![# LLaMA Factory](assets/logo.png) [![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers) [![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE) [![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main) [![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/) [![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/) [![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls) [![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK) [![Spaces](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue)](https://huggingface.co/spaces/hiyouga/LLaMA-Board) [![Studios](https://img.shields.io/badge/ModelScope-Open%20In%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board) 👋 Join our [WeChat](assets/wechat.jpg). \[ English | [中文](README_zh.md) \] ## LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory Preview LLaMA Board at **[🤗 Spaces](https://huggingface.co/spaces/hiyouga/LLaMA-Board)** or **[ModelScope](https://modelscope.cn/studios/hiyouga/LLaMA-Board)**. Launch LLaMA Board via `CUDA_VISIBLE_DEVICES=0 python src/train_web.py`. (multiple GPUs are not supported yet in this mode) Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU. https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1 ## Table of Contents - [Benchmark](#benchmark) - [Changelog](#changelog) - [Supported Models](#supported-models) - [Supported Training Approaches](#supported-training-approaches) - [Provided Datasets](#provided-datasets) - [Requirement](#requirement) - [Getting Started](#getting-started) - [Projects using LLaMA Factory](#projects-using-llama-factory) - [License](#license) - [Citation](#citation) - [Acknowledgement](#acknowledgement) ## Benchmark Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA-Factory's LoRA tuning offers up to **3.7 times faster** training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA-Factory's QLoRA further improves the efficiency regarding the GPU memory. ![benchmark](assets/benchmark.svg)
Definitions - **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024) - **Rouge Score**: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024) - **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024) - We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA-Factory's LoRA tuning.
## Changelog [23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `--use_unsloth` argument to activate unsloth patch. It achieves 1.7x speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details. [23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement). [23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#use-modelscope-hub-optional) for usage.
Full Changelog [23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neftune_noise_alpha` argument to activate NEFTune, e.g., `--neftune_noise_alpha 5`. [23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention. [23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models. [23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs. [23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings. [23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models. [23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode. [23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details. [23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development. [23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested. [23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details. [23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**. [23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models.
## Supported Models | Model | Model size | Default module | Template | | -------------------------------------------------------- | --------------------------- | ----------------- | --------- | | [Baichuan](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan | | [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan2 | | [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | | [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - | | [ChatGLM3](https://huggingface.co/THUDM/chatglm3-6b) | 6B | query_key_value | chatglm3 | | [Falcon](https://huggingface.co/tiiuae) | 7B/40B/180B | query_key_value | falcon | | [InternLM](https://huggingface.co/internlm) | 7B/20B | q_proj,v_proj | intern | | [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - | | [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 | | [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral | | [Mixtral](https://huggingface.co/mistralai) | 8x7B | q_proj,v_proj | mistral | | [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | Wqkv | - | | [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen | | [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse | | [Yi](https://huggingface.co/01-ai) | 6B/34B | q_proj,v_proj | yi | | [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan | > [!NOTE] > **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules. > > For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "chat" models. Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported. ## Supported Training Approaches | Approach | Full-parameter | Partial-parameter | LoRA | QLoRA | | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ | | Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | | DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: | > [!NOTE] > Use `--quantization_bit 4` argument to enable QLoRA. ## Provided Datasets
Pre-training datasets - [Wiki Demo (en)](data/wiki_demo.txt) - [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) - [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) - [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220) - [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered) - [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile) - [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B) - [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack) - [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
Supervised fine-tuning datasets - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca) - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [Self-cognition (zh)](data/self_cognition.json) - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection) - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN) - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN) - [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) - [UltraChat (en)](https://github.com/thunlp/UltraChat) - [LIMA (en)](https://huggingface.co/datasets/GAIR/lima) - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) - [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca) - [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa) - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn) - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar) - [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data) - [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen) - [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k) - [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) - [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) - [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct) - [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) - [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
Preference datasets - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf) - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1) - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
Please refer to [data/README.md](data/README.md) for details. Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands. ```bash pip install --upgrade huggingface_hub huggingface-cli login ``` ## Requirement - Python 3.8+ and PyTorch 1.13.1+ - 🤗Transformers, Datasets, Accelerate, PEFT and TRL - sentencepiece, protobuf and tiktoken - jieba, rouge-chinese and nltk (used at evaluation and predict) - gradio and matplotlib (used in web UI) - uvicorn, fastapi and sse-starlette (used in API) ### Hardware Requirement | Method | Bits | 7B | 13B | 30B | 65B | 8x7B | | ------ | ---- | ----- | ----- | ----- | ------ | ------ | | Full | 16 | 160GB | 320GB | 600GB | 1200GB | 900GB | | Freeze | 16 | 20GB | 40GB | 120GB | 240GB | 200GB | | LoRA | 16 | 16GB | 32GB | 80GB | 160GB | 120GB | | QLoRA | 8 | 10GB | 16GB | 40GB | 80GB | 80GB | | QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 32GB | ## Getting Started ### Data Preparation (optional) Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset. > [!NOTE] > Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`. ### Dependence Installation (optional) ```bash git clone https://github.com/hiyouga/LLaMA-Factory.git conda create -n llama_factory python=3.10 conda activate llama_factory cd LLaMA-Factory pip install -r requirements.txt ``` If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1. ```bash pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl ``` ### Use ModelScope Hub (optional) If you have trouble with downloading models and datasets from Hugging Face, you can use LLaMA-Factory together with ModelScope in the following manner. ```bash export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows ``` Then you can train the corresponding model by specifying a model ID of the ModelScope Hub. (find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models)) ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --model_name_or_path modelscope/Llama-2-7b-ms \ ... # arguments (same as above) ``` LLaMA Board also supports using the models and datasets on the ModelScope Hub. ```bash CUDA_VISIBLE_DEVICES=0 USE_MODELSCOPE_HUB=1 python src/train_web.py ``` ### Train on a single GPU > [!IMPORTANT] > If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training). #### Pre-Training ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --stage pt \ --do_train \ --model_name_or_path path_to_llama_model \ --dataset wiki_demo \ --finetuning_type lora \ --lora_target q_proj,v_proj \ --output_dir path_to_pt_checkpoint \ --overwrite_cache \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 5e-5 \ --num_train_epochs 3.0 \ --plot_loss \ --fp16 ``` #### Supervised Fine-Tuning ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --stage sft \ --do_train \ --model_name_or_path path_to_llama_model \ --dataset alpaca_gpt4_en \ --template default \ --finetuning_type lora \ --lora_target q_proj,v_proj \ --output_dir path_to_sft_checkpoint \ --overwrite_cache \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 5e-5 \ --num_train_epochs 3.0 \ --plot_loss \ --fp16 ``` #### Reward Modeling ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --stage rm \ --do_train \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_sft_checkpoint \ --create_new_adapter \ --dataset comparison_gpt4_en \ --template default \ --finetuning_type lora \ --lora_target q_proj,v_proj \ --output_dir path_to_rm_checkpoint \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 1e-6 \ --num_train_epochs 1.0 \ --plot_loss \ --fp16 ``` #### PPO Training ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --stage ppo \ --do_train \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_sft_checkpoint \ --create_new_adapter \ --dataset alpaca_gpt4_en \ --template default \ --finetuning_type lora \ --lora_target q_proj,v_proj \ --reward_model path_to_rm_checkpoint \ --output_dir path_to_ppo_checkpoint \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --top_k 0 \ --top_p 0.9 \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 1e-5 \ --num_train_epochs 1.0 \ --plot_loss \ --fp16 ``` > [!WARNING] > Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 PPO training. #### DPO Training ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --stage dpo \ --do_train \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_sft_checkpoint \ --create_new_adapter \ --dataset comparison_gpt4_en \ --template default \ --finetuning_type lora \ --lora_target q_proj,v_proj \ --output_dir path_to_dpo_checkpoint \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 1e-5 \ --num_train_epochs 1.0 \ --plot_loss \ --fp16 ``` ### Distributed Training #### Use Huggingface Accelerate ```bash accelerate config # configure the environment accelerate launch src/train_bash.py # arguments (same as above) ```
Example config for LoRA training ```yaml compute_environment: LOCAL_MACHINE distributed_type: MULTI_GPU downcast_bf16: 'no' gpu_ids: all machine_rank: 0 main_training_function: main mixed_precision: fp16 num_machines: 1 num_processes: 4 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false ```
#### Use DeepSpeed ```bash deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \ --deepspeed ds_config.json \ ... # arguments (same as above) ```
Example config for full-parameter training with DeepSpeed ZeRO-2 ```json { "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "zero_allow_untested_optimizer": true, "fp16": { "enabled": "auto", "loss_scale": 0, "initial_scale_power": 16, "loss_scale_window": 1000, "hysteresis": 2, "min_loss_scale": 1 }, "zero_optimization": { "stage": 2, "allgather_partitions": true, "allgather_bucket_size": 5e8, "reduce_scatter": true, "reduce_bucket_size": 5e8, "overlap_comm": false, "contiguous_gradients": true } } ```
### Merge LoRA weights and export model ```bash python src/export_model.py \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_checkpoint \ --template default \ --finetuning_type lora \ --export_dir path_to_export \ --export_size 2 \ --export_legacy_format False ``` > [!WARNING] > Merging LoRA weights into a quantized model is not supported. > [!TIP] > Use `--export_quantization_bit 4` and `--export_quantization_dataset data/c4_demo.json` to quantize the model after merging the LoRA weights. ### API Demo ```bash python src/api_demo.py \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_checkpoint \ --template default \ --finetuning_type lora ``` > [!TIP] > Visit `http://localhost:8000/docs` for API documentation. ### CLI Demo ```bash python src/cli_demo.py \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_checkpoint \ --template default \ --finetuning_type lora ``` ### Web Demo ```bash python src/web_demo.py \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_checkpoint \ --template default \ --finetuning_type lora ``` ### Evaluation ```bash CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_checkpoint \ --template vanilla \ --finetuning_type lora --task mmlu \ --split test \ --lang en \ --n_shot 5 \ --batch_size 4 ``` ### Predict ```bash CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \ --stage sft \ --do_predict \ --model_name_or_path path_to_llama_model \ --adapter_name_or_path path_to_checkpoint \ --dataset alpaca_gpt4_en \ --template default \ --finetuning_type lora \ --output_dir path_to_predict_result \ --per_device_eval_batch_size 8 \ --max_samples 100 \ --predict_with_generate \ --fp16 ``` > [!WARNING] > Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 predict. > [!TIP] > We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict. ## Projects using LLaMA Factory - **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B. - **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge. - **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B. - **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B. - **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: MBTI Personality large language model Project, capable of giving any LLM 16 different personality types based on dataset + training method. > [!TIP] > If you have a project that should be incorporated, please contact via email or create a pull request. ## License This repository is licensed under the [Apache-2.0 License](LICENSE). Please follow the model licenses to use the corresponding model weights: [Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan) ## Citation If this work is helpful, please kindly cite as: ```bibtex @Misc{llama-factory, title = {LLaMA Factory}, author = {hiyouga}, howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}}, year = {2023} } ``` ## Acknowledgement This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works. ## Star History ![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)