mirror of https://github.com/InternLM/InternLM
52 lines
2.2 KiB
Markdown
52 lines
2.2 KiB
Markdown
# 对话
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[English](./README.md) | 简体中文
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本文介绍采用 [Transformers](#import-from-transformers)、[ModelScope](#import-from-modelscope)、[Web demos](#dialogue)
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对 InternLM2-Chat 进行推理。
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你还可以进一步了解 InternLM2-Chat 采用的[对话格式](./chat_format_zh-CN.md)，以及如何[用 LMDeploy 进行推理或部署服务](./lmdeploy_zh-CN.md)，或者尝试用 [OpenAOE](./openaoe.md) 与多个模型对话。
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## 通过 Transformers 加载
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通过以下的代码从 Transformers 加载 InternLM 模型 （可修改模型名称替换不同的模型）
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```python
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>>> from transformers import AutoTokenizer, AutoModelForCausalLM
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>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True)
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>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True).cuda()
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>>> model = model.eval()
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>>> response, history = model.chat(tokenizer, "你好", history=[])
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>>> print(response)
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你好！有什么我可以帮助你的吗？
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>>> response, history = model.chat(tokenizer, "请提供三个管理时间的建议。", history=history)
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>>> print(response)
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```
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### 通过 ModelScope 加载
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通过以下的代码从 ModelScope 加载 InternLM2-Chat 模型 （可修改模型名称替换不同的模型）
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```python
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from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
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import torch
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model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm2-chat-7b', revision='v1.0.0')
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tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
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model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
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model = model.eval()
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response, history = model.chat(tokenizer, "hello", history=[])
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print(response)
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response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history)
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print(response)
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```
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## 通过前端网页对话
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可以通过以下代码启动一个前端的界面来与 InternLM2 Chat 7B 模型进行交互
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```bash
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pip install streamlit
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pip install transformers>=4.34
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streamlit run ./web_demo.py
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```
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