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Update README.md
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README.md
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@@ -75,9 +75,9 @@ The model is evaluated based on several metrics, including loss, reward, penalty
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- BLEU Score: 9
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- Average perplexity: 49
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- Loss: 1.7
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Although these metrics seem mediocre, it's actually better because that way the model is able to make open-ended responses, but is still coherent to the user's input.
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## Limitations and Bias
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This model is not suitable for all use cases due to its limited training time on a weak computer. As a result, it may produce irrelevant or nonsensical responses. Additionally, it has not been fine-tuned to remember the chat history, is unable to provide follow-up responses, and it does not know the answer to many questions (it was only fine-tuned to respond in a conversational way). For optimal performance, we recommend using a GPU with at least 4GB of VRAM and downloading the model manually instead of using the Transformers library. Here's how you should deploy the model:
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- BLEU Score: 9
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- Average perplexity: 49
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- Loss: 1.7
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Although these metrics seem mediocre, it's actually better because that way the model is able to make open-ended responses, but is still coherent to the user's input.
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## Limitations and Bias
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This model is not suitable for all use cases due to its limited training time on a weak computer. As a result, it may produce irrelevant or nonsensical responses. Additionally, it has not been fine-tuned to remember the chat history, is unable to provide follow-up responses, and it does not know the answer to many questions (it was only fine-tuned to respond in a conversational way). For optimal performance, we recommend using a GPU with at least 4GB of VRAM and downloading the model manually instead of using the Transformers library. Here's how you should deploy the model:
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