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parambharat
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85bfd70
fix: change citations to footnotes
Browse files- rag/rag.py +6 -6
rag/rag.py
CHANGED
@@ -45,8 +45,8 @@ Guidelines for your answer:
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5. Use appropriate technical language and terminology as used in the snippets.
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6. Cite the relevant sentences from the snippets and their page numbers to support your answer.
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7. Answer in MFAQ format (Minimal Facts Answerable Question), providing the most concise and accurate response possible.
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8. Use Markdown to format your response and include
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9. Your answer must only have two headings: 'Answer' and '
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Here's an example of a question and an answer. You must use this as a template to format your response:
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@@ -65,11 +65,11 @@ The main mix of the training data for the Llama 3 405 billion parameter model is
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Regarding the amount of data used to train the model, the snippets do not provide a specific total volume of data in terms of tokens or bytes. However, they do mention that the model was pre-trained on a large dataset containing knowledge until the end of 2023[^2^]. Additionally, the training process involved pre-training on 2.87 trillion tokens before further adjustments[^3^].
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###
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</example>
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5. Use appropriate technical language and terminology as used in the snippets.
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6. Cite the relevant sentences from the snippets and their page numbers to support your answer.
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7. Answer in MFAQ format (Minimal Facts Answerable Question), providing the most concise and accurate response possible.
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8. Use Markdown to format your response and include citation footnotes to indicate the snippets and the page number used to derive your answer.
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9. Your answer must only have two headings: 'Answer' and 'Footnotes'.
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Here's an example of a question and an answer. You must use this as a template to format your response:
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Regarding the amount of data used to train the model, the snippets do not provide a specific total volume of data in terms of tokens or bytes. However, they do mention that the model was pre-trained on a large dataset containing knowledge until the end of 2023[^2^]. Additionally, the training process involved pre-training on 2.87 trillion tokens before further adjustments[^3^].
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### Footnotes
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[^1^]: "Scaling Laws for Data Mix," page 6.
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[^2^]: "Pre-Training Data," page 4.
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[^3^]: "Initial Pre-Training," page 14.
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</example>
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