![# LLaMA Factory](assets/logo.png)

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

👋 Join our WeChat, NPU, Lab4AI, LLaMA Factory Online user group.

[ English | 中文 ]

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

https://github.com/user-attachments/assets/3991a3a8-4276-4d30-9cab-4cb0c4b9b99e

Start local training:

Please refer to usage

Start cloud training:

Colab (free): https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
PAI-DSW (free trial): https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
LLaMA Factory Online: https://www.llamafactory.com.cn/?utm_source=LLaMA-Factory
Alaya NeW (cloud GPU deal): https://docs.alayanew.com/docs/documents/useGuide/LLaMAFactory/mutiple/?utm_source=LLaMA-Factory

Read technical notes:

Documentation (WIP): 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
Official Blog: https://blog.llamafactory.net/en/
Official Course: https://www.lab4ai.cn/course/detail?id=7c13e60f6137474eb40f6fd3983c0f46&utm_source=LLaMA-Factory

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

Features
Blogs
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, Qwen3, Qwen3-VL, DeepSeek, Gemma, GLM, 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, Muon, OFT, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA.
Practical tricks: FlashAttention-2, Unsloth, Liger Kernel, KTransformers, 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 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

[!TIP] Now we have a dedicated blog for LLaMA Factory!

Website: https://blog.llamafactory.net/en/

All Blogs

Changelog

[25/10/26] We support Megatron-core training backend with mcore_adapter. See PR #9237 to get started.

[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.

Full Changelog

[25/07/02] We supported fine-tuning the GLM-4.1V-9B-Thinking model.

[25/04/28] We supported fine-tuning the Qwen3 model family.

[25/04/21] We supported the Muon optimizer. See examples for usage. Thank @tianshijing's PR.

[25/04/16] We supported fine-tuning the InternVL3 model. See PR #7258 to get started.

[25/04/14] We supported fine-tuning the GLM-Z1 and Kimi-VL models.

[25/04/06] We supported fine-tuning the Llama 4 model. See PR #7611 to get started.

[25/03/31] We supported fine-tuning the Qwen2.5 Omni model. See PR #7537 to get started.

[25/03/15] We supported SGLang as inference backend. Try

infer_backend: sglang

to accelerate inference.

[25/03/12] We supported fine-tuning the Gemma 3 model.

[25/02/24] Announcing EasyR1, an efficient, scalable and multi-modality RL training framework for efficient GRPO training.

[25/02/11] We supported saving the Ollama modelfile when exporting the model checkpoints. See examples for usage.

[25/02/05] We supported fine-tuning the Qwen2-Audio and MiniCPM-o-2.6 on audio understanding tasks.

[25/01/31] We supported fine-tuning the DeepSeek-R1 and Qwen2.5-VL models.

[25/01/15] We supported APOLLO optimizer. See examples for usage.

[25/01/14] We supported fine-tuning the MiniCPM-o-2.6 and MiniCPM-V-2.6 models. Thank @BUAADreamer's PR.

[25/01/14] We supported fine-tuning the InternLM 3 models. Thank @hhaAndroid's PR.

[25/01/10] We supported fine-tuning the Phi-4 model.

[24/12/21] We supported using SwanLab for experiment tracking and visualization. See this section for details.

[24/11/27] We supported fine-tuning the Skywork-o1 model and the OpenO1 dataset.

[24/10/09] We supported downloading pre-trained models and datasets from the Modelers Hub. See this tutorial for usage.

[24/09/19] We supported fine-tuning the Qwen2.5 models.

[24/08/30] We supported fine-tuning the Qwen2-VL models. Thank @simonJJJ's PR.

[24/08/27] We supported Liger Kernel. Try

enable_liger_kernel: true

for efficient training.

[24/08/09] We supported Adam-mini optimizer. See examples for usage. Thank @relic-yuexi's PR.

[24/07/04] We supported contamination-free packed training. Use

neat_packing: true

to activate it. Thank @chuan298's PR.

[24/06/16] We supported PiSSA algorithm. See examples for usage.

[24/06/07] We supported fine-tuning the Qwen2 and GLM-4 models.

[24/05/26] We supported SimPO algorithm for preference learning. See examples for usage.

[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

paligemma

template for chat completion.

[24/05/18] We supported KTO algorithm for preference learning. See examples for usage.

[24/05/14] We supported training and inference on the Ascend NPU devices. Check installation section for details.

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

[24/04/16] We supported BAdam optimizer. See examples 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.

[24/03/31] We supported ORPO. See examples for usage.

[24/03/21] Our paper "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models" is available at arXiv!

[24/03/20] We supported FSDP+QLoRA that fine-tunes a 70B model on 2x24GB GPUs. See examples for usage.

[24/03/13] We supported LoRA+. See examples for usage.

[24/03/07] We supported GaLore optimizer. See examples for usage.

[24/03/07] We integrated vLLM for faster and concurrent inference. Try

infer_backend: vllm

to enjoy 270% inference speed.

[24/02/28] We supported weight-decomposed LoRA (DoRA). Try

use_dora: true

to activate DoRA training.

[24/02/15] We supported block expansion proposed by LLaMA Pro. See examples for usage.

[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this blog post 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'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 for details.

[23/12/12] We supported fine-tuning the latest MoE model Mixtral 8x7B in our framework. See hardware requirement here.

[23/12/01] We supported downloading pre-trained models and datasets from the ModelScope Hub. See this tutorial for usage.

[23/10/21] We supported NEFTune trick for fine-tuning. Try

neftune_noise_alpha: 5

argument to activate NEFTune.

[23/09/27] We supported $S^2$-Attn proposed by 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 for usage.

[23/09/10] We supported FlashAttention-2. 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 for instruction-tuned models. See examples 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 / Baichuan) 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 and @codemayq for their efforts in the development.

[23/07/09] We released FastEdit ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow 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 for details.

[23/06/22] We aligned the demo API with the OpenAI's 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). See examples for usage.

[!TIP] If you cannot use the latest feature, please pull the latest code and install LLaMA-Factory again.

Supported Models

Model	Model size	Template
BLOOM/BLOOMZ	560M/1.1B/1.7B/3B/7.1B/176B	-
DeepSeek (LLM/Code/MoE)	7B/16B/67B/236B	deepseek
DeepSeek 3-3.2	236B/671B	deepseek3
DeepSeek R1 (Distill)	1.5B/7B/8B/14B/32B/70B/671B	deepseekr1
ERNIE-4.5	0.3B/21B/300B	ernie_nothink
Falcon/Falcon H1	0.5B/1.5B/3B/7B/11B/34B/40B/180B	falcon/falcon_h1
Gemma/Gemma 2/CodeGemma	2B/7B/9B/27B	gemma/gemma2
Gemma 3/Gemma 3n	270M/1B/4B/6B/8B/12B/27B	gemma3/gemma3n
GLM-4/GLM-4-0414/GLM-Z1	9B/32B	glm4/glmz1
GLM-4.5/GLM-4.5(6)V	9B/106B/355B	glm4_moe/glm4_5v
GPT-2	0.1B/0.4B/0.8B/1.5B	-
GPT-OSS	20B/120B	gpt_oss
Granite 3-4	1B/2B/3B/7B/8B	granite3/granite4
Hunyuan/Hunyuan1.5 (MT)	0.5B/1.8B/4B/7B/13B	hunyuan/hunyuan_small
InternLM 2-3	7B/8B/20B	intern2
InternVL 2.5-3.5	1B/2B/4B/8B/14B/30B/38B/78B/241B	intern_vl
Intern-S1-mini	8B	intern_s1
Kimi-VL	16B	kimi_vl
Ling 2.0 (mini/flash)	16B/100B	bailing_v2
LFM 2.5 (VL)	1.2B/1.6B	lfm2/lfm2_vl
Llama	7B/13B/33B/65B	-
Llama 2	7B/13B/70B	llama2
Llama 3-3.3	1B/3B/8B/70B	llama3
Llama 4	109B/402B	llama4
Llama 3.2 Vision	11B/90B	mllama
LLaVA-1.5	7B/13B	llava
LLaVA-NeXT	7B/8B/13B/34B/72B/110B	llava_next
LLaVA-NeXT-Video	7B/34B	llava_next_video
MiMo	7B/309B	mimo/mimo_v2
MiniCPM 4	0.5B/8B	cpm4
MiniCPM-o/MiniCPM-V 4.5	8B/9B	minicpm_o/minicpm_v
MiniMax-M1/MiniMax-M2	229B/456B	minimax1/minimax2
Ministral 3	3B/8B/14B	ministral3
Mistral/Mixtral	7B/8x7B/8x22B	mistral
PaliGemma/PaliGemma2	3B/10B/28B	paligemma
Phi-3/Phi-3.5	4B/14B	phi
Phi-3-small	7B	phi_small
Phi-4-mini/Phi-4	3.8B/14B	phi4_mini/phi4
Pixtral	12B	pixtral
Qwen2 (Code/Math/MoE/QwQ)	0.5B/1.5B/3B/7B/14B/32B/72B/110B	qwen
Qwen3 (MoE/Instruct/Thinking/Next)	0.6B/1.7B/4B/8B/14B/32B/80B/235B	qwen3/qwen3_nothink
Qwen2-Audio	7B	qwen2_audio
Qwen2.5-Omni	3B/7B	qwen2_omni
Qwen3-Omni	30B	qwen3_omni
Qwen2-VL/Qwen2.5-VL/QVQ	2B/3B/7B/32B/72B	qwen2_vl
Qwen3-VL	2B/4B/8B/30B/32B/235B	qwen3_vl
Seed (OSS/Coder)	8B/36B	seed_oss/seed_coder
StarCoder 2	3B/7B/15B	-
TeleChat 2-2.5	3B/7B/35B/115B	telechat2
Yuan 2	2B/51B/102B	yuan

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

If the model has both reasoning and non-reasoning versions, please use the
_nothink
suffix to distinguish between them. For example,
qwen3
and
qwen3_nothink
.

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	✅	✅	✅	✅	✅	✅
Supervised Fine-Tuning	✅	✅	✅	✅	✅	✅
Reward Modeling	✅	✅	✅	✅	✅	✅
PPO Training	✅	✅	✅	✅	✅	✅
DPO Training	✅	✅	✅	✅	✅	✅
KTO Training	✅	✅	✅	✅	✅	✅
ORPO Training	✅	✅	✅	✅	✅	✅
SimPO Training	✅	✅	✅	✅	✅	✅

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

Provided Datasets

Pre-training datasets

Supervised fine-tuning datasets

Preference datasets

Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.

pip install "huggingface_hub<1.0.0"
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/LlamaFactory.git
cd LlamaFactory
pip install -e .
pip install -r requirements/metrics.txt

Optional dependencies available:

metrics

deepspeed

. Install with:

pip install -e . && pip install -r requirements/metrics.txt -r requirements/deepspeed.txt

Additional dependencies for specific features are available in

examples/requirements/

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 run llamafactory-cli webui

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:

pip install -r requirements/npu.txt

. Additionally, you need to install the Ascend CANN Toolkit and Kernels. Please follow the installation tutorial.

You can also download the pre-built Docker images:

# Docker Hub
docker pull hiyouga/llamafactory:latest-npu-a2
docker pull hiyouga/llamafactory:latest-npu-a3

# quay.io
docker pull quay.io/ascend/llamafactory:latest-npu-a2
docker pull quay.io/ascend/llamafactory:latest-npu-a3

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 Qwen3-4B-Instruct model, respectively.

llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml
llamafactory-cli chat examples/inference/qwen3_lora_sft.yaml
llamafactory-cli export examples/merge_lora/qwen3_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 \
    -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 \
    -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 \
    -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.
```
shared_data
```
: 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/qwen3.yaml infer_backend=vllm vllm_enforce_eager=true

[!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.

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=<your_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: BLOOM / DeepSeek / Falcon / Gemma / GLM-4 / GPT-2 / Granite / InternLM / Llama / Llama 2 / Llama 3 / Llama 4 / MiniCPM / Mistral/Mixtral/Pixtral / Phi-3/Phi-4 / Qwen / StarCoder 2 / TeleChat2 / 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.

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