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README.md
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messages.append({"role": "user", "content": "After spending a day with a group of people, how do you feel when you return home?"})
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response = model.chat(tokenizer, messages)
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print(response)
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```python
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* Using the HuggingFace Transformers library (multi-turn dialogue):
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```bash
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response = model.chat(tokenizer, messages)
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print("Assistant:", response)
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messages.append({"role": "assistant", "content": str(response)})
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```python
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* Use LLaMA-Factory (multi-round conversation)
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python ./src/cli_demo.py \
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--model_name_or_path /path_to_your_local_model \
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--template llama2
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```python
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For more information, please refer to our [GitHub repo](https://github.com/PKU-YuanGroup/Machine-Mindset/).
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<br>
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messages.append({"role": "user", "content": "After spending a day with a group of people, how do you feel when you return home?"})
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response = model.chat(tokenizer, messages)
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print(response)
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```
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* Using the HuggingFace Transformers library (multi-turn dialogue):
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```bash
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response = model.chat(tokenizer, messages)
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print("Assistant:", response)
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messages.append({"role": "assistant", "content": str(response)})
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```
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* Use LLaMA-Factory (multi-round conversation)
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python ./src/cli_demo.py \
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--model_name_or_path /path_to_your_local_model \
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--template llama2
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```
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For more information, please refer to our [GitHub repo](https://github.com/PKU-YuanGroup/Machine-Mindset/).
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<br>
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