_reward.yaml

@@ -0,0 +1,38 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: rm
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: dpo_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/reward
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_single_gpu/llama3_lora_sft.yaml

@@ -0,0 +1,38 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/lora_single_gpu/llama3_preprocess.yaml

@@ -0,0 +1,21 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+tokenized_path: saves/llama3-8b/dataset/sft
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+overwrite_output_dir: true

examples/lora_single_gpu/llava1_5_lora_sft.yaml

@@ -0,0 +1,39 @@
+### model
+model_name_or_path: llava-hf/llava-1.5-7b-hf
+visual_inputs: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: mllm_demo
+template: vicuna
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llava1_5-7b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/merge_lora/llama3_gptq.yaml

@@ -0,0 +1,11 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+template: llama3
+
+### export
+export_dir: models/llama3_gptq
+export_quantization_bit: 4
+export_quantization_dataset: data/c4_demo.json
+export_size: 2
+export_device: cpu
+export_legacy_format: false

examples/merge_lora/llama3_lora_sft.yaml

@@ -0,0 +1,13 @@
+### Note: DO NOT use quantized model or quantization_bit when merging lora adapters
+
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+adapter_name_or_path: saves/llama3-8b/lora/sft
+template: llama3
+finetuning_type: lora
+
+### export
+export_dir: models/llama3_lora_sft
+export_size: 2
+export_device: cpu
+export_legacy_format: false

examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml

@@ -0,0 +1,38 @@
+### model
+model_name_or_path: ISTA-DASLab/Meta-Llama-3-8B-Instruct-AQLM-2Bit-1x16
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/qlora_single_gpu/llama3_lora_sft_awq.yaml

@@ -0,0 +1,38 @@
+### model
+model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-AWQ
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml

@@ -0,0 +1,39 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+quantization_bit: 4
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml

@@ -0,0 +1,38 @@
+### model
+model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-GPTQ
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 1024
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+fp16: true
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500

pyproject.toml

@@ -1,3 +1,33 @@
 [build-system]
 requires = ["setuptools>=61.0"]
 build-backend = "setuptools.build_meta"
+
+[tool.ruff]
+target-version = "py38"
+line-length = 119
+indent-width = 4
+
+[tool.ruff.lint]
+ignore = ["C408", "C901", "E501", "E731", "E741", "W605"]
+select = ["C", "E", "F", "I", "W"]
+
+[tool.ruff.lint.isort]
+lines-after-imports = 2
+known-first-party = ["llamafactory"]
+known-third-party = [
+    "accelerate",
+    "datasets",
+    "gradio",
+    "numpy",
+    "peft",
+    "torch",
+    "transformers",
+    "trl"
+]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+docstring-code-format = true
+skip-magic-trailing-comma = false
+line-ending = "auto"

requirements.txt

@@ -1,20 +1,19 @@
-torch>=1.13.1
-transformers>=4.31.0,<4.35.0
-datasets>=2.14.0
-accelerate>=0.21.0
-peft>=0.6.0
-trl==0.7.2
-gradio>=3.38.0,<4.0.0
+transformers>=4.41.2
+datasets>=2.16.0
+accelerate>=0.30.1
+peft>=0.11.1
+trl>=0.8.6
+gradio>=4.0.0
 scipy
+einops
 sentencepiece
-protobuf
 tiktoken
-fire
-jieba
-rouge-chinese
-nltk
+protobuf
 uvicorn
 pydantic
 fastapi
 sse-starlette
-matplotlib
+matplotlib>=3.7.0
+fire
+packaging
+pyyaml

scripts/cal_flops.py

@@ -0,0 +1,31 @@
+# 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/
+
+import fire
+import torch
+from deepspeed.accelerator import get_accelerator  # type: ignore
+from deepspeed.profiling.flops_profiler import get_model_profile  # type: ignore
+
+from llamafactory.chat import ChatModel
+
+
+def calculate_flops(
+    model_name_or_path: str,
+    batch_size: int = 1,
+    seq_length: int = 256,
+    flash_attn: str = "auto",
+):
+    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)
+        input_dict = {"input_ids": fake_input, "labels": fake_input.clone()}
+        flops, macs, params = get_model_profile(chat_model.model, kwargs=input_dict, print_profile=True, detailed=True)
+        print("FLOPs:", flops)
+        print("MACs:", macs)
+        print("Params:", params)
+
+
+if __name__ == "__main__":
+    fire.Fire(calculate_flops)

scripts/cal_lr.py

@@ -0,0 +1,76 @@
+# 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
+
+import math
+from typing import Literal
+
+import fire
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
+
+from llamafactory.data import get_dataset
+from llamafactory.extras.constants import IGNORE_INDEX
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_tokenizer
+
+
+BASE_LR = 3e-4  # 1.5e-4 for 30B-70B models
+BASE_BS = 4_000_000  # from llama paper
+
+
+def calculate_lr(
+    model_name_or_path: str,
+    batch_size: int,  # total batch size, namely (batch size * gradient accumulation * world size)
+    stage: Literal["pt", "sft"] = "sft",
+    dataset: str = "alpaca_en",
+    dataset_dir: str = "data",
+    template: str = "default",
+    cutoff_len: int = 1024,  # i.e. maximum input length during training
+    is_mistral: bool = False,  # mistral model uses a smaller learning rate,
+):
+    model_args, data_args, training_args, _, _ = get_train_args(
+        dict(
+            stage=stage,
+            model_name_or_path=model_name_or_path,
+            dataset=dataset,
+            dataset_dir=dataset_dir,
+            template=template,
+            cutoff_len=cutoff_len,
+            output_dir="dummy_dir",
+            overwrite_cache=True,
+        )
+    )
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
+    if stage == "pt":
+        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+    elif stage == "sft":
+        data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
+    else:
+        raise NotImplementedError
+
+    dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
+    valid_tokens, total_tokens = 0, 0
+    for batch in tqdm(dataloader):
+        valid_tokens += torch.sum(batch["labels"] != IGNORE_INDEX).item()
+        total_tokens += torch.numel(batch["labels"])
+
+    batch_max_len = cutoff_len * batch_size  # max tokens in a batch
+    valid_ratio = valid_tokens / total_tokens
+    batch_valid_len = batch_max_len * valid_ratio
+    lr = BASE_LR * math.sqrt(batch_valid_len / BASE_BS)  # lr ~ sqrt(batch_size)
+    lr = lr / 6.0 if is_mistral else lr
+    print(
+        "Optimal learning rate is {:.2e} for valid ratio% {:.2f} and effective batch size {:.2f}".format(
+            lr, valid_ratio * 100, batch_valid_len
+        )
+    )
+
+
+if __name__ == "__main__":
+    fire.Fire(calculate_lr)

scripts/cal_ppl.py

@@ -0,0 +1,116 @@
+# 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
+
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, Literal, Optional, Sequence
+
+import fire
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
+
+from llamafactory.data import get_dataset
+from llamafactory.extras.constants import IGNORE_INDEX
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_model, load_tokenizer
+
+
+@dataclass
+class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
+    r"""
+    Data collator for pairwise data.
+    """
+
+    train_on_prompt: bool = False
+
+    def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
+        r"""
+        Pads batched data to the longest sequence in the batch.
+
+        We generate 2 * n examples where the first n examples represent chosen examples and
+        the last n examples represent rejected examples.
+        """
+        chosen_features = []
+        for feature in features:
+            prompt_len, answer_len = len(feature["prompt_ids"]), len(feature["chosen_ids"])
+            input_ids = feature["prompt_ids"] + feature["chosen_ids"]
+            attention_mask = [1] * (prompt_len + answer_len)
+            labels = input_ids if self.train_on_prompt else [IGNORE_INDEX] * prompt_len + feature["chosen_ids"]
+            chosen_features.append({"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels})
+
+        return super().__call__(chosen_features)
+
+
+def cal_ppl(
+    model_name_or_path: str,
+    save_name: str,
+    batch_size: int = 4,
+    stage: Literal["pt", "sft", "rm"] = "sft",
+    dataset: str = "alpaca_en",
+    dataset_dir: str = "data",
+    template: str = "default",
+    cutoff_len: int = 1024,
+    max_samples: Optional[int] = None,
+    train_on_prompt: bool = False,
+):
+    model_args, data_args, training_args, finetuning_args, _ = get_train_args(
+        dict(
+            stage=stage,
+            model_name_or_path=model_name_or_path,
+            dataset=dataset,
+            dataset_dir=dataset_dir,
+            template=template,
+            cutoff_len=cutoff_len,
+            max_samples=max_samples,
+            train_on_prompt=train_on_prompt,
+            output_dir="dummy_dir",
+            overwrite_cache=True,
+        )
+    )
+    tokenizer_module = load_tokenizer(model_args)
+    tokenizer = tokenizer_module["tokenizer"]
+    trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
+    model = load_model(tokenizer, model_args, finetuning_args, is_trainable=False)
+    if stage == "pt":
+        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+    elif stage == "sft":
+        data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
+    elif stage == "rm":
+        data_collator = PairwiseDataCollatorWithPadding(
+            tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX, train_on_prompt=train_on_prompt
+        )
+    else:
+        raise NotImplementedError
+
+    dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
+    criterion = torch.nn.CrossEntropyLoss(reduction="none")
+    total_ppl = 0
+    perplexities = []
+    batch: Dict[str, "torch.Tensor"]
+    with torch.no_grad():
+        for batch in tqdm(dataloader):
+            batch = batch.to(model.device)
+            outputs = model(**batch)
+            shift_logits: "torch.Tensor" = outputs["logits"][..., :-1, :]
+            shift_labels: "torch.Tensor" = batch["labels"][..., 1:]
+            loss_mask = shift_labels != IGNORE_INDEX
+            flatten_logits = shift_logits.contiguous().view(shift_labels.size(0) * shift_labels.size(1), -1)
+            flatten_labels = shift_labels.contiguous().view(-1)
+            token_logps: "torch.Tensor" = criterion(flatten_logits, flatten_labels)
+            token_logps = token_logps.contiguous().view(shift_logits.size(0), -1)
+            sentence_logps = (token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
+            total_ppl += sentence_logps.exp().sum().item()
+            perplexities.extend(sentence_logps.exp().tolist())
+
+    with open(save_name, "w", encoding="utf-8") as f:
+        json.dump(perplexities, f, indent=2)
+
+    print("Average perplexity is {:.2f}".format(total_ppl / len(perplexities)))
+    print("Perplexities have been saved at {}.".format(save_name))
+
+
+if __name__ == "__main__":
+    fire.Fire(cal_ppl)

scripts/length_cdf.py

@@ -0,0 +1,51 @@
+# 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
+
+from collections import defaultdict
+
+import fire
+from tqdm import tqdm
+
+from llamafactory.data import get_dataset
+from llamafactory.hparams import get_train_args
+from llamafactory.model import load_tokenizer
+
+
+def length_cdf(
+    model_name_or_path: str,
+    dataset: str = "alpaca_en",
+    dataset_dir: str = "data",
+    template: str = "default",
+    interval: int = 1000,
+):
+    model_args, data_args, training_args, _, _ = get_train_args(
+        dict(
+            stage="sft",
+            model_name_or_path=model_name_or_path,
+            dataset=dataset,
+            dataset_dir=dataset_dir,
+            template=template,
+            cutoff_len=1_000_000,
+            output_dir="dummy_dir",
+            overwrite_cache=True,
+        )
+    )
+    tokenizer_module = load_tokenizer(model_args)
+    trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
+    total_num = len(trainset)
+    length_dict = defaultdict(int)
+    for sample in tqdm(trainset["input_ids"]):
+        length_dict[len(sample) // interval * interval] += 1
+
+    length_tuples = list(length_dict.items())
+    length_tuples.sort()
+    count_accu, prob_accu = 0, 0
+    for length, count in length_tuples:
+        count_accu += count
+        prob_accu += count / total_num * 100
+        print("{:d} ({:.2f}%) samples have length < {}.".format(count_accu, prob_accu, length + interval))
+
+
+if __name__ == "__main__":
+    fire.Fire(length_cdf)

scripts/llama_pro.py

@@ -0,0 +1,114 @@
+# 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
+
+import json
+import os
+from collections import OrderedDict
+from typing import TYPE_CHECKING, Optional
+
+import fire
+import torch
+from safetensors.torch import save_file
+from tqdm import tqdm
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+from transformers.modeling_utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+    shard_checkpoint,
+)
+
+
+if TYPE_CHECKING:
+    from transformers import PretrainedConfig, PreTrainedModel
+
+
+def change_name(name: str, old_index: int, new_index: int) -> str:
+    return name.replace(".{:d}.".format(old_index), ".{:d}.".format(new_index))
+
+
+def block_expansion(
+    model_name_or_path: str,
+    output_dir: str,
+    num_expand: int,
+    shard_size: Optional[str] = "2GB",
+    save_safetensors: Optional[bool] = False,
+):
+    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)
+    config.save_pretrained(output_dir)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+    tokenizer.save_pretrained(output_dir)
+
+    config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path)  # load the original one
+    if save_safetensors:
+        setattr(config, "tie_word_embeddings", False)  # safetensors does not allow shared weights
+
+    model: "PreTrainedModel" = AutoModelForCausalLM.from_pretrained(
+        model_name_or_path,
+        config=config,
+        torch_dtype="auto",
+        trust_remote_code=True,
+        low_cpu_mem_usage=True,
+    )
+    state_dict = model.state_dict()
+
+    if num_layers % num_expand != 0:
+        raise ValueError("`num_layers` {} should be divisible by `num_expand` {}.".format(num_layers, num_expand))
+
+    split = num_layers // num_expand
+    layer_cnt = 0
+    output_state_dict = OrderedDict()
+    for i in range(num_layers):
+        for key, value in state_dict.items():
+            if ".{:d}.".format(i) in key:
+                output_state_dict[change_name(key, i, layer_cnt)] = value
+
+        print("Add layer {} copied from layer {}".format(layer_cnt, i))
+        layer_cnt += 1
+        if (i + 1) % split == 0:
+            for key, value in state_dict.items():
+                if ".{:d}.".format(i) in key:
+                    if "down_proj" in key or "o_proj" in key:
+                        output_state_dict[change_name(key, i, layer_cnt)] = torch.zeros_like(value)
+                    else:
+                        output_state_dict[change_name(key, i, layer_cnt)] = torch.clone(value)
+
+            print("Add layer {} expanded from layer {}".format(layer_cnt, i))
+            layer_cnt += 1
+
+    for key, value in state_dict.items():
+        if key not in output_state_dict:
+            output_state_dict[key] = value
+
+    weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
+    shards, index = shard_checkpoint(output_state_dict, max_shard_size=shard_size, weights_name=weights_name)
+
+    for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
+        if save_safetensors:
+            save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
+        else:
+            torch.save(shard, os.path.join(output_dir, shard_file))
+
+    if index is None:
+        print("Model weights saved in {}".format(os.path.join(output_dir, weights_name)))
+    else:
+        index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
+        with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
+            json.dump(index, f, indent=2, sort_keys=True)
+        print("Model weights saved in {}".format(output_dir))
+
+    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))
+    print("use_llama_pro: true")
+
+
+if __name__ == "__main__":
+    fire.Fire(block_expansion)

scripts/llamafy_baichuan2.py

@@ -1,60 +1,68 @@
 # 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 --shard_size 10GB
+# 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
 
-import os
-import fire
 import json
-import torch
+import os
 from collections import OrderedDict
-from transformers.modeling_utils import shard_checkpoint, WEIGHTS_NAME, WEIGHTS_INDEX_NAME
-from typing import Any, Dict
+from typing import Any, Dict, Optional
+
+import fire
+import torch
+from safetensors.torch import save_file
+from tqdm import tqdm
+from transformers.modeling_utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+    shard_checkpoint,
+)
 
 
 CONFIG_NAME = "config.json"
 
 
-def save_weight(
-    input_dir: str,
-    output_dir: str,
-    shard_size: str
-):
+def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetensors: bool):
     baichuan2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
-    for filepath in os.listdir(input_dir):
+    for filepath in tqdm(os.listdir(input_dir), desc="Load weights"):
         if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".bin"):
             shard_weight = torch.load(os.path.join(input_dir, filepath), map_location="cpu")
             baichuan2_state_dict.update(shard_weight)
 
     llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
-    for key, value in baichuan2_state_dict.items():
+    for key, value in tqdm(baichuan2_state_dict.items(), desc="Convert format"):
         if "W_pack" in key:
             proj_size = value.size(0) // 3
             llama2_state_dict[key.replace("W_pack", "q_proj")] = value[:proj_size, :]
-            llama2_state_dict[key.replace("W_pack", "k_proj")] = value[proj_size:2*proj_size, :]
-            llama2_state_dict[key.replace("W_pack", "v_proj")] = value[2*proj_size:, :]
+            llama2_state_dict[key.replace("W_pack", "k_proj")] = value[proj_size : 2 * proj_size, :]
+            llama2_state_dict[key.replace("W_pack", "v_proj")] = value[2 * proj_size :, :]
         elif "lm_head" in key:
             llama2_state_dict[key] = torch.nn.functional.normalize(value)
         else:
             llama2_state_dict[key] = value
 
-    shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=WEIGHTS_NAME)
-    for shard_file, shard in shards.items():
-        torch.save(shard, os.path.join(output_dir, shard_file))
-    
+    weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
+    shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
+
+    for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
+        if save_safetensors:
+            save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
+        else:
+            torch.save(shard, os.path.join(output_dir, shard_file))
+
     if index is None:
         print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
     else:
-        with open(os.path.join(output_dir, WEIGHTS_INDEX_NAME), "w", encoding="utf-8") as f:
+        index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
+        with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
             json.dump(index, f, indent=2, sort_keys=True)
         print("Model weights saved in {}".format(output_dir))
 
 
-def save_config(
-    input_dir: str,
-    output_dir: str
-):
+def save_config(input_dir: str, output_dir: str):
     with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
         llama2_config_dict: Dict[str, Any] = json.load(f)
 
@@ -69,17 +77,15 @@ def save_config(
 
 
 def llamafy_baichuan2(
-    input_dir: str,
-    output_dir: str,
-    shard_size: str
+    input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
 ):
     try:
         os.makedirs(output_dir, exist_ok=False)
     except Exception as e:
         raise print("Output dir already exists", e)
 
-    save_weight(input_dir, output_dir, shard_size)
-    save_config(input_dir, output_dir)    
+    save_weight(input_dir, output_dir, shard_size, save_safetensors)
+    save_config(input_dir, output_dir)
 
 
 if __name__ == "__main__":

scripts/llamafy_qwen.py

@@ -1,33 +1,40 @@
 # coding=utf-8
 # Converts the Qwen models in the same format as LLaMA2.
-# Usage: python llamafy_qwen.py --input_dir input --output_dir output --shard_size 10GB
+# Usage: python llamafy_qwen.py --input_dir input --output_dir output
+# Converted model: https://huggingface.co/hiyouga/Qwen-14B-Chat-LLaMAfied
 
-import os
-import fire
 import json
-import torch
+import os
 from collections import OrderedDict
+from typing import Any, Dict, Optional
+
+import fire
+import torch
 from safetensors import safe_open
-from transformers.modeling_utils import shard_checkpoint, WEIGHTS_NAME, WEIGHTS_INDEX_NAME
+from safetensors.torch import save_file
+from tqdm import tqdm
+from transformers.modeling_utils import (
+    SAFE_WEIGHTS_INDEX_NAME,
+    SAFE_WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+    shard_checkpoint,
+)
 from transformers.utils import check_min_version
-from typing import Any, Dict
+
 
 try:
     check_min_version("4.34.0")
-except:
+except Exception:
     raise ValueError("Please upgrade `transformers` to 4.34.0")
 
 
 CONFIG_NAME = "config.json"
 
 
-def save_weight(
-    input_dir: str,
-    output_dir: str,
-    shard_size: str
-) -> str:
+def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetensors: bool) -> str:
     qwen_state_dict: Dict[str, torch.Tensor] = OrderedDict()
-    for filepath in os.listdir(input_dir):
+    for filepath in tqdm(os.listdir(input_dir), desc="Load weights"):
         if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".safetensors"):
             with safe_open(os.path.join(input_dir, filepath), framework="pt", device="cpu") as f:
                 for key in f.keys():
@@ -35,7 +42,7 @@ def save_weight(
 
     llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
     torch_dtype = None
-    for key, value in qwen_state_dict.items():
+    for key, value in tqdm(qwen_state_dict.items(), desc="Convert format"):
         if torch_dtype is None:
             torch_dtype = value.dtype
         if "wte" in key:
@@ -47,13 +54,15 @@ def save_weight(
             if "attn.c_attn" in key:
                 proj_size = value.size(0) // 3
                 llama2_state_dict[key.replace("attn.c_attn", "self_attn.q_proj")] = value[:proj_size, ...]
-                llama2_state_dict[key.replace("attn.c_attn", "self_attn.k_proj")] = value[proj_size:2*proj_size, ...]
-                llama2_state_dict[key.replace("attn.c_attn", "self_attn.v_proj")] = value[2*proj_size:, ...]
+                llama2_state_dict[key.replace("attn.c_attn", "self_attn.k_proj")] = value[
+                    proj_size : 2 * proj_size, ...
+                ]
+                llama2_state_dict[key.replace("attn.c_attn", "self_attn.v_proj")] = value[2 * proj_size :, ...]
             elif "attn.c_proj" in key:
                 llama2_state_dict[key.replace("attn.c_proj", "self_attn.o_proj")] = value
-                llama2_state_dict[key.replace("attn.c_proj.weight", "self_attn.o_proj.bias")] = (
-                    torch.zeros_like(value[:, 0]).squeeze()
-                )
+                llama2_state_dict[key.replace("attn.c_proj.weight", "self_attn.o_proj.bias")] = torch.zeros_like(
+                    value[:, 0]
+                ).squeeze()
             elif "ln_1" in key:
                 llama2_state_dict[key.replace("ln_1", "input_layernorm")] = value
             elif "ln_2" in key:
@@ -69,25 +78,27 @@ def save_weight(
             else:
                 raise KeyError("Unable to process key {}".format(key))
 
-    shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=WEIGHTS_NAME)
-    for shard_file, shard in shards.items():
-        torch.save(shard, os.path.join(output_dir, shard_file))
+    weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
+    shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
+
+    for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
+        if save_safetensors:
+            save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
+        else:
+            torch.save(shard, os.path.join(output_dir, shard_file))
 
     if index is None:
-        print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
+        print("Model weights saved in {}".format(os.path.join(output_dir, weights_name)))
     else:
-        with open(os.path.join(output_dir, WEIGHTS_INDEX_NAME), "w", encoding="utf-8") as f:
+        index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
+        with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
             json.dump(index, f, indent=2, sort_keys=True)
         print("Model weights saved in {}".format(output_dir))
 
     return str(torch_dtype).replace("torch.", "")
 
 
-def save_config(
-    input_dir: str,
-    output_dir: str,
-    torch_dtype: str
-):
+def save_config(input_dir: str, output_dir: str, torch_dtype: str):
     with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
         qwen_config_dict: Dict[str, Any] = json.load(f)
 
@@ -118,17 +129,15 @@ def save_config(
 
 
 def llamafy_qwen(
-    input_dir: str,
-    output_dir: str,
-    shard_size: str
+    input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
 ):
     try:
         os.makedirs(output_dir, exist_ok=False)
     except Exception as e:
         raise print("Output dir already exists", e)
 
-    torch_dtype = save_weight(input_dir, output_dir, shard_size)
-    save_config(input_dir, output_dir, torch_dtype)    
+    torch_dtype = save_weight(input_dir, output_dir, shard_size, save_safetensors)
+    save_config(input_dir, output_dir, torch_dtype)
 
 
 if __name__ == "__main__":

scripts/loftq_init.py

@@ -0,0 +1,82 @@
+# 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
+
+import os
+from typing import TYPE_CHECKING, Optional
+
+import fire
+import torch
+import torch.nn as nn
+from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+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,
+):
+    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)
+    lora_config = LoraConfig(
+        task_type=TaskType.CAUSAL_LM,
+        inference_mode=True,
+        r=lora_rank,
+        lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
+        lora_dropout=0.1,
+        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()
+
+    # 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)
+
+    # Save base model
+    unwrap_model(base_model)
+    base_model.save_pretrained(save_dir, safe_serialization=save_safetensors)
+    tokenizer.save_pretrained(save_dir)
+
+
+if __name__ == "__main__":
+    fire.Fire(quantize_loftq)

scripts/test_toolcall.py

@@ -0,0 +1,64 @@
+import json
+import os
+from typing import Sequence
+
+from openai import OpenAI
+from transformers.utils.versions import require_version
+
+
+require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
+
+
+def calculate_gpa(grades: Sequence[str], hours: Sequence[int]) -> float:
+    grade_to_score = {"A": 4, "B": 3, "C": 2}
+    total_score, total_hour = 0, 0
+    for grade, hour in zip(grades, hours):
+        total_score += grade_to_score[grade] * hour
+        total_hour += hour
+    return round(total_score / total_hour, 2)
+
+
+def main():
+    client = OpenAI(
+        api_key="{}".format(os.environ.get("API_KEY", "0")),
+        base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
+    )
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "calculate_gpa",
+                "description": "Calculate the Grade Point Average (GPA) based on grades and credit hours",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "grades": {"type": "array", "items": {"type": "string"}, "description": "The grades"},
+                        "hours": {"type": "array", "items": {"type": "integer"}, "description": "The credit hours"},
+                    },
+                    "required": ["grades", "hours"],
+                },
+            },
+        }
+    ]
+    tool_map = {"calculate_gpa": calculate_gpa}
+
+    messages = []
+    messages.append({"role": "user", "content": "My grades are A, A, B, and C. The credit hours are 3, 4, 3, and 2."})
+    result = client.chat.completions.create(messages=messages, model="test", tools=tools)
+    if result.choices[0].message.tool_calls is None:
+        raise ValueError("Cannot retrieve function call from the response.")
+
+    messages.append(result.choices[0].message)
+    tool_call = result.choices[0].message.tool_calls[0].function
+    print(tool_call)
+    # Function(arguments='{"grades": ["A", "A", "B", "C"], "hours": [3, 4, 3, 2]}', name='calculate_gpa')
+    name, arguments = tool_call.name, json.loads(tool_call.arguments)
+    tool_result = tool_map[name](**arguments)
+    messages.append({"role": "tool", "content": json.dumps({"gpa": tool_result}, ensure_ascii=False)})
+    result = client.chat.completions.create(messages=messages, model="test", tools=tools)
+    print(result.choices[0].message.content)
+    # Based on the grades and credit hours you provided, your Grade Point Average (GPA) is 3.42.
+
+
+if __name__ == "__main__":
+    main()

setup.py

@@ -1,13 +1,14 @@
 import os
 import re
-from setuptools import setup, find_packages
+
+from setuptools import find_packages, setup
 
 
 def get_version():
-    with open(os.path.join("src", "llmtuner", "__init__.py"), "r", encoding="utf-8") as f:
+    with open(os.path.join("src", "llamafactory", "extras", "env.py"), "r", encoding="utf-8") as f:
         file_content = f.read()
-        pattern = r"{0}\W*=\W*\"([^\"]+)\"".format("__version__")
-        version, = re.findall(pattern, file_content)
+        pattern = r"{}\W*=\W*\"([^\"]+)\"".format("VERSION")
+        (version,) = re.findall(pattern, file_content)
         return version
 
 
@@ -18,10 +19,27 @@ def get_requires():
         return lines
 
 
-def main():
+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"],
+    "bitsandbytes": ["bitsandbytes>=0.39.0"],
+    "vllm": ["vllm>=0.4.3"],
+    "galore": ["galore-torch"],
+    "badam": ["badam"],
+    "gptq": ["optimum>=1.16.0", "auto-gptq>=0.5.0"],
+    "awq": ["autoawq"],
+    "aqlm": ["aqlm[gpu]>=1.1.0"],
+    "qwen": ["transformers_stream_generator"],
+    "modelscope": ["modelscope"],
+    "dev": ["ruff", "pytest"],
+}
 
+
+def main():
     setup(
-        name="llmtuner",
+        name="llamafactory",
         version=get_version(),
         author="hiyouga",
         author_email="hiyouga" "@" "buaa.edu.cn",
@@ -35,8 +53,10 @@ def main():
         packages=find_packages("src"),
         python_requires=">=3.8.0",
         install_requires=get_requires(),
+        extras_require=extra_require,
+        entry_points={"console_scripts": ["llamafactory-cli = llamafactory.cli:main"]},
         classifiers=[
-            "Development Status :: 3 - Alpha",
+            "Development Status :: 4 - Beta",
             "Intended Audience :: Developers",
             "Intended Audience :: Education",
             "Intended Audience :: Science/Research",
@@ -46,8 +66,9 @@ def main():
             "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",
             "Programming Language :: Python :: 3.10",
+            "Programming Language :: Python :: 3.11",
             "Topic :: Scientific/Engineering :: Artificial Intelligence",
-        ]
+        ],
     )
 
 

src/api.py

@@ -0,0 +1,19 @@
+import os
+
+import uvicorn
+
+from llamafactory.api.app import create_app
+from llamafactory.chat import ChatModel
+
+
+def main():
+    chat_model = ChatModel()
+    app = create_app(chat_model)
+    api_host = os.environ.get("API_HOST", "0.0.0.0")
+    api_port = int(os.environ.get("API_PORT", "8000"))
+    print("Visit http://localhost:{}/docs for API document.".format(api_port))
+    uvicorn.run(app, host=api_host, port=api_port)
+
+
+if __name__ == "__main__":
+    main()

src/api_demo.py

@@ -1,14 +0,0 @@
-import uvicorn
-
-from llmtuner import ChatModel, create_app
-
-
-def main():
-    chat_model = ChatModel()
-    app = create_app(chat_model)
-    print("Visit http://localhost:8000/docs for API document.")
-    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
-
-
-if __name__ == "__main__":
-    main()

src/cli_demo.py

@@ -1,39 +0,0 @@
-import readline
-from llmtuner import ChatModel
-
-
-def main():
-    chat_model = ChatModel()
-    history = []
-    print("Welcome to the CLI application, use `clear` to remove the history, use `exit` to exit the application.")
-
-    while True:
-        try:
-            query = input("\nUser: ")
-        except UnicodeDecodeError:
-            print("Detected decoding error at the inputs, please set the terminal encoding to utf-8.")
-            continue
-        except Exception:
-            raise
-
-        if query.strip() == "exit":
-            break
-
-        if query.strip() == "clear":
-            history = []
-            print("History has been removed.")
-            continue
-
-        print("Assistant: ", end="", flush=True)
-
-        response = ""
-        for new_text in chat_model.stream_chat(query, history):
-            print(new_text, end="", flush=True)
-            response += new_text
-        print()
-
-        history = history + [(query, response)]
-
-
-if __name__ == "__main__":
-    main()

src/evaluate.py

@@ -1,190 +0,0 @@
-# coding=utf-8
-# Evaluates the performance of pre-trained models.
-# Usage: python evaluate.py --model_name_or_path path_to_model --checkpoint_dir path_to_ckpt --template vanilla
-#                           --task ceval --split validation --lang zh --n_shot 5 --batch_size 4 --save_name result
-# Inspired by: https://github.com/hendrycks/test/blob/master/evaluate_flan.py
-
-import os
-import fire
-import json
-import torch
-import numpy as np
-import transformers
-from collections import Counter
-from datasets import load_dataset
-from dataclasses import dataclass
-from tqdm import tqdm, trange
-from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple
-
-from llmtuner import ChatModel
-
-if TYPE_CHECKING:
-    from datasets import Dataset
-
-
-choices = ["A", "B", "C", "D"]
-
-
-@dataclass
-class EvalTemplate:
-
-    system: str
-    choice: str
-    answer: str
-    prefix: str
-
-    def parse_example(
-        self,
-        example: Dict[str, str]
-    ) -> Tuple[str, str]:
-        candidates = [self.choice.format(choice=ch, content=example[ch]) for ch in choices if ch in example]
-        return "".join([example["question"]] + candidates + [self.answer]), example["answer"]
-
-    def format_example(
-        self,
-        target_data: Dict[str, str],
-        support_set: "Dataset",
-        subject_name: str,
-        use_history: bool
-    ) -> Tuple[str, str, List[Tuple[str, str]]]:
-        query, resp = self.parse_example(target_data)
-        history = [self.parse_example(support_set[k]) for k in range(len(support_set))]
-
-        if len(history):
-            temp = history.pop(0)
-            history.insert(0, (self.system.format(subject=subject_name) + temp[0], temp[1]))
-        else:
-            query = self.system.format(subject=subject_name) + query
-
-        if not use_history:
-            query = "\n\n".join(["".join(item) for item in history] + [query])
-            history = []
-        return query.strip(), resp, history
-
-
-eval_templates = {
-    "en": EvalTemplate(
-        system="The following are multiple choice questions (with answers) about {subject}.\n\n",
-        choice="\n{choice}. {content}",
-        answer="\nAnswer: ",
-        prefix=" "
-    ),
-    "zh": EvalTemplate(
-        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="\n"
-    )
-}
-
-
-@torch.inference_mode()
-def batch_inference(
-    chat_model: ChatModel,
-    batch_input: Dict[str, torch.Tensor],
-    prefix_char: str
-) -> List[str]:
-    logits = chat_model.model(**batch_input).logits
-    lengths = torch.sum(batch_input["attention_mask"], dim=-1)
-    nextword_logits = torch.stack([logits[i, lengths[i] - 1] for i in range(len(lengths))], dim=0)
-    probs = torch.nn.functional.softmax(
-        torch.stack(
-            [
-                nextword_logits[:, chat_model.tokenizer.encode(prefix_char + choice, add_special_tokens=False)[-1]]
-                for choice in choices
-            ],
-            dim=-1
-        ),
-        dim=-1
-    ).detach()
-    return [chr(ord("A") + offset.item()) for offset in torch.argmax(probs, dim=-1)]
-
-
-def evaluate(
-    model_name_or_path: str,
-    finetuning_type: Optional[str] = "lora",
-    checkpoint_dir: Optional[str] = None,
-    template: Optional[str] = "vanilla",
-    task: Optional[str] = "ceval",
-    dataset_dir: Optional[str] = "evaluation",
-    split: Optional[Literal["validation", "test"]] = "validation",
-    lang: Optional[Literal["zh", "en"]] = "zh",
-    n_shot: Optional[int] = 5,
-    n_avg: Optional[int] = 1,
-    batch_size: Optional[int] = 4,
-    save_name: Optional[str] = None,
-    seed: Optional[int] = 42
-):
-    with open(os.path.join(dataset_dir, task, "mapping.json"), "r", encoding="utf-8") as f:
-        categorys: Dict[str, Dict[str, str]] = json.load(f)
-
-    transformers.set_seed(seed)
-    chat_model = ChatModel(dict(
-        model_name_or_path=model_name_or_path,
-        finetuning_type=finetuning_type,
-        checkpoint_dir=checkpoint_dir,
-        template=template
-    ))
-    chat_model.tokenizer.padding_side = "right" # avoid overflow issue in batched inference for llama2
-    eval_template = eval_templates[lang]
-
-    category_corrects: Dict[str, np.ndarray] = {
-        subj: np.array([], dtype="bool") for subj in ["Average", "STEM", "Social Sciences", "Humanities", "Other"]
-    }
-    pbar = tqdm(categorys.keys(), desc="Processing subjects", position=0)
-    results = {}
-    for subject in pbar:
-        dataset = load_dataset(os.path.join(dataset_dir, task), subject)
-        labels, answers, all_outputs = [], [], []
-        for epoch in range(n_avg):
-            pbar.set_postfix_str("{} Trial: {}".format(categorys[subject]["name"], epoch))
-            inputs, outputs = [], []
-            for i in trange(len(dataset[split]), desc="Formatting batches", position=1, leave=False):
-                support_set = dataset["train"].shuffle().select(range(min(n_shot, len(dataset["train"]))))
-                query, resp, history = eval_template.format_example(
-                    target_data=dataset[split][i],
-                    support_set=support_set,
-                    subject_name=categorys[subject]["name"],
-                    use_history=chat_model.template.use_history
-                )
-                input_ids, _ = chat_model.template.encode_oneturn(
-                    tokenizer=chat_model.tokenizer, query=query, resp=resp, history=history
-                )
-                inputs.append({"input_ids": input_ids, "attention_mask": [1] * len(input_ids)})
-                if epoch == 0:
-                    labels.append(resp)
-
-            for i in trange(0, len(inputs), batch_size, desc="Predicting batches", position=1, leave=False):
-                batch_input = chat_model.tokenizer.pad(
-                    inputs[i : i + batch_size], return_attention_mask=True, return_tensors="pt"
-                ).to(chat_model.model.device)
-                preds = batch_inference(chat_model, batch_input, eval_template.prefix)
-                outputs += preds
-            all_outputs.append(outputs)
-
-        for i in range(len(all_outputs[0])):
-            count = Counter([all_outputs[epoch][i] for epoch in range(n_avg)])
-            answers.append(count.most_common(1)[0][0])
-
-        corrects = (np.array(answers) == np.array(labels))
-        category_name = categorys[subject]["category"]
-        category_corrects[category_name] = np.concatenate([category_corrects[category_name], corrects], axis=0)
-        category_corrects["Average"] = np.concatenate([category_corrects["Average"], corrects], axis=0)
-        results[subject] = {str(i): answers[i] for i in range(len(answers))}
-
-    score_info = "\n".join([
-        "{:>15}: {:.2f}".format(category_name, 100 * np.mean(category_correct))
-        for category_name, category_correct in category_corrects.items() if len(category_correct)
-    ])
-
-    print(score_info)
-    if save_name is not None:
-        with open(save_name + ".json", "w", encoding="utf-8", newline="\n") as f:
-            json.dump(results, f, indent=2)
-
-        with open(save_name + ".log", "w", encoding="utf-8", newline="\n") as f:
-            f.write(score_info)
-
-
-if __name__ == "__main__":
-    fire.Fire(evaluate)

src/export_model.py

@@ -1,9 +0,0 @@
-from llmtuner import export_model
-
-
-def main():
-    export_model()
-
-
-if __name__ == "__main__":
-    main()

src/llamafactory/__init__.py

@@ -0,0 +1,6 @@
+# Level: api, webui > chat, eval, train > data, model > hparams > extras
+
+from .cli import VERSION
+
+
+__version__ = VERSION

src/llamafactory/api/app.py

@@ -0,0 +1,108 @@
+import os
+from contextlib import asynccontextmanager
+from typing import Optional
+
+from typing_extensions import Annotated
+
+from ..chat import ChatModel
+from ..extras.misc import torch_gc
+from ..extras.packages import is_fastapi_available, is_starlette_available, is_uvicorn_available
+from .chat import (
+    create_chat_completion_response,
+    create_score_evaluation_response,
+    create_stream_chat_completion_response,
+)
+from .protocol import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ModelCard,
+    ModelList,
+    ScoreEvaluationRequest,
+    ScoreEvaluationResponse,
+)
+
+
+if is_fastapi_available():
+    from fastapi import Depends, FastAPI, HTTPException, status
+    from fastapi.middleware.cors import CORSMiddleware
+    from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer
+
+
+if is_starlette_available():
+    from sse_starlette import EventSourceResponse
+
+
+if is_uvicorn_available():
+    import uvicorn
+
+
+@asynccontextmanager
+async def lifespan(app: "FastAPI"):  # collects GPU memory
+    yield
+    torch_gc()
+
+
+def create_app(chat_model: "ChatModel") -> "FastAPI":
+    app = FastAPI(lifespan=lifespan)
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+    api_key = os.environ.get("API_KEY")
+    security = HTTPBearer(auto_error=False)
+
+    async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
+        if api_key and (auth is None or auth.credentials != api_key):
+            raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key.")
+
+    @app.get(
+        "/v1/models",
+        response_model=ModelList,
+        status_code=status.HTTP_200_OK,
+        dependencies=[Depends(verify_api_key)],
+    )
+    async def list_models():
+        model_card = ModelCard(id="gpt-3.5-turbo")
+        return ModelList(data=[model_card])
+
+    @app.post(
+        "/v1/chat/completions",
+        response_model=ChatCompletionResponse,
+        status_code=status.HTTP_200_OK,
+        dependencies=[Depends(verify_api_key)],
+    )
+    async def create_chat_completion(request: ChatCompletionRequest):
+        if not chat_model.engine.can_generate:
+            raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
+
+        if request.stream:
+            generate = create_stream_chat_completion_response(request, chat_model)
+            return EventSourceResponse(generate, media_type="text/event-stream")
+        else:
+            return await create_chat_completion_response(request, chat_model)
+
+    @app.post(
+        "/v1/score/evaluation",
+        response_model=ScoreEvaluationResponse,
+        status_code=status.HTTP_200_OK,
+        dependencies=[Depends(verify_api_key)],
+    )
+    async def create_score_evaluation(request: ScoreEvaluationRequest):
+        if chat_model.engine.can_generate:
+            raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
+
+        return await create_score_evaluation_response(request, chat_model)
+
+    return app
+
+
+def run_api() -> None:
+    chat_model = ChatModel()
+    app = create_app(chat_model)
+    api_host = os.environ.get("API_HOST", "0.0.0.0")
+    api_port = int(os.environ.get("API_PORT", "8000"))
+    print("Visit http://localhost:{}/docs for API document.".format(api_port))
+    uvicorn.run(app, host=api_host, port=api_port)

src/llamafactory/api/chat.py

@@ -0,0 +1,219 @@
+import base64
+import io
+import json
+import os
+import uuid
+from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
+
+from ..data import Role as DataRole
+from ..extras.logging import get_logger
+from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
+from .common import dictify, jsonify
+from .protocol import (
+    ChatCompletionMessage,
+    ChatCompletionResponse,
+    ChatCompletionResponseChoice,
+    ChatCompletionResponseUsage,
+    ChatCompletionStreamResponse,
+    ChatCompletionStreamResponseChoice,
+    Finish,
+    Function,
+    FunctionCall,
+    Role,
+    ScoreEvaluationResponse,
+)
+
+
+if is_fastapi_available():
+    from fastapi import HTTPException, status
+
+
+if is_pillow_available():
+    from PIL import Image
+
+
+if is_requests_available():
+    import requests
+
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+    from ..chat import ChatModel
+    from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
+
+
+logger = get_logger(__name__)
+ROLE_MAPPING = {
+    Role.USER: DataRole.USER.value,
+    Role.ASSISTANT: DataRole.ASSISTANT.value,
+    Role.SYSTEM: DataRole.SYSTEM.value,
+    Role.FUNCTION: DataRole.FUNCTION.value,
+    Role.TOOL: DataRole.OBSERVATION.value,
+}
+
+
+def _process_request(
+    request: "ChatCompletionRequest",
+) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional["NDArray"]]:
+    logger.info("==== request ====\n{}".format(json.dumps(dictify(request), indent=2, ensure_ascii=False)))
+
+    if len(request.messages) == 0:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
+
+    if request.messages[0].role == Role.SYSTEM:
+        system = request.messages.pop(0).content
+    else:
+        system = None
+
+    if len(request.messages) % 2 == 0:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
+
+    input_messages = []
+    image = None
+    for i, message in enumerate(request.messages):
+        if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
+        elif i % 2 == 1 and message.role not in [Role.ASSISTANT, Role.FUNCTION]:
+            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)
+            input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
+        elif isinstance(message.content, list):
+            for input_item in message.content:
+                if input_item.type == "text":
+                    input_messages.append({"role": ROLE_MAPPING[message.role], "content": input_item.text})
+                else:
+                    image_url = input_item.image_url.url
+                    if image_url.startswith("data:image"):  # base64 image
+                        image_data = base64.b64decode(image_url.split(",", maxsplit=1)[1])
+                        image_path = io.BytesIO(image_data)
+                    elif os.path.isfile(image_url):  # local file
+                        image_path = open(image_url, "rb")
+                    else:  # web uri
+                        image_path = requests.get(image_url, stream=True).raw
+
+                    image = Image.open(image_path).convert("RGB")
+        else:
+            input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
+
+    tool_list = request.tools
+    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:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
+    else:
+        tools = None
+
+    return input_messages, system, tools, image
+
+
+def _create_stream_chat_completion_chunk(
+    completion_id: str,
+    model: str,
+    delta: "ChatCompletionMessage",
+    index: Optional[int] = 0,
+    finish_reason: Optional["Finish"] = None,
+) -> str:
+    choice_data = ChatCompletionStreamResponseChoice(index=index, delta=delta, finish_reason=finish_reason)
+    chunk = ChatCompletionStreamResponse(id=completion_id, model=model, choices=[choice_data])
+    return jsonify(chunk)
+
+
+async def create_chat_completion_response(
+    request: "ChatCompletionRequest", chat_model: "ChatModel"
+) -> "ChatCompletionResponse":
+    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+    input_messages, system, tools, image = _process_request(request)
+    responses = await chat_model.achat(
+        input_messages,
+        system,
+        tools,
+        image,
+        do_sample=request.do_sample,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        max_new_tokens=request.max_tokens,
+        num_return_sequences=request.n,
+        stop=request.stop,
+    )
+
+    prompt_length, response_length = 0, 0
+    choices = []
+    for i, response in enumerate(responses):
+        if tools:
+            result = chat_model.engine.template.format_tools.extract(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])
+            finish_reason = Finish.TOOL
+        else:
+            response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)
+            finish_reason = Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
+
+        choices.append(ChatCompletionResponseChoice(index=i, message=response_message, finish_reason=finish_reason))
+        prompt_length = response.prompt_length
+        response_length += response.response_length
+
+    usage = ChatCompletionResponseUsage(
+        prompt_tokens=prompt_length,
+        completion_tokens=response_length,
+        total_tokens=prompt_length + response_length,
+    )
+
+    return ChatCompletionResponse(id=completion_id, model=request.model, choices=choices, usage=usage)
+
+
+async def create_stream_chat_completion_response(
+    request: "ChatCompletionRequest", chat_model: "ChatModel"
+) -> AsyncGenerator[str, None]:
+    completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+    input_messages, system, tools, image = _process_request(request)
+    if tools:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
+
+    if request.n > 1:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream multiple responses.")
+
+    yield _create_stream_chat_completion_chunk(
+        completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(role=Role.ASSISTANT, content="")
+    )
+    async for new_token in chat_model.astream_chat(
+        input_messages,
+        system,
+        tools,
+        image,
+        do_sample=request.do_sample,
+        temperature=request.temperature,
+        top_p=request.top_p,
+        max_new_tokens=request.max_tokens,
+        stop=request.stop,
+    ):
+        if len(new_token) != 0:
+            yield _create_stream_chat_completion_chunk(
+                completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(content=new_token)
+            )
+
+    yield _create_stream_chat_completion_chunk(
+        completion_id=completion_id, model=request.model, delta=ChatCompletionMessage(), finish_reason=Finish.STOP
+    )
+    yield "[DONE]"
+
+
+async def create_score_evaluation_response(
+    request: "ScoreEvaluationRequest", chat_model: "ChatModel"
+) -> "ScoreEvaluationResponse":
+    if len(request.messages) == 0:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
+
+    scores = await chat_model.aget_scores(request.messages, max_length=request.max_length)
+    return ScoreEvaluationResponse(model=request.model, scores=scores)

src/llamafactory/api/common.py

@@ -0,0 +1,20 @@
+import json
+from typing import TYPE_CHECKING, Any, Dict
+
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+
+
+def dictify(data: "BaseModel") -> Dict[str, Any]:
+    try:  # pydantic v2
+        return data.model_dump(exclude_unset=True)
+    except AttributeError:  # pydantic v1
+        return data.dict(exclude_unset=True)
+
+
+def jsonify(data: "BaseModel") -> str:
+    try:  # pydantic v2
+        return json.dumps(data.model_dump(exclude_unset=True), ensure_ascii=False)
+    except AttributeError:  # pydantic v1
+        return data.json(exclude_unset=True, ensure_ascii=False)

src/llamafactory/api/protocol.py

@@ -0,0 +1,139 @@
+import time
+from enum import Enum, unique
+from typing import Any, Dict, List, Optional, Union
+
+from pydantic import BaseModel, Field
+from typing_extensions import Literal
+
+
+@unique
+class Role(str, Enum):
+    USER = "user"
+    ASSISTANT = "assistant"
+    SYSTEM = "system"
+    FUNCTION = "function"
+    TOOL = "tool"
+
+
+@unique
+class Finish(str, Enum):
+    STOP = "stop"
+    LENGTH = "length"
+    TOOL = "tool_calls"
+
+
+class ModelCard(BaseModel):
+    id: str
+    object: Literal["model"] = "model"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    owned_by: Literal["owner"] = "owner"
+
+
+class ModelList(BaseModel):
+    object: Literal["list"] = "list"
+    data: List[ModelCard] = []
+
+
+class Function(BaseModel):
+    name: str
+    arguments: str
+
+
+class FunctionDefinition(BaseModel):
+    name: str
+    description: str
+    parameters: Dict[str, Any]
+
+
+class FunctionAvailable(BaseModel):
+    type: Literal["function", "code_interpreter"] = "function"
+    function: Optional[FunctionDefinition] = None
+
+
+class FunctionCall(BaseModel):
+    id: str
+    type: Literal["function"] = "function"
+    function: Function
+
+
+class ImageURL(BaseModel):
+    url: str
+
+
+class MultimodalInputItem(BaseModel):
+    type: Literal["text", "image_url"]
+    text: Optional[str] = None
+    image_url: Optional[ImageURL] = None
+
+
+class ChatMessage(BaseModel):
+    role: Role
+    content: Optional[Union[str, List[MultimodalInputItem]]] = None
+    tool_calls: Optional[List[FunctionCall]] = None
+
+
+class ChatCompletionMessage(BaseModel):
+    role: Optional[Role] = None
+    content: Optional[str] = None
+    tool_calls: Optional[List[FunctionCall]] = None
+
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: List[ChatMessage]
+    tools: Optional[List[FunctionAvailable]] = None
+    do_sample: bool = True
+    temperature: Optional[float] = None
+    top_p: Optional[float] = None
+    n: int = 1
+    max_tokens: Optional[int] = None
+    stop: Optional[Union[str, List[str]]] = None
+    stream: bool = False
+
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatCompletionMessage
+    finish_reason: Finish
+
+
+class ChatCompletionStreamResponseChoice(BaseModel):
+    index: int
+    delta: ChatCompletionMessage
+    finish_reason: Optional[Finish] = None
+
+
+class ChatCompletionResponseUsage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+
+
+class ChatCompletionResponse(BaseModel):
+    id: str
+    object: Literal["chat.completion"] = "chat.completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: ChatCompletionResponseUsage
+
+
+class ChatCompletionStreamResponse(BaseModel):
+    id: str
+    object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionStreamResponseChoice]
+
+
+class ScoreEvaluationRequest(BaseModel):
+    model: str
+    messages: List[str]
+    max_length: Optional[int] = None
+
+
+class ScoreEvaluationResponse(BaseModel):
+    id: str
+    object: Literal["score.evaluation"] = "score.evaluation"
+    model: str
+    scores: List[float]

src/llamafactory/chat/__init__.py

@@ -0,0 +1,5 @@
+from .base_engine import BaseEngine
+from .chat_model import ChatModel
+
+
+__all__ = ["BaseEngine", "ChatModel"]

src/llamafactory/chat/base_engine.py

@@ -0,0 +1,69 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Optional, Sequence, Union
+
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+    from transformers import PreTrainedModel, PreTrainedTokenizer
+    from vllm import AsyncLLMEngine
+
+    from ..data import Template
+    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+@dataclass
+class Response:
+    response_text: str
+    response_length: int
+    prompt_length: int
+    finish_reason: Literal["stop", "length"]
+
+
+class BaseEngine(ABC):
+    model: Union["PreTrainedModel", "AsyncLLMEngine"]
+    tokenizer: "PreTrainedTokenizer"
+    can_generate: bool
+    template: "Template"
+    generating_args: Dict[str, Any]
+
+    @abstractmethod
+    def __init__(
+        self,
+        model_args: "ModelArguments",
+        data_args: "DataArguments",
+        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
+    ) -> None: ...
+
+    @abstractmethod
+    async def start(
+        self,
+    ) -> None: ...
+
+    @abstractmethod
+    async def chat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> List["Response"]: ...
+
+    @abstractmethod
+    async def stream_chat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> AsyncGenerator[str, None]: ...
+
+    @abstractmethod
+    async def get_scores(
+        self,
+        batch_input: List[str],
+        **input_kwargs,
+    ) -> List[float]: ...

src/llamafactory/chat/chat_model.py

@@ -0,0 +1,140 @@
+import asyncio
+from threading import Thread
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, Generator, List, Optional, Sequence
+
+from ..extras.misc import torch_gc
+from ..hparams import get_infer_args
+from .hf_engine import HuggingfaceEngine
+from .vllm_engine import VllmEngine
+
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+
+    from .base_engine import BaseEngine, Response
+
+
+def _start_background_loop(loop: asyncio.AbstractEventLoop) -> None:
+    asyncio.set_event_loop(loop)
+    loop.run_forever()
+
+
+class ChatModel:
+    def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
+        model_args, data_args, finetuning_args, generating_args = get_infer_args(args)
+        if model_args.infer_backend == "huggingface":
+            self.engine: "BaseEngine" = HuggingfaceEngine(model_args, data_args, finetuning_args, generating_args)
+        elif model_args.infer_backend == "vllm":
+            self.engine: "BaseEngine" = VllmEngine(model_args, data_args, finetuning_args, generating_args)
+        else:
+            raise NotImplementedError("Unknown backend: {}".format(model_args.infer_backend))
+
+        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,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> List["Response"]:
+        task = asyncio.run_coroutine_threadsafe(self.achat(messages, system, tools, image, **input_kwargs), self._loop)
+        return task.result()
+
+    async def achat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> List["Response"]:
+        return await self.engine.chat(messages, system, tools, image, **input_kwargs)
+
+    def stream_chat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> Generator[str, None, None]:
+        generator = self.astream_chat(messages, system, tools, image, **input_kwargs)
+        while True:
+            try:
+                task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
+                yield task.result()
+            except StopAsyncIteration:
+                break
+
+    async def astream_chat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> AsyncGenerator[str, None]:
+        async for new_token in self.engine.stream_chat(messages, system, tools, image, **input_kwargs):
+            yield new_token
+
+    def get_scores(
+        self,
+        batch_input: List[str],
+        **input_kwargs,
+    ) -> List[float]:
+        task = asyncio.run_coroutine_threadsafe(self.aget_scores(batch_input, **input_kwargs), self._loop)
+        return task.result()
+
+    async def aget_scores(
+        self,
+        batch_input: List[str],
+        **input_kwargs,
+    ) -> List[float]:
+        return await self.engine.get_scores(batch_input, **input_kwargs)
+
+
+def run_chat() -> None:
+    try:
+        import platform
+
+        if platform.system() != "Windows":
+            import readline  # noqa: F401
+    except ImportError:
+        print("Install `readline` for a better experience.")
+
+    chat_model = ChatModel()
+    messages = []
+    print("Welcome to the CLI application, use `clear` to remove the history, use `exit` to exit the application.")
+
+    while True:
+        try:
+            query = input("\nUser: ")
+        except UnicodeDecodeError:
+            print("Detected decoding error at the inputs, please set the terminal encoding to utf-8.")
+            continue
+        except Exception:
+            raise
+
+        if query.strip() == "exit":
+            break
+
+        if query.strip() == "clear":
+            messages = []
+            torch_gc()
+            print("History has been removed.")
+            continue
+
+        messages.append({"role": "user", "content": query})
+        print("Assistant: ", end="", flush=True)
+
+        response = ""
+        for new_text in chat_model.stream_chat(messages):
+            print(new_text, end="", flush=True)
+            response += new_text
+        print()
+        messages.append({"role": "assistant", "content": response})

src/llamafactory/chat/hf_engine.py

@@ -0,0 +1,324 @@
+import asyncio
+import concurrent.futures
+import os
+from threading import Thread
+from typing import TYPE_CHECKING, Any, AsyncGenerator, Callable, Dict, List, Optional, Sequence, Tuple, Union
+
+import torch
+from transformers import GenerationConfig, TextIteratorStreamer
+
+from ..data import get_template_and_fix_tokenizer
+from ..extras.logging import get_logger
+from ..extras.misc import get_logits_processor
+from ..model import load_model, load_tokenizer
+from .base_engine import BaseEngine, Response
+
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+    from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
+    from transformers.image_processing_utils import BaseImageProcessor
+    from trl import PreTrainedModelWrapper
+
+    from ..data import Template
+    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+class HuggingfaceEngine(BaseEngine):
+    def __init__(
+        self,
+        model_args: "ModelArguments",
+        data_args: "DataArguments",
+        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
+    ) -> None:
+        self.can_generate = finetuning_args.stage == "sft"
+        tokenizer_module = load_tokenizer(model_args)
+        self.tokenizer = tokenizer_module["tokenizer"]
+        self.processor = tokenizer_module["processor"]
+        self.tokenizer.padding_side = "left" if self.can_generate else "right"
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
+        self.model = load_model(
+            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()
+
+    @staticmethod
+    def _process_args(
+        model: "PreTrainedModel",
+        tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
+        template: "Template",
+        generating_args: Dict[str, Any],
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        input_kwargs: Optional[Dict[str, Any]] = {},
+    ) -> Tuple[Dict[str, Any], int]:
+        if (
+            processor is not None
+            and image is not None
+            and not hasattr(processor, "image_seq_length")
+            and template.image_token not in messages[0]["content"]
+        ):  # llava-like models
+            messages[0]["content"] = template.image_token + messages[0]["content"]
+
+        paired_messages = messages + [{"role": "assistant", "content": ""}]
+        system = system or generating_args["default_system"]
+        pixel_values = None
+        prompt_ids, _ = template.encode_oneturn(
+            tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools
+        )
+        if processor is not None and image is not None:  # add image features
+            image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
+            batch_feature = image_processor(image, return_tensors="pt")
+            pixel_values = batch_feature.to(model.device)["pixel_values"]  # shape (B, C, H, W)
+            if hasattr(processor, "image_seq_length"):  # paligemma models
+                image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
+                prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
+
+        prompt_length = len(prompt_ids)
+        inputs = torch.tensor([prompt_ids], device=model.device)
+        attention_mask = torch.ones_like(inputs, dtype=torch.bool)
+
+        do_sample: Optional[bool] = input_kwargs.pop("do_sample", None)
+        temperature: Optional[float] = input_kwargs.pop("temperature", None)
+        top_p: Optional[float] = input_kwargs.pop("top_p", None)
+        top_k: Optional[float] = input_kwargs.pop("top_k", None)
+        num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1)
+        repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None)
+        length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None)
+        max_length: Optional[int] = input_kwargs.pop("max_length", None)
+        max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
+        stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
+
+        if stop is not None:
+            logger.warning("Stop parameter is not supported in Huggingface engine yet.")
+
+        generating_args = generating_args.copy()
+        generating_args.update(
+            dict(
+                do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
+                temperature=temperature if temperature is not None else generating_args["temperature"],
+                top_p=top_p if top_p is not None else generating_args["top_p"],
+                top_k=top_k if top_k is not None else generating_args["top_k"],
+                num_return_sequences=num_return_sequences,
+                repetition_penalty=repetition_penalty
+                if repetition_penalty is not None
+                else generating_args["repetition_penalty"],
+                length_penalty=length_penalty if length_penalty is not None else generating_args["length_penalty"],
+                eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
+                pad_token_id=tokenizer.pad_token_id,
+            )
+        )
+
+        if isinstance(num_return_sequences, int) and num_return_sequences > 1:  # do_sample needs temperature > 0
+            generating_args["do_sample"] = True
+            generating_args["temperature"] = generating_args["temperature"] or 1.0
+
+        if not generating_args["temperature"]:
+            generating_args["do_sample"] = False
+
+        if not generating_args["do_sample"]:
+            generating_args.pop("temperature", None)
+            generating_args.pop("top_p", None)
+
+        if max_length:
+            generating_args.pop("max_new_tokens", None)
+            generating_args["max_length"] = max_length
+
+        if max_new_tokens:
+            generating_args.pop("max_length", None)
+            generating_args["max_new_tokens"] = max_new_tokens
+
+        gen_kwargs = dict(
+            inputs=inputs,
+            attention_mask=attention_mask,
+            generation_config=GenerationConfig(**generating_args),
+            logits_processor=get_logits_processor(),
+        )
+
+        if pixel_values is not None:
+            gen_kwargs["pixel_values"] = pixel_values
+
+        return gen_kwargs, prompt_length
+
+    @staticmethod
+    @torch.inference_mode()
+    def _chat(
+        model: "PreTrainedModel",
+        tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
+        template: "Template",
+        generating_args: Dict[str, Any],
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        input_kwargs: Optional[Dict[str, Any]] = {},
+    ) -> List["Response"]:
+        gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
+            model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
+        )
+        generate_output = model.generate(**gen_kwargs)
+        response_ids = generate_output[:, prompt_length:]
+        response = tokenizer.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+        results = []
+        for i in range(len(response)):
+            eos_index = (response_ids[i] == tokenizer.eos_token_id).nonzero()
+            response_length = (eos_index[0].item() + 1) if len(eos_index) else len(response_ids[i])
+            results.append(
+                Response(
+                    response_text=response[i],
+                    response_length=response_length,
+                    prompt_length=prompt_length,
+                    finish_reason="stop" if len(eos_index) else "length",
+                )
+            )
+
+        return results
+
+    @staticmethod
+    @torch.inference_mode()
+    def _stream_chat(
+        model: "PreTrainedModel",
+        tokenizer: "PreTrainedTokenizer",
+        processor: Optional["ProcessorMixin"],
+        template: "Template",
+        generating_args: Dict[str, Any],
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        input_kwargs: Optional[Dict[str, Any]] = {},
+    ) -> Callable[[], str]:
+        gen_kwargs, _ = HuggingfaceEngine._process_args(
+            model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
+        )
+        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+        gen_kwargs["streamer"] = streamer
+        thread = Thread(target=model.generate, kwargs=gen_kwargs, daemon=True)
+        thread.start()
+
+        def stream():
+            try:
+                return streamer.__next__()
+            except StopIteration:
+                raise StopAsyncIteration()
+
+        return stream
+
+    @staticmethod
+    @torch.inference_mode()
+    def _get_scores(
+        model: "PreTrainedModelWrapper",
+        tokenizer: "PreTrainedTokenizer",
+        batch_input: List[str],
+        input_kwargs: Optional[Dict[str, Any]] = {},
+    ) -> List[float]:
+        max_length = input_kwargs.pop("max_length", None)
+        device = getattr(model.pretrained_model, "device", "cuda")
+        inputs = tokenizer(
+            batch_input,
+            padding=True,
+            truncation=True,
+            max_length=max_length or getattr(model.config, "max_position_embeddings", 1024),
+            return_tensors="pt",
+            add_special_tokens=True,
+        ).to(device)
+
+        input_ids: torch.Tensor = inputs["input_ids"]
+        _, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
+
+        if getattr(model.config, "model_type", None) == "chatglm":
+            values = torch.transpose(values, 0, 1)
+
+        scores = []
+        for i in range(input_ids.size(0)):
+            end_indexes = (input_ids[i] != tokenizer.pad_token_id).nonzero()
+            end_index = end_indexes[-1].item() if len(end_indexes) else 0
+            scores.append(values[i, end_index].nan_to_num().item())
+
+        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]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> List["Response"]:
+        if not self.can_generate:
+            raise ValueError("The current model does not support `chat`.")
+
+        loop = asyncio.get_running_loop()
+        input_args = (
+            self.model,
+            self.tokenizer,
+            self.processor,
+            self.template,
+            self.generating_args,
+            messages,
+            system,
+            tools,
+            image,
+            input_kwargs,
+        )
+        async with self._semaphore:
+            with concurrent.futures.ThreadPoolExecutor() as pool:
+                return await loop.run_in_executor(pool, self._chat, *input_args)
+
+    async def stream_chat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> AsyncGenerator[str, None]:
+        if not self.can_generate:
+            raise ValueError("The current model does not support `stream_chat`.")
+
+        loop = asyncio.get_running_loop()
+        input_args = (
+            self.model,
+            self.tokenizer,
+            self.processor,
+            self.template,
+            self.generating_args,
+            messages,
+            system,
+            tools,
+            image,
+            input_kwargs,
+        )
+        async with self._semaphore:
+            with concurrent.futures.ThreadPoolExecutor() as pool:
+                stream = self._stream_chat(*input_args)
+                while True:
+                    try:
+                        yield await loop.run_in_executor(pool, stream)
+                    except StopAsyncIteration:
+                        break
+
+    async def get_scores(
+        self,
+        batch_input: List[str],
+        **input_kwargs,
+    ) -> List[float]:
+        if self.can_generate:
+            raise ValueError("Cannot get scores using an auto-regressive model.")
+
+        loop = asyncio.get_running_loop()
+        input_args = (self.model, self.tokenizer, batch_input, input_kwargs)
+        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

@@ -0,0 +1,214 @@
+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 ..model import load_config, load_tokenizer
+from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
+from .base_engine import BaseEngine, Response
+
+
+if is_vllm_available():
+    from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
+    from vllm.lora.request import LoRARequest
+    from vllm.sequence import MultiModalData
+
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+    from transformers.image_processing_utils import BaseImageProcessor
+
+    from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
+
+
+logger = get_logger(__name__)
+
+
+class VllmEngine(BaseEngine):
+    def __init__(
+        self,
+        model_args: "ModelArguments",
+        data_args: "DataArguments",
+        finetuning_args: "FinetuningArguments",
+        generating_args: "GeneratingArguments",
+    ) -> None:
+        config = load_config(model_args)  # may download model from ms hub
+
+        self.can_generate = finetuning_args.stage == "sft"
+        tokenizer_module = load_tokenizer(model_args)
+        self.tokenizer = tokenizer_module["tokenizer"]
+        self.processor = tokenizer_module["processor"]
+        self.tokenizer.padding_side = "left"
+        self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
+        self.generating_args = generating_args.to_dict()
+
+        engine_args = {
+            "model": model_args.model_name_or_path,
+            "trust_remote_code": True,
+            "download_dir": model_args.cache_dir,
+            "dtype": model_args.vllm_dtype,
+            "max_model_len": model_args.vllm_maxlen,
+            "tensor_parallel_size": get_device_count() or 1,
+            "gpu_memory_utilization": model_args.vllm_gpu_util,
+            "disable_log_stats": True,
+            "disable_log_requests": True,
+            "enforce_eager": model_args.vllm_enforce_eager,
+            "enable_lora": model_args.adapter_name_or_path is not None,
+            "max_lora_rank": model_args.vllm_max_lora_rank,
+        }
+
+        if model_args.visual_inputs:
+            image_size = config.vision_config.image_size
+            patch_size = config.vision_config.patch_size
+            self.image_feature_size = (image_size // patch_size) ** 2
+            engine_args["image_input_type"] = "pixel_values"
+            engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids(self.template.image_token)
+            engine_args["image_input_shape"] = "1,3,{},{}".format(image_size, image_size)
+            engine_args["image_feature_size"] = self.image_feature_size
+            if getattr(config, "is_yi_vl_derived_model", None):
+                import vllm.model_executor.models.llava
+
+                logger.info("Detected Yi-VL model, applying projector patch.")
+                vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
+
+        self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
+        if model_args.adapter_name_or_path is not None:
+            self.lora_request = LoRARequest("default", 1, model_args.adapter_name_or_path[0])
+        else:
+            self.lora_request = None
+
+    async def _generate(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> AsyncIterator["RequestOutput"]:
+        request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
+
+        if (
+            self.processor is not None
+            and image is not None
+            and not hasattr(self.processor, "image_seq_length")
+            and self.template.image_token not in messages[0]["content"]
+        ):  # llava-like models (TODO: paligemma models)
+            messages[0]["content"] = self.template.image_token * self.image_feature_size + messages[0]["content"]
+
+        paired_messages = messages + [{"role": "assistant", "content": ""}]
+        system = system or self.generating_args["default_system"]
+        prompt_ids, _ = self.template.encode_oneturn(
+            tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
+        )
+
+        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)
+        else:
+            multi_modal_data = None
+
+        prompt_length = len(prompt_ids)
+
+        use_beam_search: bool = self.generating_args["num_beams"] > 1
+        temperature: Optional[float] = input_kwargs.pop("temperature", None)
+        top_p: Optional[float] = input_kwargs.pop("top_p", None)
+        top_k: Optional[float] = input_kwargs.pop("top_k", None)
+        num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1)
+        repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None)
+        length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None)
+        max_length: Optional[int] = input_kwargs.pop("max_length", None)
+        max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
+        stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
+
+        if "max_new_tokens" in self.generating_args:
+            max_tokens = self.generating_args["max_new_tokens"]
+        elif "max_length" in self.generating_args:
+            if self.generating_args["max_length"] > prompt_length:
+                max_tokens = self.generating_args["max_length"] - prompt_length
+            else:
+                max_tokens = 1
+
+        if max_length:
+            max_tokens = max_length - prompt_length if max_length > prompt_length else 1
+
+        if max_new_tokens:
+            max_tokens = max_new_tokens
+
+        sampling_params = SamplingParams(
+            n=num_return_sequences,
+            repetition_penalty=(
+                repetition_penalty if repetition_penalty is not None else self.generating_args["repetition_penalty"]
+            )
+            or 1.0,  # repetition_penalty must > 0
+            temperature=temperature if temperature is not None else self.generating_args["temperature"],
+            top_p=(top_p if top_p is not None else self.generating_args["top_p"]) or 1.0,  # top_p must > 0
+            top_k=top_k if top_k is not None else self.generating_args["top_k"],
+            use_beam_search=use_beam_search,
+            length_penalty=length_penalty if length_penalty is not None else self.generating_args["length_penalty"],
+            stop=stop,
+            stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
+            max_tokens=max_tokens,
+            skip_special_tokens=True,
+        )
+
+        result_generator = self.model.generate(
+            inputs={"prompt_token_ids": prompt_ids, "multi_modal_data": multi_modal_data},
+            sampling_params=sampling_params,
+            request_id=request_id,
+            lora_request=self.lora_request,
+        )
+        return result_generator
+
+    async def start(self) -> None:
+        pass
+
+    async def chat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> List["Response"]:
+        final_output = None
+        generator = await self._generate(messages, system, tools, image, **input_kwargs)
+        async for request_output in generator:
+            final_output = request_output
+
+        results = []
+        for output in final_output.outputs:
+            results.append(
+                Response(
+                    response_text=output.text,
+                    response_length=len(output.token_ids),
+                    prompt_length=len(final_output.prompt_token_ids),
+                    finish_reason=output.finish_reason,
+                )
+            )
+
+        return results
+
+    async def stream_chat(
+        self,
+        messages: Sequence[Dict[str, str]],
+        system: Optional[str] = None,
+        tools: Optional[str] = None,
+        image: Optional["NDArray"] = None,
+        **input_kwargs,
+    ) -> AsyncGenerator[str, None]:
+        generated_text = ""
+        generator = await self._generate(messages, system, tools, image, **input_kwargs)
+        async for result in generator:
+            delta_text = result.outputs[0].text[len(generated_text) :]
+            generated_text = result.outputs[0].text
+            yield delta_text
+
+    async def get_scores(
+        self,
+        batch_input: List[str],
+        **input_kwargs,
+    ) -> List[float]:
+        raise NotImplementedError("vLLM engine does not support get_scores.")

src/llamafactory/cli.py

@@ -0,0 +1,106 @@
+import os
+import random
+import subprocess
+import sys
+from enum import Enum, unique
+
+from . import launcher
+from .api.app import run_api
+from .chat.chat_model import run_chat
+from .eval.evaluator import run_eval
+from .extras.env import VERSION, print_env
+from .extras.logging import get_logger
+from .extras.misc import get_device_count
+from .train.tuner import export_model, run_exp
+from .webui.interface import run_web_demo, run_web_ui
+
+
+USAGE = (
+    "-" * 70
+    + "\n"
+    + "| Usage:                                                             |\n"
+    + "|   llamafactory-cli api -h: launch an OpenAI-style API server       |\n"
+    + "|   llamafactory-cli chat -h: launch a chat interface in CLI         |\n"
+    + "|   llamafactory-cli eval -h: evaluate models                        |\n"
+    + "|   llamafactory-cli export -h: merge LoRA adapters and export model |\n"
+    + "|   llamafactory-cli train -h: train models                          |\n"
+    + "|   llamafactory-cli webchat -h: launch a chat interface in Web UI   |\n"
+    + "|   llamafactory-cli webui: launch LlamaBoard                        |\n"
+    + "|   llamafactory-cli version: show version info                      |\n"
+    + "-" * 70
+)
+
+WELCOME = (
+    "-" * 58
+    + "\n"
+    + "| Welcome to LLaMA Factory, version {}".format(VERSION)
+    + " " * (21 - len(VERSION))
+    + "|\n|"
+    + " " * 56
+    + "|\n"
+    + "| Project page: https://github.com/hiyouga/LLaMA-Factory |\n"
+    + "-" * 58
+)
+
+logger = get_logger(__name__)
+
+
+@unique
+class Command(str, Enum):
+    API = "api"
+    CHAT = "chat"
+    ENV = "env"
+    EVAL = "eval"
+    EXPORT = "export"
+    TRAIN = "train"
+    WEBDEMO = "webchat"
+    WEBUI = "webui"
+    VER = "version"
+    HELP = "help"
+
+
+def main():
+    command = sys.argv.pop(1)
+    if command == Command.API:
+        run_api()
+    elif command == Command.CHAT:
+        run_chat()
+    elif command == Command.ENV:
+        print_env()
+    elif command == Command.EVAL:
+        run_eval()
+    elif command == Command.EXPORT:
+        export_model()
+    elif command == Command.TRAIN:
+        force_torchrun = os.environ.get("FORCE_TORCHRUN", "0").lower() in ["true", "1"]
+        if force_torchrun or get_device_count() > 1:
+            master_addr = os.environ.get("MASTER_ADDR", "127.0.0.1")
+            master_port = os.environ.get("MASTER_PORT", str(random.randint(20001, 29999)))
+            logger.info("Initializing distributed tasks at: {}:{}".format(master_addr, master_port))
+            subprocess.run(
+                (
+                    "torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
+                    "--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
+                ).format(
+                    nnodes=os.environ.get("NNODES", "1"),
+                    node_rank=os.environ.get("RANK", "0"),
+                    nproc_per_node=os.environ.get("NPROC_PER_NODE", str(get_device_count())),
+                    master_addr=master_addr,
+                    master_port=master_port,
+                    file_name=launcher.__file__,
+                    args=" ".join(sys.argv[1:]),
+                ),
+                shell=True,
+            )
+        else:
+            run_exp()
+    elif command == Command.WEBDEMO:
+        run_web_demo()
+    elif command == Command.WEBUI:
+        run_web_ui()
+    elif command == Command.VER:
+        print(WELCOME)
+    elif command == Command.HELP:
+        print(USAGE)
+    else:
+        raise NotImplementedError("Unknown command: {}".format(command))

src/llamafactory/data/__init__.py

@@ -0,0 +1,16 @@
+from .collator import KTODataCollatorWithPadding, PairwiseDataCollatorWithPadding
+from .data_utils import Role, split_dataset
+from .loader import get_dataset
+from .template import TEMPLATES, Template, get_template_and_fix_tokenizer
+
+
+__all__ = [
+    "KTODataCollatorWithPadding",
+    "PairwiseDataCollatorWithPadding",
+    "Role",
+    "split_dataset",
+    "get_dataset",
+    "TEMPLATES",
+    "Template",
+    "get_template_and_fix_tokenizer",
+]