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@@ -6,6 +6,14 @@ Cassandre-RAG is a fine-tuned llama-3.1-8b model, built for RAG on French admini
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  The model was trained on a H100, using these parameters:
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  ### Training Hyperparameters
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  - Max Steps: 3000
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  - Learning Rate: 3e-4
 
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  The model was trained on a H100, using these parameters:
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+ ## Training Data
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+ The model was fine-tuned on a specialized corpus consisting of:
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+ 1. Synthetic queries: Generated from chunks of text extracted from French administrative documents.
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+ 2. Retrieved documents: For each synthetic query, relevant documents were retrieved using the BM25 ranking algorithm.
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+ 3. Generated answers: Responses to the synthetic queries were created based on the retrieved documents.
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  ### Training Hyperparameters
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  - Max Steps: 3000
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  - Learning Rate: 3e-4