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# Model Description
This model is fine-tuned from the meta-llama/Llama-3.2-1B base model to enhance its capabilities in generating relevant and accurate responses related to generic medications under the PMBJP scheme. The fine-tuning process included the following hyperparameters:
- Fine Tuning Template: Llama Q&A
- Max Tokens: 512
- LoRA Alpha: 6
- LoRA Rank (r): 128
- Learning rate: 5e-5
- Gradient Accumulation Steps: 2
- Batch Size: 4
- Quantization: 16 bits
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# Model Quantitative Performace
- Training Quantitative Loss: 0.1407 (at final 5th epoch 5150th Step)
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# Limitations
- Token Limitations: With a max token limit of 512, the model might not handle very long queries or contexts effectively.
- Training Data Limitations: The model’s performance is contingent on the quality and coverage of the fine-tuning dataset, which may affect its generalizability to different contexts or medications not covered in the dataset.
- Potential Biases: As with any model fine-tuned on specific data, there may be biases based on the dataset used for training.
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# Model Performace Evaluation:
- Evaluation on 1000 Questions based on dataset (to evaluate the finetuned knowledge base)
- At temperature 0.3
- Correct Responses: 76%
- Incorrect Responses: 24%