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@@ -24,6 +24,8 @@ This model is designed for RAG tasks, where it can answer questions based on pro
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  Use the code below to get started with the model:
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  ```python
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  from transformers import AutoTokenizer, pipeline
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@@ -86,16 +88,8 @@ The citation is marked with <co:1></co> tags, indicating that this information c
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  ## Code Explanation
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  The code is split into two main parts:
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- ## Chat Template Preparation:
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- We create a chat list with a system message and a user query.
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- The apply_chat_template method is used to format this chat into a prompt suitable for the model.
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- ## Pipeline Setup and Generation:
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- We set up a text-generation pipeline with our model and tokenizer.
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- The prepared prompt is passed to the pipeline to generate a response.
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  Use the code below to get started with the model:
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+ NOTE: Try to use the same system prompt and document formatting as the example provided below, this is the same format that was used to finetune the model.
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  ```python
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  from transformers import AutoTokenizer, pipeline
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  ## Code Explanation
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  The code is split into two main parts:
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+ 1. Chat Template Preparation: We create a chat list with a system message and a user query. The apply_chat_template method is used to format this chat into a prompt suitable for the model.
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+ 2. Pipeline Setup and Generation: We set up a text-generation pipeline with our model and tokenizer. The prepared prompt is passed to the pipeline to generate a response.
 
 
 
 
 
 
 
 
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