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@@ -34,7 +34,7 @@ In our paper, we develop three domain-specific models from LLaMA-1-7B, which are
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  ## Domain-Specific LLaMA-2-Chat
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  Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
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- For example, to chat with the finance model:
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  ## Domain-Specific LLaMA-2-Chat
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  Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
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+ For example, to chat with the biomedicine model:
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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