Easily fine-tune 100+ large language models with zero-code CLI and Web UI

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Fine-tuning a large language model can be easy as...

https://github.com/user-attachments/assets/7c96b465-9df7-45f4-8053-bf03e58386d3

Choose your path:

• Documentation (WIP): https://llamafactory.readthedocs.io/zh-cn/latest/
• Colab: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
• Local machine: Please refer to usage
• PAI-DSW: Llama3 Example | Qwen2-VL Example
• Amazon SageMaker: Blog

[!NOTE] Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.

Table of Contents

• Features
• Benchmark
• Changelog
• Supported Models
• Supported Training Approaches
• Provided Datasets
• Requirement
• Getting Started
  • Installation
  • Data Preparation
  • Quickstart
  • Fine-Tuning with LLaMA Board GUI
  • Build Docker
  • Deploy with OpenAI-style API and vLLM
  • Download from ModelScope Hub
  • Download from Modelers Hub
  • Use W&B Logger
  • Use SwanLab Logger
• Projects using LLaMA Factory
• License
• Citation
• Acknowledgement

Features

• Various models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc.
• Integrated methods: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO, ORPO, etc.
• Scalable resources: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
• Advanced algorithms: GaLore, BAdam, APOLLO, Adam-mini, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
• Practical tricks: FlashAttention-2, Unsloth, Liger Kernel, RoPE scaling, NEFTune and rsLoRA.
• Wide tasks: Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc.
• Experiment monitors: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc.
• Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker.

Day-N Support for Fine-Tuning Cutting-Edge Models

| Support Date | Model Name | | ------------ | ---------------------------------------------------------- | | Day 0 | Qwen2.5 / Qwen2-VL / QwQ / QvQ / InternLM3 / MiniCPM-o-2.6 | | Day 1 | Llama 3 / GLM-4 / Mistral Small / PaliGemma2 |

Benchmark

Compared to ChatGLM's P-Tuning, 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.

Definitions

- **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.

Changelog

[23/06/03] Now we support quantized training and inference (aka QLoRA). Try --quantization_bit 4/8 argument to work with quantized model. (experimental feature)

[23/05/31] Now we support training the BLOOM & BLOOMZ models in this repo. Try --model_name_or_path bigscience/bloomz-7b1-mt argument to use the BLOOMZ model.

Supported Models

• LLaMA (7B/13B/33B/65B)
• BLOOM & BLOOMZ (560M/1.1B/1.7B/3B/7.1B/176B)

Supported Training Approaches

| Approach | Full-tuning | Freeze-tuning | LoRA | QLoRA | | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | | Pre-Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | Supervised Fine-Tuning | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | Reward Modeling | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | PPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | DPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | KTO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | ORPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | | SimPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: |

[!TIP] The implementation details of PPO can be found in this blog.

Provided Datasets

• For pre-training:
  • Wiki Demo
• For supervised fine-tuning:
  • Stanford Alpaca
  • Stanford Alpaca (Chinese)
  • GPT-4 Generated Data
  • BELLE 2M
  • BELLE 1M
  • BELLE 0.5M
  • BELLE Dialogue 0.4M
  • BELLE School Math 0.25M
  • BELLE Multiturn Chat 0.8M
  • Guanaco Dataset
  • Firefly 1.1M
  • CodeAlpaca 20k
  • Alpaca CoT
  • Web QA (Chinese)
  • UltraChat
• For reward model training:
  • HH-RLHF
  • GPT-4 Generated Data
  • GPT-4 Generated Data (Chinese)

Please refer to 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 pip install --upgrade huggingface_hub huggingface-cli login

Requirement

• Python 3.8+ and PyTorch 1.13.1+
• 🤗Transformers, Datasets, Accelerate, PEFT and TRL
• protobuf, cpm_kernels and sentencepiece
• jieba, rouge_chinese and nltk (used at evaluation)
• gradio and mdtex2html (used in web_demo.py)

And powerful GPUs!

Getting Started

Data Preparation (optional)

Please refer to data/example_dataset for checking the details about the format of dataset files. You can either use a single .json file or a dataset loading 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 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml llamafactory-cli chat examples/inference/llama3_lora_sft.yaml llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml

See examples/README.md for advanced usage (including distributed training).

[!TIP] Use llamafactory-cli help to show help information.

Fine-Tuning with LLaMA Board GUI (powered by Gradio)

bash llamafactory-cli webui

Build Docker

For CUDA users:

bash cd docker/docker-cuda/ docker compose up -d docker compose exec llamafactory bash

For Ascend NPU users:

bash cd docker/docker-npu/ docker compose up -d docker compose exec llamafactory bash

For AMD ROCm users:

bash cd docker/docker-rocm/ docker compose up -d docker compose exec llamafactory bash

Build without Docker Compose

For CUDA users: ```bash docker build -f ./docker/docker-cuda/Dockerfile \ --build-arg INSTALL_BNB=false \ --build-arg INSTALL_VLLM=false \ --build-arg INSTALL_DEEPSPEED=false \ --build-arg INSTALL_FLASHATTN=false \ --build-arg PIP_INDEX=https://pypi.org/simple \ -t llamafactory:latest . docker run -dit --gpus=all \ -v ./hf_cache:/root/.cache/huggingface \ -v ./ms_cache:/root/.cache/modelscope \ -v ./om_cache:/root/.cache/openmind \ -v ./data:/app/data \ -v ./output:/app/output \ -p 7860:7860 \ -p 8000:8000 \ --shm-size 16G \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash ``` For Ascend NPU users: ```bash # Choose docker image upon your environment docker build -f ./docker/docker-npu/Dockerfile \ --build-arg INSTALL_DEEPSPEED=false \ --build-arg PIP_INDEX=https://pypi.org/simple \ -t llamafactory:latest . # Change `device` upon your resources docker run -dit \ -v ./hf_cache:/root/.cache/huggingface \ -v ./ms_cache:/root/.cache/modelscope \ -v ./om_cache:/root/.cache/openmind \ -v ./data:/app/data \ -v ./output:/app/output \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -p 7860:7860 \ -p 8000:8000 \ --device /dev/davinci0 \ --device /dev/davinci_manager \ --device /dev/devmm_svm \ --device /dev/hisi_hdc \ --shm-size 16G \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash ``` For AMD ROCm users: ```bash docker build -f ./docker/docker-rocm/Dockerfile \ --build-arg INSTALL_BNB=false \ --build-arg INSTALL_VLLM=false \ --build-arg INSTALL_DEEPSPEED=false \ --build-arg INSTALL_FLASHATTN=false \ --build-arg PIP_INDEX=https://pypi.org/simple \ -t llamafactory:latest . docker run -dit \ -v ./hf_cache:/root/.cache/huggingface \ -v ./ms_cache:/root/.cache/modelscope \ -v ./om_cache:/root/.cache/openmind \ -v ./data:/app/data \ -v ./output:/app/output \ -v ./saves:/app/saves \ -p 7860:7860 \ -p 8000:8000 \ --device /dev/kfd \ --device /dev/dri \ --shm-size 16G \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash ```

Details about volume

- `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory. - `ms_cache`: Similar to Hugging Face cache but for ModelScope users. - `om_cache`: Similar to Hugging Face cache but for Modelers users. - `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.

Deploy with OpenAI-style API and vLLM

bash API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml

[!TIP] Visit this page for API document.

Examples: Image understanding | Function calling

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, e.g., LLM-Research/Meta-Llama-3-8B-Instruct.

Download from Modelers Hub

You can also use Modelers Hub to download models and datasets.

bash export USE_OPENMIND_HUB=1 # `set USE_OPENMIND_HUB=1` for Windows

Train the model by specifying a model ID of the Modelers Hub as the model_name_or_path. You can find a full list of model IDs at Modelers Hub, e.g., TeleAI/TeleChat-7B-pt.

Use W&B Logger

To use Weights & Biases for logging experimental results, you need to add the following arguments to yaml files.

yaml report_to: wandb run_name: test_run # optional

Set WANDB_API_KEY to your key when launching training tasks to log in with your W&B account.

Use SwanLab Logger

To use SwanLab for logging experimental results, you need to add the following arguments to yaml files.

yaml use_swanlab: true swanlab_run_name: test_run # optional

When launching training tasks, you can log in to SwanLab in three ways:

1. Add swanlab_api_key=<your_api_key> to the yaml file, and set it to your API key.
2. Set the environment variable SWANLAB_API_KEY to your API key.
3. Use the swanlab login command to complete the login.

Projects using LLaMA Factory

If you have a project that should be incorporated, please contact via email or create a pull request.

Click to show

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. KDD 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. ACL 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. ACL 2024 Findings. [[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. ACL 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. COLING 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. ICML 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. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140) 1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585) 1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760) 1. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378) 1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055) 1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739) 1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816) 1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215) 1. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [[paper]](https://aclanthology.org/2024.lt4hala-1.30) 1. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2406.00380) 1. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [[arxiv]](https://arxiv.org/abs/2406.02106) 1. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [[arxiv]](https://arxiv.org/abs/2406.03136) 1. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2406.04496) 1. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [[arxiv]](https://arxiv.org/abs/2406.05688) 1. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [[arxiv]](https://arxiv.org/abs/2406.05955) 1. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [[arxiv]](https://arxiv.org/abs/2406.06973) 1. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [[arxiv]](https://arxiv.org/abs/2406.07115) 1. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [[arxiv]](https://arxiv.org/abs/2406.07815) 1. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2406.10099) 1. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [[arxiv]](https://arxiv.org/abs/2406.10173) 1. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [[arxiv]](https://arxiv.org/abs/2406.12074) 1. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [[arxiv]](https://arxiv.org/abs/2406.14408) 1. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [[arxiv]](https://arxiv.org/abs/2406.14546) 1. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [[arxiv]](https://arxiv.org/abs/2406.15695) 1. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [[arxiv]](https://arxiv.org/abs/2406.17233) 1. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [[arxiv]](https://arxiv.org/abs/2406.18069) 1. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh's Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [[paper]](https://aclanthology.org/2024.americasnlp-1.25) 1. Li et al. Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 2024. [[arxiv]](https://arxiv.org/abs/2406.19949) 1. Yang et al. Financial Knowledge Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2407.00365) 1. Lin et al. DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging. 2024. [[arxiv]](https://arxiv.org/abs/2407.01470) 1. Bako et al. Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization. 2024. [[arxiv]](https://arxiv.org/abs/2407.06129) 1. Huang et al. RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. 2024. [[arxiv]](https://arxiv.org/abs/2407.08044) 1. Jiang et al. LLM-Collaboration on Automatic Science Journalism for the General Audience. 2024. [[arxiv]](https://arxiv.org/abs/2407.09756) 1. Inouye et al. Applied Auto-tuning on LoRA Hyperparameters. 2024. [[paper]](https://scholarcommons.scu.edu/cseng_senior/272/) 1. Qi et al. Research on Tibetan Tourism Viewpoints information generation system based on LLM. 2024. [[arxiv]](https://arxiv.org/abs/2407.13561) 1. Xu et al. Course-Correction: Safety Alignment Using Synthetic Preferences. 2024. [[arxiv]](https://arxiv.org/abs/2407.16637) 1. Sun et al. LAMBDA: A Large Model Based Data Agent. 2024. [[arxiv]](https://arxiv.org/abs/2407.17535) 1. Zhu et al. CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2407.19705) 1. Yu et al. Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2408.00137) 1. Xie et al. The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. IALP 2024. [[paper]](https://www.asianlp.sg/conferences/ialp2024/proceedings/papers/IALP2024_P055.pdf) 1. Liu et al. Instruct-Code-Llama: Improving Capabilities of Language Model in Competition Level Code Generation by Online Judge Feedback. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_11) 1. Wang et al. Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning. ICIC 2024. [[paper]](https://link.springer.com/chapter/10.1007/978-981-97-5669-8_23) 1. Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2408.04693) 1. Zeng et al. Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2408.04168) 1. Xia et al. Using Pre-trained Language Model for Accurate ESG Prediction. FinNLP 2024. [[paper]](https://aclanthology.org/2024.finnlp-2.1/) 1. Liang et al. I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm. 2024. [[arxiv]](https://arxiv.org/abs/2408.08072) 1. Bai et al. Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation. CIKM 2024. [[paper]](https://dl.acm.org/doi/10.1145/3627673.3679611) 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. 1. **[AutoRE](https://github.com/THUDM/AutoRE)**: A document-level relation extraction system based on large language models. 1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX. 1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory. 1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [[blog]](https://zhuanlan.zhihu.com/p/987727357) 1. **[360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory)**: A modified library that supports long sequence SFT & DPO using ring attention. 1. **[Sky-T1](https://novasky-ai.github.io/posts/sky-t1/)**: An o1-like model fine-tuned by NovaSky AI with very small cost.

License

This repository is licensed under the Apache-2.0 License.

Please follow the model licenses to use the corresponding model weights: Baichuan 2 / BLOOM / ChatGLM3 / Command R / DeepSeek / Falcon / Gemma / GLM-4 / GPT-2 / Granite / Index / InternLM / Llama / Llama 2 (LLaVA-1.5) / Llama 3 / MiniCPM / Mistral/Mixtral/Pixtral / OLMo / Phi-1.5/Phi-2 / Phi-3/Phi-4 / Qwen / Skywork / StarCoder 2 / TeleChat2 / XVERSE / Yi / Yi-1.5 / Yuan 2

Citation

If this work is helpful, please kindly cite as:

bibtex @inproceedings{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 Zhangchi Feng and Yongqiang Ma}, booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)}, address={Bangkok, Thailand}, publisher={Association for Computational Linguistics}, year={2024}, url={http://arxiv.org/abs/2403.13372} }

Acknowledgement

This repo benefits from PEFT, TRL, QLoRA and FastChat. Thanks for their wonderful works.

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