#LLM Tuning
[23/08/03] Now we support training the Qwen-7B model in this repo. Try --model_name_or_path Qwen/Qwen-7B-Chat
and --lora_target c_attn
arguments to train the Qwen-7B model. Remember to use --template chatml
argument when you are using the Qwen-7B-Chat model.
[23/07/31] Now we support dataset streaming. Try --streaming
and --max_steps 100
arguments to stream your dataset.
[23/07/29] We release two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos (LLaMA-2 / Baichuan) for details.
[23/07/19] Now we support training the LLaMA-2 models in this repo. Try --model_name_or_path meta-llama/Llama-2-7b-hf
argument to use the LLaMA-2 model. Remember to use --template llama2
argument when you are using the LLaMA-2-chat model.
[23/07/18] Now we develop 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.
[23/07/11] Now we support training the Baichuan-13B model in this repo. Try --model_name_or_path baichuan-inc/Baichuan-13B-Base
and --lora_target W_pack
arguments to train the Baichuan-13B model. Remember to use --template baichuan
argument when you are using the Baichuan-13B-Chat model.
[23/07/09] Now we release FastEdit⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow FastEdit if you are interested.
[23/07/07] Now we support training the InternLM-7B model in this repo. Try --model_name_or_path internlm/internlm-7b
argument to use the InternLM model. Remember to use --template intern
argument when you are using the InternLM-chat model.
[23/07/05] Now we support training the Falcon-7B/40B models in this repo. Try --model_name_or_path tiiuae/falcon-7b
and --lora_target query_key_value
arguments to use the Falcon model.
[23/06/29] We provide a reproducible example of training a chat model using instruction-following datasets, see this Hugging Face Repo for details.
[23/06/22] Now we align the demo API with the OpenAI's format where you can insert the fine-tuned model in arbitrary ChatGPT-based applications.
[23/06/15] Now we support training the Baichuan-7B model in this repo. Try --model_name_or_path baichuan-inc/Baichuan-7B
and --lora_target W_pack
arguments to use the Baichuan-7B model.
[23/06/03] Now we support quantized training and inference (aka QLoRA). Try --quantization_bit 4/8
argument to work with quantized models.
[23/05/31] Now we support training the BLOOM & BLOOMZ models in this repo. Try --model_name_or_path bigscience/bloomz-7b1-mt
and --lora_target query_key_value
arguments to use the BLOOMZ model.
model | model size | model_name_or_path | lora_target | template |
---|---|---|---|---|
LLaMA | 7B/13B/33B/65B | - | q_proj,v_proj | default |
LLaMA-2 | 7B/13B/70B | meta-llama/Llama-2-7b-hf | q_proj,v_proj | llama2 |
BLOOM | 560M/1.1B/1.7B/3B/7.1B/176B | bigscience/bloom-7b1 | query_key_value | default |
BLOOMZ | 560M/1.1B/1.7B/3B/7.1B/176B | bigscience/bloomz-7b1-mt | query_key_value | default |
Falcon | 7B/40B | tiiuae/falcon-7b | query_key_value | default |
Baichuan | 7B/13B | baichuan-inc/Baichuan-13B-Chat | W_pack | baichuan |
InternLM | 7B | internlm/internlm-7b | q_proj,v_proj | intern |
Qwen | 7B | Qwen/Qwen-7B-Chat | c_attn | chatml |
- For pre-training:
- For supervised fine-tuning:
- Stanford Alpaca (en)
- Stanford Alpaca (zh)
- GPT-4 Generated Data (en&zh)
- Open Assistant (multilingual)
- Self-cognition (zh)
- ShareGPT (zh)
- RefGPT (zh)
- 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)
- Firefly 1.1M (zh)
- LIMA (en)
- CodeAlpaca 20k (en)
- Alpaca CoT (multilingual)
- Web QA (zh)
- UltraChat (en)
- WebNovel (zh)
- For reward modelling:
Please refer to 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.
pip install --upgrade huggingface_hub
huggingface-cli login
- Python 3.8+ and PyTorch 1.13.1+
- 🤗Transformers, Datasets, Accelerate, PEFT and TRL
- sentencepiece and tiktoken
- jieba, rouge-chinese and nltk (used at evaluation)
- gradio and matplotlib (used in web_demo.py)
- uvicorn, fastapi and sse-starlette (used in api_demo.py)
And powerful GPUs!
Please refer to data/example_dataset
for checking the details about the format of dataset files. You can either use a single .json
file or a dataset loading 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
.
git lfs install
git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
conda create -n llama_etuning python=3.10
conda activate llama_etuning
cd LLaMA-Efficient-Tuning
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.
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
CUDA_VISIBLE_DEVICES=0 python src/train_web.py
Currently the web UI only supports training on a single GPU.
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage pt \
--model_name_or_path path_to_your_model \
--do_train \
--dataset wiki_demo \
--template default \
--finetuning_type lora \
--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
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
--model_name_or_path path_to_your_model \
--do_train \
--dataset alpaca_gpt4_en \
--template default \
--finetuning_type lora \
--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
Remember to specify --lora_target W_pack
if you are using Baichuan models.
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage rm \
--model_name_or_path path_to_your_model \
--do_train \
--dataset comparison_gpt4_en \
--template default \
--finetuning_type lora \
--resume_lora_training False \
--checkpoint_dir path_to_sft_checkpoint \
--output_dir path_to_rm_checkpoint \
--per_device_train_batch_size 4 \
--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
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage ppo \
--model_name_or_path path_to_your_model \
--do_train \
--dataset alpaca_gpt4_en \
--template default \
--finetuning_type lora \
--resume_lora_training False \
--checkpoint_dir path_to_sft_checkpoint \
--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 \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--plot_loss
accelerate config # configure the environment
accelerate launch src/train_bash.py # arguments (same as above)
Example configuration for full-tuning with DeepSpeed ZeRO-2
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 4
gradient_clipping: 0.5
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: false
zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
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
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
--model_name_or_path path_to_your_model \
--do_eval \
--dataset alpaca_gpt4_en \
--template default \
--finetuning_type lora \
--checkpoint_dir path_to_checkpoint \
--output_dir path_to_eval_result \
--per_device_eval_batch_size 8 \
--max_samples 100 \
--predict_with_generate
We recommend using --per_device_eval_batch_size=1
and --max_target_length 128
at 4/8-bit evaluation.
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
--model_name_or_path path_to_your_model \
--do_predict \
--dataset alpaca_gpt4_en \
--template default \
--finetuning_type lora \
--checkpoint_dir path_to_checkpoint \
--output_dir path_to_predict_result \
--per_device_eval_batch_size 8 \
--max_samples 100 \
--predict_with_generate
python src/api_demo.py \
--model_name_or_path path_to_your_model \
--template default \
--finetuning_type lora \
--checkpoint_dir path_to_checkpoint
Visit http://localhost:8000/docs
for API documentation.
python src/cli_demo.py \
--model_name_or_path path_to_your_model \
--template default \
--finetuning_type lora \
--checkpoint_dir path_to_checkpoint
python src/web_demo.py \
--model_name_or_path path_to_your_model \
--template default \
--finetuning_type lora \
--checkpoint_dir path_to_checkpoint
python src/export_model.py \
--model_name_or_path path_to_your_model \
--template default \
--finetuning_type lora \
--checkpoint_dir path_to_checkpoint \
--output_dir path_to_export
- Supporting flash attention (torch / xformers / flashattn).
- Implementing multi-query attention for faster inference.
- Supporting full-parameter RLHF training.
This repository is licensed under the Apache-2.0 License.
Please follow the model licenses to use the corresponding model weights:
If this work is helpful, please kindly cite as:
@Misc{llama-efficient-tuning,
title = {LLaMA Efficient Tuning},
author = {hiyouga},
howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
year = {2023}
}
This repo is a sibling of ChatGLM-Efficient-Tuning. They share a similar code structure of efficient tuning on large language models.