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--- |
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license: apache-2.0 |
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--- |
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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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--- |
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This is an SFT model trained using https://github.com/hiyouga/LLaMA-Efficient-Tuning. |
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Thanks to the original author for their hard work. |
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All work is based on https://huggingface.co./baichuan-inc/baichuan-7B. |
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You can find the matching data set on the github of the fine-tuning framework. |
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We carried out 4 epoch of distributed training on the 8-card H100 machine, which took a short time. However, there is not much change in the loss. |
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In the future, we will update the data set to see how it will perform in a vertical field. |
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Of course, this is the inference code of the original author. You can use it directly. |
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Usage: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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from peft import PeftModel |
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tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/baichuan-7B", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("baichuan-inc/baichuan-7B", device_map="auto", trust_remote_code=True) |
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model = PeftModel.from_pretrained(model, "/data/baichuan-7b-sft") #change to your own path. |
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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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inputs = tokenizer(["<human>:{}\n<bot>:".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 \ |
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--model_name_or_path baichuan-inc/baichuan-7B \ |
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--checkpoint_dir hiyouga/baichuan-7b-sft \ |
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``` |
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