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jiangchengchengNLP
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README.md
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license: apache-2.0
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---
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license: apache-2.0
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---
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这是基于Auto-GPTQ框架的量化模型,模型选取为huatuoGPT2-7B,这是一个微调模型,基底模型为百川-7B。
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参数说明: 原模型大小:16GB,量化后模型大小:5GB
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推理准确度尚未测试,请谨慎使用
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量化过程中,校准数据采用微调训练集Medical Fine-tuning Instruction (GPT-4)。
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使用示例(目前仅支持awq,transformers的集成尚在研究):
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开始之前务必指定GPU
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```
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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```
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确保你安装了auto-awq
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```
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!git clone https://github.com/casper-hansen/AutoAWQ
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cd AutoAWQ
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!pip install -e .
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```
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```
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from awq import AutoAWQForCausalLM
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from awq.utils.utils import get_best_device
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from transformers import AutoTokenizer, TextStreamer
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quant_path = "jiangchengchengNLP/huatuo_AutoAWQ_7B4bits"
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# Load model
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model = AutoAWQForCausalLM.from_quantized(quant_path,device="cuda",fuse_layers=False)
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tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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prompt = "You're standing on the surface of the Earth. "\
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"You walk one mile south, one mile west and one mile north. "\
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"You end up exactly where you started. Where are you?"
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chat = [
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{"role": "user", "content": prompt},
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]
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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tokenizer.chat_template="""
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{%- for message in messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
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{%- endif -%}
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{%- if message['role'] == 'user' -%}
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{{ '<问>:' + message['content'] + '\n' }}
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{%- elif message['role'] == 'assistant' -%}
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{{ '<答>:' + message['content'] + '\n' }}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{- '<答>:' -}}
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{% endif %}
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"""
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tokens = tokenizer.apply_chat_template(
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chat,
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return_tensors="pt"
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)
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tokens = tokens.to("cuda:0")
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generation_output = model.generate(
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tokens,
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streamer=streamer,
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max_new_tokens=1000,
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eos_token_id=terminators,
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max_length=1000,
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)
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```
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