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Update README_CN.md

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revise modelscope quickstart
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Keming (Luke) Lu
2023-09-12 11:24:30 +08:00
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@@ -172,28 +172,19 @@ print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
魔搭（ModelScope）是开源的模型即服务共享平台，为泛AI开发者提供灵活、易用、低成本的一站式模型服务产品。使用ModelScope同样非常简单，代码如下所示： 魔搭（ModelScope）是开源的模型即服务共享平台，为泛AI开发者提供灵活、易用、低成本的一站式模型服务产品。使用ModelScope同样非常简单，代码如下所示：
```python ```python
import os from modelscope import AutoModelForCausalLM, AutoTokenizer
from modelscope.pipelines import pipeline from modelscope import GenerationConfig
from modelscope.utils.constant import Tasks
from modelscope import snapshot_download
model_id = 'QWen/qwen-7b-chat' tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.5',trust_remote_code=True)
revision = 'v1.0.0' model = AutoModelForCausalLM.from_pretrained("qwen/Qwen-7B-Chat", revision = 'v1.0.5',device_map="auto", trust_remote_code=True,fp16 = True).eval()
model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat",revision = 'v1.0.5', trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
model_dir = snapshot_download(model_id, revision) response, history = model.chat(tokenizer, "你好", history=None)
print(response)
pipe = pipeline( response, history = model.chat(tokenizer, "浙江的省会在哪里？", history=history)
task=Tasks.chat, model=model_dir, device_map='auto') print(response)
history = None response, history = model.chat(tokenizer, "它有什么好玩的景点", history=history)
print(response)
text = '浙江的省会在哪里？'
results = pipe(text, history=history)
response, history = results['response'], results['history']
print(f'Response: {response}')
text = '它有什么好玩的地方呢？'
results = pipe(text, history=history)
response, history = results['response'], results['history']
print(f'Response: {response}')
``` ```
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