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  ---
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- base_model: unsloth/Llama-3.2-1B
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  language:
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  - en
 
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  license: apache-2.0
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  tags:
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  - text-generation-inference
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  - transformers
 
 
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  - unsloth
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  - llama
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  - gguf
 
 
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  ---
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- # Uploaded model
 
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  - **Developed by:** student-abdullah
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  - **License:** apache-2.0
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- - **Finetuned from model :** unsloth/Llama-3.2-1B
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-
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- This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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  [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ base_model: meta-llama/Llama-3.2-1B
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  language:
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  - en
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+ - hi
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  license: apache-2.0
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  tags:
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  - text-generation-inference
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  - transformers
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+ - torch
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+ - trl
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  - unsloth
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  - llama
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  - gguf
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+ datasets:
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+ - student-abdullah/BigPharma_Generic_Q-A_Format_Augemented_Hinglish_Dataset
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  ---
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+
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+ # Uploaded model
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  - **Developed by:** student-abdullah
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  - **License:** apache-2.0
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+ - **Finetuned from model:** meta-llama/Llama-3.2-1B
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+ - **Created on:** 30th September, 2024
 
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+ ---
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+ # Acknowledgement
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  [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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+
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+ ---
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+ # Model Description
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+ 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:
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+
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+ - Fine Tuning Template: Llama 3.1 Q&A
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+ - Max Tokens: 512
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+ - LoRA Alpha: 10
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+ - LoRA Rank (r): 128
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+ - Learning rate: 1e-4
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+ - Gradient Accumulation Steps: 2
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+ - Batch Size: 4
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+ - Qunatization: No Quantisation
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+
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+ ---
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+ # Model Quantitative Performace
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+ - Training Quantitative Loss: 0.1167 (at final 5,200th epoch)
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+
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+ ---
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+ # Limitations
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+ - Token Limitations: With a max token limit of 512, the model might not handle very long queries or contexts effectively.
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+ - 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.
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+ - Potential Biases: As with any model fine-tuned on specific data, there may be biases based on the dataset used for training.