LlamaFactory

• LlamaFactory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model for News Classifier (Chinese)
• A One-Stop Code-Free Model Fine-Tuning & Deployment Platform based on SageMaker and LlamaFactory (Chinese)
• LlamaFactory Multi-Modal Fine-Tuning Practice: Fine-Tuning Qwen2-VL for Personal Tourist Guide (Chinese)
• LlamaFactory: Fine-tuning Llama3 for Role-Playing (Chinese)

[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 LlamaFactory 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 LlamaFactory. 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.

• Wiki Demo (en)
• RefinedWeb (en)
• RedPajama V2 (en)
• Wikipedia (en)
• Wikipedia (zh)
• Pile (en)
• SkyPile (zh)
• FineWeb (en)
• FineWeb-Edu (en)
• CCI3-HQ (zh)
• CCI3-Data (zh)
• CCI4.0-M2-Base-v1 (en&zh)
• CCI4.0-M2-CoT-v1 (en&zh)
• CCI4.0-M2-Extra-v1 (en&zh)
• The Stack (en)
• StarCoder (en)

• Identity (en&zh)
• Stanford Alpaca (en)
• Stanford Alpaca (zh)
• Alpaca GPT4 (en&zh)
• Glaive Function Calling V2 (en&zh)
• LIMA (en)
• Guanaco Dataset (multilingual)
• BELLE 2M (zh)
• BELLE 1M (zh)
• BELLE 0.5M (zh)
• BELLE Dialogue 0.4M (zh)
• BELLE School Math 0.25M (zh)
• BELLE Multiturn Chat 0.8M (zh)
• UltraChat (en)
• OpenPlatypus (en)
• CodeAlpaca 20k (en)
• Alpaca CoT (multilingual)
• OpenOrca (en)
• SlimOrca (en)
• MathInstruct (en)
• Firefly 1.1M (zh)
• Wiki QA (en)
• Web QA (zh)
• WebNovel (zh)
• Nectar (en)
• deepctrl (en&zh)
• Advertise Generating (zh)
• ShareGPT Hyperfiltered (en)
• ShareGPT4 (en&zh)
• UltraChat 200k (en)
• Infinity Instruct (zh)
• AgentInstruct (en)
• LMSYS Chat 1M (en)
• Evol Instruct V2 (en)
• Cosmopedia (en)
• STEM (zh)
• Ruozhiba (zh)
• Neo-sft (zh)
• Magpie-Pro-300K-Filtered (en)
• Magpie-ultra-v0.1 (en)
• WebInstructSub (en)
• OpenO1-SFT (en&zh)
• Open-Thoughts (en)
• Open-R1-Math (en)
• Chinese-DeepSeek-R1-Distill (zh)
• LLaVA mixed (en&zh)
• Pokemon-gpt4o-captions (en&zh)
• DLR-Web (en)
• Open Assistant (de)
• Dolly 15k (de)
• Alpaca GPT4 (de)
• OpenSchnabeltier (de)
• Evol Instruct (de)
• Dolphin (de)
• Booksum (de)
• Airoboros (de)
• Ultrachat (de)

• DPO mixed (en&zh)
• UltraFeedback (en)
• COIG-P (zh)
• RLHF-V (en)
• VLFeedback (en)
• RLAIF-V (en)
• Orca DPO Pairs (en)
• HH-RLHF (en)
• Nectar (en)
• Orca DPO (de)
• KTO mixed (en)

Create an isolated Python environment with uv:

uv run llamafactory-cli webui

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

To enable Quantized LoRA (QLoRA) on Windows, you need to install bitsandbytes.

For most users, it is recommended to install the latest official release:

pip install bitsandbytes

If you are using uv to manage your virtual environment, it is recommended to install bitsandbytes after installing the GPU-enabled version of PyTorch:

uv pip install bitsandbytes --no-deps

[!IMPORTANT] Pay attention to the CUDA Toolkit version when installing bitsandbytes. Official bitsandbytes releases are built for specific CUDA Toolkit versions. On Windows x86-64, separate builds are currently provided for CUDA 11.8–12.6 and CUDA 12.8–12.9. Support for NVIDIA RTX 50 Series GPUs (e.g., RTX 5060 Ti, sm_120) requires the CUDA 12.8–12.9 builds.

If your environment uses an older CUDA version, or you need compatibility with older Windows / PyTorch combinations, you can install the third-party precompiled Windows wheel:

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.

To install LlamaFactory on Ascend NPU devices, please use Python 3.12 and install the extra dependencies with 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-910b-ubuntu
docker pull hiyouga/llamafactory:latest-a3-ubuntu
docker pull hiyouga/llamafactory:latest-950-ubuntu
docker pull hiyouga/llamafactory:latest-910b-openeuler
docker pull hiyouga/llamafactory:latest-a3-openeuler
docker pull hiyouga/llamafactory:latest-950-openeuler

# quay.io
docker pull quay.io/ascend/llamafactory:latest-910b-ubuntu
docker pull quay.io/ascend/llamafactory:latest-a3-ubuntu
docker pull quay.io/ascend/llamafactory:latest-950-ubuntu
docker pull quay.io/ascend/llamafactory:latest-910b-openeuler
docker pull quay.io/ascend/llamafactory:latest-a3-openeuler
docker pull quay.io/ascend/llamafactory:latest-950-openeuler

Install BitsAndBytes

To use QLoRA based on bitsandbytes on Ascend NPU, please follow these 3 steps:

1. 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 .

1. Install transformers from the main branch.

git clone -b main https://github.com/huggingface/transformers.git
cd transformers
pip install .

1. Set double_quantization: false in the configuration. You can refer to the example.

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

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.

1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [arxiv]
2. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [arxiv]
3. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [arxiv]
4. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [arxiv]
5. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [arxiv]
6. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [arxiv]
7. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [arxiv]
8. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [arxiv]
9. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [arxiv]
10. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [arxiv]
11. Yang et al. LaCo: Large Language Model Pruning via Layer Collapse. 2024. [arxiv]
12. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [arxiv]
13. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [arxiv]
14. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [arxiv]
15. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [arxiv]
16. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [arxiv]
17. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [arxiv]
18. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [arxiv]
19. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [arxiv]
20. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [arxiv]
21. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [arxiv]
22. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [arxiv]
23. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [arxiv]
24. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [arxiv]
25. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [arxiv]
26. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [arxiv]
27. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [arxiv]
28. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [arxiv]
29. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [arxiv]
30. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [arxiv]
31. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [arxiv]
32. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [arxiv]
33. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [arxiv]
34. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [arxiv]
35. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [arxiv]
36. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [arxiv]
37. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [arxiv]
38. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [arxiv]
39. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [arxiv]
40. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [arxiv]
41. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [arxiv]
42. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [arxiv]
43. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [arxiv]
44. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [paper]
45. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [arxiv]
46. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [arxiv]
47. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [arxiv]
48. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [arxiv]
49. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [arxiv]
50. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [arxiv]
51. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [arxiv]
52. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [arxiv]
53. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [arxiv]
54. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [arxiv]
55. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [arxiv]
56. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [arxiv]
57. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [arxiv]
58. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [arxiv]
59. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [arxiv]
60. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [arxiv]
61. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [arxiv]
62. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh's Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [paper]
63. Li et al. Calibrating LLMs with Preference Optimization on Thought Trees for Generating Rationale in Science Question Scoring. 2024. [arxiv]
64. Yang et al. Financial Knowledge Large Language Model. 2024. [arxiv]
65. Lin et al. DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging. 2024. [arxiv]
66. Bako et al. Evaluating the Semantic Profiling Abilities of LLMs for Natural Language Utterances in Data Visualization. 2024. [arxiv]
67. Huang et al. RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization. 2024. [arxiv]
68. Jiang et al. LLM-Collaboration on Automatic Science Journalism for the General Audience. 2024. [arxiv]
69. Inouye et al. Applied Auto-tuning on LoRA Hyperparameters. 2024. [paper]
70. Qi et al. Research on Tibetan Tourism Viewpoints information generation system based on LLM. 2024. [arxiv]
71. Xu et al. Course-Correction: Safety Alignment Using Synthetic Preferences. 2024. [arxiv]
72. Sun et al. LAMBDA: A Large Model Based Data Agent. 2024. [arxiv]
73. Zhu et al. CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare. 2024. [arxiv]
74. Yu et al. Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment. 2024. [arxiv]
75. Xie et al. The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning. IALP 2024. [paper]
76. Liu et al. Instruct-Code-Llama: Improving Capabilities of Language Model in Competition Level Code Generation by Online Judge Feedback. ICIC 2024. [paper]
77. Wang et al. Cybernetic Sentinels: Unveiling the Impact of Safety Data Selection on Model Security in Supervised Fine-Tuning. ICIC 2024. [paper]
78. Xia et al. Understanding the Performance and Estimating the Cost of LLM Fine-Tuning. 2024. [arxiv]
79. Zeng et al. Perceive, Reflect, and Plan: Designing LLM Agent for Goal-Directed City Navigation without Instructions. 2024. [arxiv]
80. Xia et al. Using Pre-trained Language Model for Accurate ESG Prediction. FinNLP 2024. [paper]
81. Liang et al. I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm. 2024. [arxiv]
82. Bai et al. Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation. CIKM 2024. [paper]
83. Zhang et al. CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling. ACL 2024. [paper]
84. StarWhisper: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
85. DISC-LawLLM: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
86. Sunsimiao: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
87. CareGPT: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
88. 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.
89. Luminia-13B-v3: A large language model specialized in generate metadata for stable diffusion. [demo]
90. Chinese-LLaVA-Med: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
91. AutoRE: A document-level relation extraction system based on large language models.
92. NVIDIA RTX AI Toolkit: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX.
93. LazyLLM: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LlamaFactory.
94. RAG-Retrieval: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [blog]
95. 360-LLaMA-Factory: A modified library that supports long sequence SFT & DPO using ring attention.
96. Sky-T1: An o1-like model fine-tuned by NovaSky AI with very small cost.
97. WeClone: One-stop solution for creating your digital avatar from chat logs.
98. EmoLLM: A project about large language models (LLMs) and mental health.

• LlamaFactory：微调 DeepSeek-R1-Distill-Qwen-7B 模型实现新闻标题分类器（中文）
• 基于 Amazon SageMaker 和 LlamaFactory 打造一站式无代码模型微调部署平台 Model Hub（中文）
• LlamaFactory 多模态微调实践：微调 Qwen2-VL 构建文旅大模型（中文）
• LlamaFactory：微调 Llama3 模型实现角色扮演（中文）

[25/07/02] 我们支持了 GLM-4.1V-9B-Thinking 模型的微调。

[25/04/28] 我们支持了 Qwen3 系列模型的微调。

[25/04/21] 我们支持了 Muon 优化器。详细用法请参照 examples。感谢 @tianshijing 的 PR。

[25/04/16] 我们支持了 InternVL3 模型的微调。查看 PR #7258 以使用。

[25/04/14] 我们支持了 GLM-Z1 和 Kimi-VL 模型的微调。

[25/04/06] 我们支持了 Llama 4 模型的微调。查看 PR #7611 以使用。

[25/03/31] 我们支持了 Qwen2.5 Omni 模型的微调。查看 PR #7537 以使用。

[25/03/15] 我们支持了 SGLang 推理后端，请使用 infer_backend: sglang 启用。

[25/03/12] 我们支持了 Gemma 3 模型的微调。

[25/02/24] 我们宣布开源 EasyR1，一个高效可扩展的多模态强化学习框架，支持高效的 GRPO 训练。

[25/02/11] 我们支持了在导出模型时保存 Ollama 配置文件。详细用法请参照 examples。

[25/02/05] 我们支持了在语音理解任务上微调 Qwen2-Audio 和 MiniCPM-o-2.6 模型。

[25/01/31] 我们支持了 DeepSeek-R1 和 Qwen2.5-VL 模型的微调。

[25/01/15] 我们支持了 APOLLO 优化器。详细用法请参照 examples。

[25/01/14] 我们支持了 MiniCPM-o-2.6 和 MiniCPM-V-2.6 模型的微调。 感谢 @BUAADreamer 的 PR.

[25/01/14] 我们支持了 InternLM 3 模型的微调。感谢 @hhaAndroid 的 PR。

[25/01/10] 我们支持了 Phi-4 模型的微调。

[24/12/21] 我们支持了使用 SwanLab 跟踪与可视化实验。详细用法请参考 此部分。

[24/11/27] 我们支持了 Skywork-o1 模型的微调和 OpenO1 数据集。

[24/10/09] 我们支持了从 魔乐社区 下载预训练模型和数据集。详细用法请参照 此教程。

[24/09/19] 我们支持了 Qwen2.5 模型的微调。

[24/08/30] 我们支持了 Qwen2-VL 模型的微调。感谢 @simonJJJ 的 PR。

[24/08/27] 我们支持了 Liger Kernel。请使用 enable_liger_kernel: true 来加速训练。

[24/08/09] 我们支持了 Adam-mini 优化器。详细用法请参照 examples。感谢 @relic-yuexi 的 PR。

[24/07/04] 我们支持了无污染打包训练。请使用 neat_packing: true 参数。感谢 @chuan298 的 PR。

[24/06/16] 我们支持了 PiSSA 算法。详细用法请参照 examples。

[24/06/07] 我们支持了 Qwen2 和 GLM-4 模型的微调。

[24/05/26] 我们支持了 SimPO 偏好对齐算法。详细用法请参照 examples。

[24/05/20] 我们支持了 PaliGemma 系列模型的微调。注意 PaliGemma 是预训练模型，你需要使用 paligemma 模板进行微调使其获得对话能力。

[24/05/18] 我们支持了 KTO 偏好对齐算法。详细用法请参照 examples。

[24/05/14] 我们支持了昇腾 NPU 设备的训练和推理。详情请查阅安装部分。

[24/04/26] 我们支持了多模态模型 LLaVA-1.5 的微调。详细用法请参照 examples。

[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 Colab 笔记本。Hugging Face 社区公开了两个利用 LlamaFactory 微调的 Llama-3 模型，详情请见 Llama3-8B-Chinese-Chat 和 Llama3-Chinese。

[24/04/21] 我们基于 AstraMindAI 的仓库支持了 混合深度训练。详细用法请参照 examples。

[24/04/16] 我们支持了 BAdam 优化器。详细用法请参照 examples。

[24/04/16] 我们支持了 unsloth 的长序列训练（24GB 可训练 Llama-2-7B-56k）。该方法相比 FlashAttention-2 提供了 117% 的训练速度和 50% 的显存节约。更多数据请见此页面。

[24/03/31] 我们支持了 ORPO。详细用法请参照 examples。

[24/03/21] 我们的论文 "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models" 可在 arXiv 上查看！

[24/03/20] 我们支持了能在 2x24GB GPU 上微调 70B 模型的 FSDP+QLoRA。详细用法请参照 examples。

[24/03/13] 我们支持了 LoRA+。详细用法请参照 examples。

[24/03/07] 我们支持了 GaLore 优化器。详细用法请参照 examples。

[24/03/07] 我们集成了 vLLM 以实现极速并发推理。请使用 infer_backend: vllm 来获得 270% 的推理速度。

[24/02/28] 我们支持了 DoRA 微调。请使用 use_dora: true 参数进行 DoRA 微调。

[24/02/15] 我们支持了 LLaMA Pro 提出的块扩展方法。详细用法请参照 examples。

[24/02/05] Qwen1.5（Qwen2 测试版）系列模型已在 LlamaFactory 中实现微调支持。详情请查阅该博客页面。

[24/01/18] 我们针对绝大多数模型实现了 Agent 微调，微调时指定 dataset: glaive_toolcall_zh 即可使模型获得工具调用能力。

[23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 unsloth 的 LoRA 训练加速。请使用 use_unsloth: true 参数启用 unsloth 优化。该方法可提供 170% 的训练速度，详情请查阅此页面。

[23/12/12] 我们支持了微调最新的混合专家模型 Mixtral 8x7B。硬件需求请查阅此处。

[23/12/01] 我们支持了从 魔搭社区 下载预训练模型和数据集。详细用法请参照 此教程。

[23/10/21] 我们支持了 NEFTune 训练技巧。请使用 neftune_noise_alpha: 5 参数启用 NEFTune。

[23/09/27] 我们针对 LLaMA 模型支持了 LongLoRA 提出的 $S^2$-Attn。请使用 shift_attn: true 参数以启用该功能。

[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。详细用法请参照 examples。

[23/09/10] 我们支持了 FlashAttention-2。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 flash_attn: fa2 参数以启用 FlashAttention-2。

[23/08/12] 我们支持了 RoPE 插值来扩展 LLaMA 模型的上下文长度。请使用 rope_scaling: linear 参数训练模型或使用 rope_scaling: dynamic 参数评估模型。

[23/08/11] 我们支持了指令模型的 DPO 训练。详细用法请参照 examples。

[23/07/31] 我们支持了数据流式加载。请使用 streaming: true 和 max_steps: 10000 参数来流式加载数据集。

[23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目（LLaMA-2 / Baichuan）。

[23/07/18] 我们开发了支持训练和测试的浏览器一体化界面。请使用 train_web.py 在您的浏览器中微调模型。感谢 @KanadeSiina 和 @codemayq 在该功能开发中付出的努力。

[23/07/09] 我们开源了 FastEdit ⚡🩹，一个简单易用的、能迅速编辑大模型事实记忆的工具包。如果您感兴趣请关注我们的 FastEdit 项目。

[23/06/29] 我们提供了一个可复现的指令模型微调示例，详细内容请查阅 Baichuan-7B-sft。

[23/06/22] 我们对齐了示例 API 与 OpenAI API 的格式，您可以将微调模型接入任意基于 ChatGPT 的应用中。

[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 QLoRA）。详细用法请参照 examples。

• Wiki Demo (en)
• RefinedWeb (en)
• RedPajama V2 (en)
• Wikipedia (en)
• Wikipedia (zh)
• Pile (en)
• SkyPile (zh)
• FineWeb (en)
• FineWeb-Edu (en)
• CCI3-HQ (zh)
• CCI3-Data (zh)
• CCI4.0-M2-Base-v1 (en&zh)
• CCI4.0-M2-CoT-v1 (en&zh)
• CCI4.0-M2-Extra-v1 (en&zh)
• The Stack (en)
• StarCoder (en)

• Identity (en&zh)
• Stanford Alpaca (en)
• Stanford Alpaca (zh)
• Alpaca GPT4 (en&zh)
• Glaive Function Calling V2 (en&zh)
• LIMA (en)
• Guanaco Dataset (multilingual)
• BELLE 2M (zh)
• BELLE 1M (zh)
• BELLE 0.5M (zh)
• BELLE Dialogue 0.4M (zh)
• BELLE School Math 0.25M (zh)
• BELLE Multiturn Chat 0.8M (zh)
• UltraChat (en)
• OpenPlatypus (en)
• CodeAlpaca 20k (en)
• Alpaca CoT (multilingual)
• OpenOrca (en)
• SlimOrca (en)
• MathInstruct (en)
• Firefly 1.1M (zh)
• Wiki QA (en)
• Web QA (zh)
• WebNovel (zh)
• Nectar (en)
• deepctrl (en&zh)
• Advertise Generating (zh)
• ShareGPT Hyperfiltered (en)
• ShareGPT4 (en&zh)
• UltraChat 200k (en)
• Infinity Instruct (zh)
• AgentInstruct (en)
• LMSYS Chat 1M (en)
• Evol Instruct V2 (en)
• Cosmopedia (en)
• STEM (zh)
• Ruozhiba (zh)
• Neo-sft (zh)
• Magpie-Pro-300K-Filtered (en)
• Magpie-ultra-v0.1 (en)
• WebInstructSub (en)
• OpenO1-SFT (en&zh)
• Open-Thoughts (en)
• Open-R1-Math (en)
• Chinese-DeepSeek-R1-Distill (zh)
• LLaVA mixed (en&zh)
• Pokemon-gpt4o-captions (en&zh)
• DLR-Web (en)
• Open Assistant (de)
• Dolly 15k (de)
• Alpaca GPT4 (de)
• OpenSchnabeltier (de)
• Evol Instruct (de)
• Dolphin (de)
• Booksum (de)
• Airoboros (de)
• Ultrachat (de)

• DPO mixed (en&zh)
• UltraFeedback (en)
• COIG-P (zh)
• RLHF-V (en)
• VLFeedback (en)
• RLAIF-V (en)
• Orca DPO Pairs (en)
• HH-RLHF (en)
• Nectar (en)
• Orca DPO (de)
• KTO mixed (en)

使用 uv 创建隔离的 Python 环境：

uv run llamafactory-cli webui

安装 PyTorch

Windows 平台需要额外手动安装 GPU 版本的 PyTorch 依赖包，您可以参考官方网站和以下命令安装并测试 PyTorch 是否正确安装。

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())"

如果看到 True 则说明安装成功。

若遇到类似 Can't pickle local object 的报错，请设置 dataloader_num_workers: 0。

安装 BitsAndBytes

如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装 bitsandbytes。

对于大多数用户，建议优先使用官方发布的最新版本：

pip install bitsandbytes

如果使用 uv 管理虚拟环境，建议在安装好 GPU 版本 PyTorch 之后，再执行：

uv pip install bitsandbytes --no-deps

[!IMPORTANT] 安装 bitsandbytes 时，请注意 CUDA Toolkit 版本。bitsandbytes 的官方发布包是按不同 CUDA 版本分别构建的，Windows x86-64 目前提供了面向 CUDA 11.8–12.6 和 CUDA 12.8–12.9 的不同构建；其中支持 RTX 50 系列（如 RTX 5060 Ti，sm_120）的构建对应 CUDA 12.8–12.9。

若当前环境的 CUDA 版本较旧，或者需要兼容较老的 Windows / PyTorch 组合，可以使用第三方预编译版本：

pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl

安装 Flash Attention-2

如果要在 Windows 平台上开启 FlashAttention-2，请使用 flash-attention-windows-wheel 中的脚本自行编译与安装。

在昇腾 NPU 设备上安装 LlamaFactory 时，请使用 Python 3.12，并使用 pip install -r requirements/npu.txt 命令安装额外依赖项。此外，还需要安装 Ascend CANN Toolkit 与 Kernels，安装方法请参考安装教程。

您可以直接下载预安装的最新docker镜像：

# Docker Hub
docker pull hiyouga/llamafactory:latest-910b-ubuntu
docker pull hiyouga/llamafactory:latest-a3-ubuntu
docker pull hiyouga/llamafactory:latest-950-ubuntu
docker pull hiyouga/llamafactory:latest-910b-openeuler
docker pull hiyouga/llamafactory:latest-a3-openeuler
docker pull hiyouga/llamafactory:latest-950-openeuler

# quay.io
docker pull quay.io/ascend/llamafactory:latest-910b-ubuntu
docker pull quay.io/ascend/llamafactory:latest-a3-ubuntu
docker pull quay.io/ascend/llamafactory:latest-950-ubuntu
docker pull quay.io/ascend/llamafactory:latest-910b-openeuler
docker pull quay.io/ascend/llamafactory:latest-a3-openeuler
docker pull quay.io/ascend/llamafactory:latest-950-openeuler

安装 BitsAndBytes

如果要在 Ascend NPU 上进行基于 bitsandbytes 的 QLoRA 量化微调，请执行如下步骤：

1. 手动编译 bitsandbytes：请参考安装文档完成 NPU 版的 bitsandbytes 安装，编译要求环境 cmake 版本不低于 3.22.1，g++ 版本不低于 12.x。

# 从源码安装 bitsandbytes
# 克隆 bitsandbytes 仓库, Ascend NPU 目前在 multi-backend-refactor 中支持
git clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git
cd bitsandbytes/

# 安装依赖
pip install -r requirements-dev.txt

# 安装编译工具依赖，该步骤在不同系统上命令有所不同，供参考
apt-get install -y build-essential cmake

# 编译 & 安装
cmake -DCOMPUTE_BACKEND=npu -S .
make
pip install .

1. 安装 transformers 的 main 分支版本。

git clone -b main https://github.com/huggingface/transformers.git
cd transformers
pip install .

1. 在训练参数中设置 double_quantization: false，可参考示例。

CUDA 用户：

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

昇腾 NPU 用户：

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

AMD ROCm 用户：

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

您可以通过移除 Dockerfile 中 VOLUME [ "/root/.cache/huggingface", "/app/shared_data", "/app/output" ] 的注释来使用数据卷。

在构建 Docker 时使用参数 -v ./hf_cache:/root/.cache/huggingface 来挂载数据卷。各个数据卷的含义表示如下。

• hf_cache：使用宿主机的 Hugging Face 缓存文件夹。
• shared_data：宿主机中存放数据集的文件夹路径。
• output：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。

1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [arxiv]
2. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [arxiv]
3. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [arxiv]
4. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [arxiv]
5. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [arxiv]
6. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [arxiv]
7. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [arxiv]
8. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [arxiv]
9. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [arxiv]
10. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [arxiv]
11. Yang et al. LaCo: Large Language Model Pruning via Layer Collapse. 2024. [arxiv]
12. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [arxiv]
13. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [arxiv]
14. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [arxiv]
15. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [arxiv]
16. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [arxiv]
17. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [arxiv]
18. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [arxiv]
19. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [arxiv]
20. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [arxiv]
21. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [arxiv]
22. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [arxiv]
23. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [arxiv]
24. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [arxiv]
25. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 2024. [arxiv]
26. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [arxiv]
27. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [arxiv]
28. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [arxiv]
29. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [arxiv]
30. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 2024. [arxiv]
31. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [arxiv]
32. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [arxiv]
33. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [arxiv]
34. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [arxiv]
35. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [arxiv]
36. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [arxiv]
37. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [arxiv]
38. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [arxiv]
39. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [arxiv]
40. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [arxiv]
41. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [arxiv]
42. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [arxiv]
43. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [arxiv]
44. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [paper]
45. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [arxiv]
46. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [arxiv]
47. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [arxiv]
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83. StarWhisper: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
84. DISC-LawLLM: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
85. Sunsimiao: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
86. CareGPT: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
87. MachineMindset：MBTI性格大模型项目，根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
88. Luminia-13B-v3：一个用于生成 Stable Diffusion 提示词的大型语言模型。[demo]
89. Chinese-LLaVA-Med：中文多模态医学大模型，基于 LLaVA-1.5-7B 在中文多模态医疗数据上微调而得。
90. AutoRE：基于大语言模型的文档级关系抽取系统。
91. NVIDIA RTX AI Toolkit：在 Windows 主机上利用英伟达 RTX 设备进行大型语言模型微调的开发包。
92. LazyLLM：一个低代码构建多 Agent 大模型应用的开发工具，支持基于 LlamaFactory 的模型微调.
93. RAG-Retrieval：一个全链路 RAG 检索模型微调、推理和蒸馏代码库。[blog]
94. 360-LLaMA-Factory：一个魔改后的代码库，通过 Ring Attention 支持长序列的 SFT 和 DPO 训练。
95. Sky-T1：由 NovaSky AI 微调的低成本类 o1 长推理模型。
96. WeClone：从聊天记录创造数字分身的一站式解决方案。