Llama3.2_Med-Dataset_FineTuning_01-10_gguf

Uploaded model

• Developed by: student-abdullah
• License: apache-2.0
• Finetuned from model: meta-llama/Llama-3.2-1B
• Created on: 1st October, 2024

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Acknowledgement

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

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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: 10
• LoRA Rank (r): 128
• Learning rate: 1e-5
• Gradient Accumulation Steps: 2
• Batch Size: 4
• Quantization: None

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Model Quantitative Performace

• Training Quantitative Loss: 0.2073 (at final 5200th 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)
• Correct Responses: 79.03%
• Incorrect Responses: 20.97%

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