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README.md
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README.md
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@ -263,34 +263,55 @@ use_cpu: false
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage pt \
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--stage sft \
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--model_name_or_path path_to_your_model \
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--do_eval \
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--dataset alpaca_gpt4_en \
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--finetuning_type lora \
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--checkpoint_dir path_to_checkpoint \
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--output_dir path_to_eval_result \
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--per_device_eval_batch_size 8 \
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--max_samples 50 \
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--max_samples 100 \
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--predict_with_generate
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```
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We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit evaluation.
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### Predict
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage sft \
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--model_name_or_path path_to_your_model \
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--do_predict \
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--dataset alpaca_gpt4_en \
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--finetuning_type lora \
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--checkpoint_dir path_to_checkpoint \
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--output_dir path_to_predict_result \
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--per_device_eval_batch_size 8 \
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--max_samples 100 \
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--predict_with_generate
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```
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If you want to predict the samples with empty responses, please kindly fill the `response` column with **dummy tokens** to ensure the sample will not be discarded throughout the preprocessing phase.
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### API Demo
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```bash
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python src/api_demo.py \
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--model_name_or_path path_to_your_model \
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--finetuning_type lora \
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--checkpoint_dir path_to_checkpoint
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```
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See `http://localhost:8000/docs` for API documentation.
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Visit `http://localhost:8000/docs` for API documentation.
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### CLI Demo
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```bash
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python src/cli_demo.py \
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--model_name_or_path path_to_your_model \
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--finetuning_type lora \
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--checkpoint_dir path_to_checkpoint
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```
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@ -299,6 +320,7 @@ python src/cli_demo.py \
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```bash
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python src/web_demo.py \
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--model_name_or_path path_to_your_model \
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--finetuning_type lora \
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--checkpoint_dir path_to_checkpoint
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```
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@ -307,6 +329,7 @@ python src/web_demo.py \
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```bash
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python src/export_model.py \
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--model_name_or_path path_to_your_model \
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--finetuning_type lora \
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--checkpoint_dir path_to_checkpoint \
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--output_dir path_to_export
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```
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