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

https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89-7ace5698baf6

Choose your path:

• Colab: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
• Local machine: Please refer to usage

Table of Contents

• Features
• Benchmark
• Changelog
• Supported Models
• Supported Training Approaches
• Provided Datasets
• Requirement
• Getting Started
• Projects using LLaMA Factory
• License
• Citation
• 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 and ORPO.
• Scalable resources: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
• Advanced algorithms: GaLore, 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, 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

[24/04/26] We supported fine-tuning the LLaVA-1.5 multimodal LLMs. See examples/lora_single_gpu/sft_mllm.sh for usage.

[24/04/22] We provided a Colab notebook 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 and Llama3-Chinese for details.

[24/04/21] We supported Mixture-of-Depths according to AstraMindAI's implementation. See examples/extras/mod for usage.

[24/04/16] We supported BAdam. See examples/extras/badam for usage.

[24/04/16] We supported 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.

Full Changelog

[24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See `examples/lora_single_gpu` for usage. [24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv! [24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See `examples/extras/fsdp_qlora` for usage. [24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See `examples/extras/loraplus` for usage. [24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. See `examples/extras/galore` for usage. [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. (LoRA is not yet supported, merge it first.) [24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `--use_dora` to activate DoRA training. [24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See `examples/extras/llama_pro` 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`. [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` 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](#use-modelscope-hub-optional) for usage. [23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neftune_noise_alpha` argument to activate NEFTune, e.g., `--neftune_noise_alpha 5`. [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. [23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models. [23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn 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 [this example](#dpo-training) to train your models. [23/07/31] We supported **dataset streaming**. Try `--streaming` 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. [23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development. [23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested. [23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details. [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.

Supported Models

| Model | Model size | Default module | Template | | -------------------------------------------------------- | --------------------------- | ----------------- | --------- | | Baichuan2 | 7B/13B | Wpack | baichuan2 | | BLOOM | 560M/1.1B/1.7B/3B/7.1B/176B | querykeyvalue | - | | BLOOMZ | 560M/1.1B/1.7B/3B/7.1B/176B | querykeyvalue | - | | ChatGLM3 | 6B | querykeyvalue | chatglm3 | | Command-R | 35B/104B | qproj,vproj | cohere | | DeepSeek (MoE) | 7B/16B/67B | qproj,vproj | deepseek | | Falcon | 7B/40B/180B | querykeyvalue | falcon | | Gemma/CodeGemma | 2B/7B | qproj,vproj | gemma | | InternLM2 | 7B/20B | wqkv | intern2 | | LLaMA | 7B/13B/33B/65B | qproj,vproj | - | | LLaMA-2 | 7B/13B/70B | qproj,vproj | llama2 | | LLaMA-3 | 8B/70B | qproj,vproj | llama3 | | LLaVA-1.5 | 7B/13B | qproj,vproj | vicuna | | Mistral/Mixtral | 7B/8x7B/8x22B | qproj,vproj | mistral | | OLMo | 1B/7B | qproj,vproj | - | | Phi-1.5/2 | 1.3B/2.7B | qproj,vproj | - | | Phi-3 | 3.8B | qkvproj | phi | | Qwen | 1.8B/7B/14B/72B | cattn | qwen | | Qwen1.5 (Code/MoE) | 0.5B/1.8B/4B/7B/14B/32B/72B | qproj,vproj | qwen | | StarCoder2 | 3B/7B/15B | qproj,vproj | - | | XVERSE | 7B/13B/65B | qproj,vproj | xverse | | Yi | 6B/9B/34B | qproj,vproj | yi | | Yuan | 2B/51B/102B | qproj,v_proj | yuan |

[!NOTE] Default module is used for the --lora_target argument, you can use --lora_target all to specify all the available modules for better convergence.

For the "base" models, the --template argument can be chosen from default, alpaca, vicuna etc. But make sure to use the corresponding template for the "instruct/chat" models.

Remember to use the SAME template in training and inference.

Please refer to constants.py for a full list of models we supported.

You also can add a custom chat template to template.py.

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: | | ORPO Training | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: | :whitecheckmark: |

Provided Datasets

Pre-training datasets

- [Wiki Demo (en)](data/wiki_demo.txt) - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) - [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) - [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) - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)

Supervised fine-tuning datasets

- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) - [Alpaca GPT4 (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) - [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) - [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) - [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) - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar) - [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data) - [Ad Gen (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) - [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) - [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) - [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)

Preference datasets

- [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) - [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs) - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar) - [DPO mix (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k) - [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)

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

| Mandatory | Minimum | Recommend | | ------------ | ------- | --------- | | python | 3.8 | 3.10 | | torch | 1.13.1 | 2.2.0 | | transformers | 4.37.2 | 4.39.3 | | datasets | 2.14.3 | 2.18.0 | | accelerate | 0.27.2 | 0.28.0 | | peft | 0.9.0 | 0.10.0 | | trl | 0.8.1 | 0.8.1 |

| Optional | Minimum | Recommend | | ------------ | ------- | --------- | | CUDA | 11.6 | 12.2 | | deepspeed | 0.10.0 | 0.14.0 | | bitsandbytes | 0.39.0 | 0.43.0 | | flash-attn | 2.3.0 | 2.5.6 |

Hardware Requirement

* estimated

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

Getting Started

Data Preparation

Please refer to 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.

Dependence Installation

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 -e .[metrics]

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

For Windows users

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 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version. ```bash pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl ``` 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.

Train with LLaMA Board GUI (powered by Gradio)

[!IMPORTANT] LLaMA Board GUI only supports training on a single GPU, please use CLI for distributed training.

Use local environment

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

For Alibaba Cloud users

If you encountered display problems in LLaMA Board on Alibaba Cloud, try using the following command to set environment variables before starting LLaMA Board: ```bash export GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/ ```

Use Docker

bash docker build -f ./Dockerfile -t llama-factory:latest . docker run --gpus=all \ -v ./hf_cache:/root/.cache/huggingface/ \ -v ./data:/app/data \ -v ./output:/app/output \ -e CUDA_VISIBLE_DEVICES=0 \ -p 7860:7860 \ --shm-size 16G \ --name llama_factory \ -d llama-factory:latest

Use Docker Compose

bash docker compose -f ./docker-compose.yml up -d

Details about volume

- 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.

Train with Command Line Interface

See examples/README.md for usage.

Use python src/train_bash.py -h to display arguments description.

Deploy with OpenAI-style API and vLLM

bash CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 python src/api_demo.py \ --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \ --template llama3 \ --infer_backend vllm \ --vllm_enforce_eager

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.

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. 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. 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[[arxiv]](https://arxiv.org/abs/2404.07084) 1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B. 1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge. 1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B. 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.

License

This repository is licensed under the Apache-2.0 License.

Please follow the model licenses to use the corresponding model weights: Baichuan2 / BLOOM / ChatGLM3 / Command-R / DeepSeek / Falcon / Gemma / InternLM2 / LLaMA / LLaMA-2/LLaVA-1.5 / LLaMA-3 / Mistral / OLMo / Phi-1.5/2 / Phi-3 / Qwen / StarCoder2 / XVERSE / Yi / Yuan

Citation

If this work is helpful, please kindly cite as:

bibtex @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, TRL, QLoRA and FastChat. Thanks for their wonderful works.

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