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@@ -388,6 +388,12 @@ Remember: For this project **Pipeline** is going to be seprated in two different
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- **`trainer.train()`**: Initiates the training process on the dataset, where the model is specifically trained on generating accurate responses while the instructions are handled separately. This helps improve the quality of responses in tasks where the model needs to generate coherent replies based on the input conversation context.
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I performed instruction fine-tuning, but with a primary focus on **response generation** rather than directly interpreting the instruction itself. While the model is still fine-tuned to follow instructions, the training process is designed to enhance the model’s ability to generate **contextually relevant, coherent, and accurate responses** based on the given instruction.
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The reason for focusing more on responses is to make the model **more dynamic and conversational**, ensuring that it generates high-quality outputs even when the instructions are varied or complex. Instead of explicitly focusing on how well the model understands the instructions, we prioritize improving its response generation, making it better at delivering useful, human-like answers.
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This approach allows the model to **implicitly understand instructions** while being optimized to generate responses that align with the user’s expectations. In essence, while the fine-tuning is rooted in instructions, the goal is to improve the model's performance in **conversational tasks** by driving its training with **response quality** in mind.
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