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support loading lora from hub

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hiyouga
2023-06-16 00:02:17 +08:00
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README.md
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@@ -9,6 +9,8 @@
## Changelog
[23/06/15] Now we support training the baichuan-7B model in this repo. Try `--model_name_or_path baichuan-inc/baichuan-7B` argument to use the baichuan-7B model.
[23/06/03] Now we support quantized training and inference (aka [QLoRA](https://github.com/artidoro/qlora)). Try `--quantization_bit 4/8` argument to work with quantized model. (experimental feature)
[23/05/31] Now we support training the BLOOM & BLOOMZ models in this repo. Try `--model_name_or_path bigscience/bloomz-7b1-mt` argument to use the BLOOMZ model.
@@ -111,7 +113,7 @@ python -m transformers.models.llama.convert_llama_weights_to_hf \
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_pt.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--do_train \
--dataset wiki_demo \
--finetuning_type lora \
@@ -132,11 +134,10 @@ CUDA_VISIBLE_DEVICES=0 python src/train_pt.py \
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--do_train \
--dataset alpaca_gpt4_en \
--finetuning_type lora \
--checkpoint_dir path_to_pt_checkpoint \
--output_dir path_to_sft_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
@@ -146,7 +147,6 @@ CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--resume_lora_training False \
--plot_loss \
--fp16
```
@@ -155,11 +155,10 @@ CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_rm.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--do_train \
--dataset comparison_gpt4_en \
--finetuning_type lora \
--checkpoint_dir path_to_pt_checkpoint \
--output_dir path_to_rm_checkpoint \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
@@ -176,11 +175,11 @@ CUDA_VISIBLE_DEVICES=0 python src/train_rm.py \
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_ppo.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--do_train \
--dataset alpaca_gpt4_en \
--finetuning_type lora \
--checkpoint_dir path_to_pt_checkpoint,path_to_sft_checkpoint \
--checkpoint_dir path_to_sft_checkpoint \
--reward_model path_to_rm_checkpoint \
--output_dir path_to_ppo_checkpoint \
--per_device_train_batch_size 2 \
@@ -205,7 +204,7 @@ accelerate launch src/train_XX.py # arguments (same as above)
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--do_eval \
--dataset alpaca_gpt4_en \
--checkpoint_dir path_to_checkpoint \
@@ -215,20 +214,20 @@ CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
--predict_with_generate
```
We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` in INT8 evaluation.
We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit evaluation.
### CLI Demo
```bash
python src/cli_demo.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--checkpoint_dir path_to_checkpoint
```
### Web Demo
```bash
python src/web_demo.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--checkpoint_dir path_to_checkpoint
```
@@ -236,7 +235,7 @@ python src/web_demo.py \
```bash
python src/export_model.py \
--model_name_or_path path_to_llama_model \
--model_name_or_path path_to_your_model \
--checkpoint_dir path_to_checkpoint \
--output_dir path_to_export
```
@@ -249,6 +248,8 @@ Please follow the [Model Card](https://github.com/facebookresearch/llama/blob/ma
Please follow the [RAIL License](https://huggingface.co/spaces/bigscience/license) to use the BLOOM & BLOOMZ models.
Please follow the [baichuan-7B License](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) to use the baichuan-7B model.
## Citation
If this work is helpful, please cite as: