mirror of https://github.com/InternLM/InternLM
79 lines
4.0 KiB
Markdown
79 lines
4.0 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 InternLM2.5-Chat 进行推理。
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你还可以进一步了解 InternLM2.5-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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import torch
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from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
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model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
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tokenizer = AutoTokenizer.from_pretrained(model_dir,trust_remote_code=True)
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# 设置`torch_dtype=torch.float16`来将模型精度指定为torch.float16，否则可能会因为您的硬件原因造成显存不足的问题。
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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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# (可选) 如果在低资源设备上，可以通过bitsandbytes加载4-bit或8-bit量化的模型，进一步节省GPU显存.
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# 4-bit 量化的 InternLM3 8B 大约会消耗 8GB 显存.
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# pip install -U bitsandbytes
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# 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True)
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# 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True)
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messages = [
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{"role": "system", "content": "You are an AI assistant whose name is InternLM."},
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{"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
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]
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tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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generated_ids = model.generate(tokenized_chat, max_new_tokens=512)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids)[0]
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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 InternLM2.5-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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import torch
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from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
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model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
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tokenizer = AutoTokenizer.from_pretrained(model_dir,trust_remote_code=True)
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# 设置`torch_dtype=torch.float16`来将模型精度指定为torch.float16，否则可能会因为您的硬件原因造成显存不足的问题。
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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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# (可选) 如果在低资源设备上，可以通过bitsandbytes加载4-bit或8-bit量化的模型，进一步节省GPU显存.
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# 4-bit 量化的 InternLM3 8B 大约会消耗 8GB 显存.
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# pip install -U bitsandbytes
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# 8-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_8bit=True)
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# 4-bit: model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, load_in_4bit=True)
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messages = [
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{"role": "system", "content": "You are an AI assistant whose name is InternLM."},
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{"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
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]
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tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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generated_ids = model.generate(tokenized_chat, max_new_tokens=512)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(tokenized_chat, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids)[0]
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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 InternLM3-8B-Instruct 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>=4.48
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streamlit run ./web_demo.py
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```
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