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--- |
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license: apache-2.0 |
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datasets: |
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- tatsu-lab/alpaca |
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- sahil2801/CodeAlpaca-20k |
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language: |
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- zh |
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- en |
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library_name: transformers |
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tags: |
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- baichuan |
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- lora |
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pipeline_tag: text-generation |
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inference: false |
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--- |
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A bilingual instruction-tuned LoRA model of https://huggingface.co/baichuan-inc/baichuan-7B |
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- Instruction-following datasets used: alpaca, alpaca-zh, codealpaca |
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- Training framework: https://github.com/hiyouga/LLaMA-Efficient-Tuning |
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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 this model. |
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Usage: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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tokenizer = AutoTokenizer.from_pretrained("hiyouga/baichuan-7b-sft", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("hiyouga/baichuan-7b-sft", trust_remote_code=True).cuda() |
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) |
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query = "晚上睡不着怎么办" |
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template = ( |
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"A chat between a curious user and an artificial intelligence assistant. " |
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"The assistant gives helpful, detailed, and polite answers to the user's questions.\n" |
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"Human: {}\nAssistant: " |
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) |
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inputs = tokenizer([template.format(query)], return_tensors="pt") |
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inputs = inputs.to("cuda") |
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generate_ids = model.generate(**inputs, max_new_tokens=256, streamer=streamer) |
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``` |
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You could also alternatively launch a CLI demo by using the script in https://github.com/hiyouga/LLaMA-Efficient-Tuning |
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```bash |
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python src/cli_demo.py --template default --model_name_or_path hiyouga/baichuan-7b-sft |
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``` |
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--- |
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You could reproduce our results with the following scripts using [LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning): |
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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 baichuan-inc/baichuan-7B \ |
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--do_train \ |
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--dataset alpaca_gpt4_en,alpaca_gpt4_zh,codealpaca \ |
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--template default \ |
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--finetuning_type lora \ |
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--lora_rank 16 \ |
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--lora_target W_pack,o_proj,gate_proj,down_proj,up_proj \ |
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--output_dir baichuan_lora \ |
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--overwrite_cache \ |
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--per_device_train_batch_size 8 \ |
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--per_device_eval_batch_size 8 \ |
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--gradient_accumulation_steps 8 \ |
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--preprocessing_num_workers 16 \ |
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--lr_scheduler_type cosine \ |
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--logging_steps 10 \ |
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--save_steps 100 \ |
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--eval_steps 100 \ |
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--learning_rate 5e-5 \ |
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--max_grad_norm 0.5 \ |
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--num_train_epochs 2.0 \ |
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--val_size 0.01 \ |
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--evaluation_strategy steps \ |
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--load_best_model_at_end \ |
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--plot_loss \ |
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--fp16 |
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``` |
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Loss curve on training set: |
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Loss curve on evaluation set: |
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