Used by Amazon, NVIDIA, Aliyun, etc. πŸ‘‹ Join our WeChat, NPU, Lab4AI, LLaMA Factory Online user group. [ English | δΈ­ζ–‡ ] Fine-tuning a large language model can be easy as... train_en.mp4 Choose your path: Documentation (WIP) : https://llamafactory.readthedocs.io/en/latest/ : https://llamafactory.readthedocs.io/en/latest/ Documentation (AMD GPU) : https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html : https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/fine_tune/llama_factory_llama3.html Colab (free) : https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing : https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing Local machine : Please refer to usage : Please refer to usage PAI-DSW (free trial) : https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory : https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory Alaya NeW (cloud GPU deal) : https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory : https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory Official Course : https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory : https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory LLaMA Factory Online: https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory Note Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them. Table of Contents Features Various models : LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Qwen2-VL, DeepSeek, Yi, Gemma, ChatGLM, Phi, etc. : 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. : (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. : 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, Muon, OFT, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA. : GaLore, BAdam, APOLLO, Adam-mini, Muon, OFT, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA. Practical tricks : FlashAttention-2, Unsloth, Liger Kernel, RoPE scaling, NEFTune and rsLoRA. : 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. : Multi-turn dialogue, tool using, image understanding, visual grounding, video recognition, audio understanding, etc. Experiment monitors : LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc. : LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc. Faster inference: OpenAI-style API, Gradio UI and CLI with vLLM worker or SGLang worker. Day-N Support for Fine-Tuning Cutting-Edge Models Support Date Model Name Day 0 Qwen3 / Qwen2.5-VL / Gemma 3 / GLM-4.1V / InternLM 3 / MiniCPM-o-2.6 Day 1 Llama 3 / GLM-4 / Mistral Small / PaliGemma2 / Llama 4 Blogs Changelog [25/08/22] We supported OFT and OFTv2. See examples for usage. [25/08/20] We supported fine-tuning the Intern-S1-mini models. See PR #8976 to get started. [25/08/06] We supported fine-tuning the GPT-OSS models. See PR #8826 to get started. Tip If you cannot use the latest feature, please pull the latest code and install LLaMA-Factory again. Supported Models Note For the "base" models, the template argument can be chosen from default , alpaca , vicuna etc. But make sure to use the corresponding template for the "instruct/chat" models. Remember to use the SAME template in training and inference. *: You should install the transformers from main branch and use DISABLE_VERSION_CHECK=1 to skip version check. **: You need to install a specific version of transformers to use the corresponding model. 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 OFT QOFT Pre-Training βœ… βœ… βœ… βœ… βœ… βœ… Supervised Fine-Tuning βœ… βœ… βœ… βœ… βœ… βœ… Reward Modeling βœ… βœ… βœ… βœ… βœ… βœ… PPO Training βœ… βœ… βœ… βœ… βœ… βœ… DPO Training βœ… βœ… βœ… βœ… βœ… βœ… KTO Training βœ… βœ… βœ… βœ… βœ… βœ… ORPO Training βœ… βœ… βœ… βœ… βœ… βœ… SimPO Training βœ… βœ… βœ… βœ… βœ… βœ… Tip The implementation details of PPO can be found in this blog. Provided Datasets Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands. pip install --upgrade huggingface_hub huggingface-cli login Requirement Mandatory Minimum Recommend python 3.9 3.10 torch 2.0.0 2.6.0 torchvision 0.15.0 0.21.0 transformers 4.49.0 4.50.0 datasets 2.16.0 3.2.0 accelerate 0.34.0 1.2.1 peft 0.14.0 0.15.1 trl 0.8.6 0.9.6 Optional Minimum Recommend CUDA 11.6 12.2 deepspeed 0.10.0 0.16.4 bitsandbytes 0.39.0 0.43.1 vllm 0.4.3 0.8.2 flash-attn 2.5.6 2.7.2 Hardware Requirement * estimated Method Bits 7B 14B 30B 70B x B Full ( bf16 or fp16 ) 32 120GB 240GB 600GB 1200GB 18x GB Full ( pure_bf16 ) 16 60GB 120GB 300GB 600GB 8x GB Freeze/LoRA/GaLore/APOLLO/BAdam/OFT 16 16GB 32GB 64GB 160GB 2x GB QLoRA / QOFT 8 10GB 20GB 40GB 80GB x GB QLoRA / QOFT 4 6GB 12GB 24GB 48GB x/2 GB QLoRA / QOFT 2 4GB 8GB 16GB 24GB x/4 GB Getting Started Installation Important Installation is mandatory. Install from Source git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git cd LLaMA-Factory pip install -e " .[torch,metrics] " --no-build-isolation Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, aqlm, vllm, sglang, galore, apollo, badam, adam-mini, qwen, minicpm_v, openmind, swanlab, dev Install from Docker Image docker run -it --rm --gpus=all --ipc=host hiyouga/llamafactory:latest This image is built on Ubuntu 22.04 (x86_64), CUDA 12.4, Python 3.11, PyTorch 2.6.0, and Flash-attn 2.7.4. Find the pre-built images: https://hub.docker.com/r/hiyouga/llamafactory/tags Please refer to build docker to build the image yourself. Setting up a virtual environment with uv Create an isolated Python environment with uv: uv sync --extra torch --extra metrics --prerelease=allow Run LLaMA-Factory in the isolated environment: uv run --prerelease=allow llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml For Windows users Install PyTorch You need to manually install the GPU version of PyTorch on the Windows platform. Please refer to the official website and the following command to install PyTorch with CUDA support: pip uninstall torch torchvision torchaudio pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 python -c " import torch; print(torch.cuda.is_available()) " If you see True then you have successfully installed PyTorch with CUDA support. Try dataloader_num_workers: 0 if you encounter Can't pickle local object error. Install BitsAndBytes 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 based on your CUDA version. pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl Install Flash Attention-2 To enable FlashAttention-2 on the Windows platform, please use the script from flash-attention-windows-wheel to compile and install it by yourself. For Ascend NPU users To install LLaMA Factory on Ascend NPU devices, please upgrade Python to version 3.10 or higher and specify extra dependencies: pip install -e ".[torch-npu,metrics]" . Additionally, you need to install the Ascend CANN Toolkit and Kernels. Please follow the installation tutorial or use the following commands: # 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%20V100R001C20SPC702/Ascend-cann-toolkit_8.0.0.alpha002_linux- " $( uname -i ) " .run bash Ascend-cann-toolkit_8.0.0.alpha002_linux- " $( uname -i ) " .run --install # install CANN Kernels wget https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C20SPC702/Ascend-cann-kernels-910b_8.0.0.alpha002_linux- " $( uname -i ) " .run bash Ascend-cann-kernels-910b_8.0.0.alpha002_linux- " $( uname -i ) " .run --install # set env variables source /usr/local/Ascend/ascend-toolkit/set_env.sh Requirement Minimum Recommend CANN 8.0.RC1 8.0.0.alpha002 torch 2.1.0 2.4.0 torch-npu 2.1.0 2.4.0.post2 deepspeed 0.13.2 0.13.2 vllm-ascend - 0.7.3 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. Download the pre-built Docker images: 32GB | 64GB Install BitsAndBytes To use QLoRA based on bitsandbytes on Ascend NPU, please follow these 3 steps: Manually compile bitsandbytes: Refer to the installation documentation for the NPU version of bitsandbytes to complete the compilation and installation. The compilation requires a cmake version of at least 3.22.1 and a g++ version of at least 12.x. # Install bitsandbytes from source # Clone bitsandbytes repo, Ascend NPU backend is currently enabled on multi-backend-refactor branch git clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git cd bitsandbytes/ # Install dependencies pip install -r requirements-dev.txt # Install the dependencies for the compilation tools. Note that the commands for this step may vary depending on the operating system. The following are provided for reference apt-get install -y build-essential cmake # Compile & install cmake -DCOMPUTE_BACKEND=npu -S . make pip install . Install transformers from the main branch. git clone -b main https://github.com/huggingface/transformers.git cd transformers pip install . Set double_quantization: false in the configuration. You can refer to the example. Data Preparation Please refer to data/README.md for checking the details about the format of dataset files. You can use datasets on HuggingFace / ModelScope / Modelers hub, load the dataset in local disk, or specify a path to s3/gcs cloud storage. Note Please update data/dataset_info.json to use your custom dataset. You can also use Easy Dataset, DataFlow and GraphGen to create synthetic data for fine-tuning. Quickstart Use the following 3 commands to run LoRA fine-tuning, inference and merging of the Llama3-8B-Instruct model, respectively. 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. Read FAQs first if you encounter any problems. Fine-Tuning with LLaMA Board GUI (powered by Gradio) llamafactory-cli webui LLaMA Factory Online Read our documentation. Build Docker For CUDA users: cd docker/docker-cuda/ docker compose up -d docker compose exec llamafactory bash For Ascend NPU users: cd docker/docker-npu/ docker compose up -d docker compose exec llamafactory bash For AMD ROCm users: cd docker/docker-rocm/ docker compose up -d docker compose exec llamafactory bash Build without Docker Compose For CUDA users: docker build -f ./docker/docker-cuda/Dockerfile \ --build-arg PIP_INDEX=https://pypi.org/simple \ --build-arg EXTRAS=metrics \ -t llamafactory:latest . docker run -dit --ipc=host --gpus=all \ -p 7860:7860 \ -p 8000:8000 \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash For Ascend NPU users: docker build -f ./docker/docker-npu/Dockerfile \ --build-arg PIP_INDEX=https://pypi.org/simple \ --build-arg EXTRAS=torch-npu,metrics \ -t llamafactory:latest . docker run -dit --ipc=host \ -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 \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash For AMD ROCm users: docker build -f ./docker/docker-rocm/Dockerfile \ --build-arg PIP_INDEX=https://pypi.org/simple \ --build-arg EXTRAS=metrics \ -t llamafactory:latest . docker run -dit --ipc=host \ -p 7860:7860 \ -p 8000:8000 \ --device /dev/kfd \ --device /dev/dri \ --name llamafactory \ llamafactory:latest docker exec -it llamafactory bash Use Docker volumes You can uncomment VOLUME [ "/root/.cache/huggingface", "/app/shared_data", "/app/output" ] in the Dockerfile to use data volumes. When building the Docker image, use -v ./hf_cache:/root/.cache/huggingface argument to mount the local directory to the container. The following data volumes are available. hf_cache : Utilize Hugging Face cache on the host machine. : Utilize Hugging Face cache on the host machine. shared_data : The directionary to store datasets on the host machine. : The directionary to store datasets on the host machine. 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 API_PORT=8000 llamafactory-cli api examples/inference/llama3.yaml infer_backend=vllm vllm_enforce_eager=true Download from ModelScope Hub If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope. 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. 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. 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. use_swanlab : true swanlab_run_name : test_run # optional When launching training tasks, you can log in to SwanLab in three ways: Add swanlab_api_key= to the yaml file, and set it to your API key. Set the environment variable SWANLAB_API_KEY to your API key. 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 Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [arxiv] Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [arxiv] Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [arxiv] Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [arxiv] Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [arxiv] Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [arxiv] Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [arxiv] Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [arxiv] Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [arxiv] Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [arxiv] Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [arxiv] Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [arxiv] Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [arxiv] Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [arxiv] Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [arxiv] Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [arxiv] Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [arxiv] Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [arxiv] Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [arxiv] Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [arxiv] Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [arxiv] Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [arxiv] Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [arxiv] Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [arxiv] Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [arxiv] Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [arxiv] Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [arxiv] Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [arxiv] Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [arxiv] Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [arxiv] Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [arxiv] Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [arxiv] Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [arxiv] Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [arxiv] Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [arxiv] Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [arxiv] Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [arxiv] Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [arxiv] Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [arxiv] Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [arxiv] Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [arxiv] Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [arxiv] Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [arxiv] Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [paper] Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [arxiv] Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [arxiv] Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [arxiv] Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [arxiv] Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [arxiv] Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [arxiv] Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [arxiv] Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [arxiv] Zhu et al. Are Large Language Models Good Statisticians?. 2024. [arxiv] Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [arxiv] Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [arxiv] He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [arxiv] Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [arxiv] Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [arxiv] Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [arxiv] Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [arxiv] Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [arxiv] Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh's Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [paper] Li et al. Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 2024. [arxiv] Yang et al. Financial Knowledge Large Language Model. 2024. [arxiv] Lin et al. DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging. 2024. [arxiv] Bako et al. Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization. 2024. [arxiv] Huang et al. RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. 2024. [arxiv] Jiang et al. LLM-Collaboration on Automatic Science Journalism for the General Audience. 2024. [arxiv] Inouye et al. Applied Auto-tuning on LoRA Hyperparameters. 2024. [paper] Qi et al. Research on Tibetan Tourism Viewpoints information generation system based on LLM. 2024. [arxiv] Xu et al. Course-Correction: Safety Alignment Using Synthetic Preferences. 2024. [arxiv] Sun et al. LAMBDA: A Large Model Based Data Agent. 2024. [arxiv] Zhu et al. CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare. 2024. [arxiv] Yu et al. Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment. 2024. [arxiv] Xie et al. The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. IALP 2024. [paper] Liu et al. Instruct-Code-Llama: Improving Capabilities of Language Model in Competition Level Code Generation by Online Judge Feedback. ICIC 2024. [paper] Wang et al. Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning. ICIC 2024. [paper] Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [arxiv] Zeng et al. Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions. 2024. [arxiv] Xia et al. Using Pre-trained Language Model for Accurate ESG Prediction. FinNLP 2024. [paper] Liang et al. I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm. 2024. [arxiv] Bai et al. Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation. CIKM 2024. [paper] Zhang et al. CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling. ACL 2024. [paper] StarWhisper: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B. 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: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B. CareGPT: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B. MachineMindset: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods. Luminia-13B-v3: A large language model specialized in generate metadata for stable diffusion. [demo] Chinese-LLaVA-Med: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B. AutoRE: A document-level relation extraction system based on large language models. NVIDIA RTX AI Toolkit: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX. LazyLLM: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory. RAG-Retrieval: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [blog] 360-LLaMA-Factory: A modified library that supports long sequence SFT & DPO using ring attention. Sky-T1: An o1-like model fine-tuned by NovaSky AI with very small cost. WeClone: One-stop solution for creating your digital avatar from chat logs. EmoLLM: A project about large language models (LLMs) and mental health. 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 / Llama 3 / Llama 4 / 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: @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.