BeastGokul/Bio-Mistral-7B-finetuned

Model Card for BioMistral-7B-Finetuned

Model Summary

BioMistral-7B-Finetuned is a biomedical language model adapted from the BioMistral-7B model. This fine-tuned model is tailored for biomedical question-answering tasks and optimized through LoRA (Low-Rank Adaptation) on a 4-bit quantized base. It is particularly useful for tasks that require understanding and generating biomedical text in English.

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Model Details

Model Description

This model was fine-tuned for biomedical applications, primarily focusing on enhancing accuracy in question-answering tasks within this domain.

• Base Model: BioMistral-7B
• License: apache-2.0
• Fine-tuned for Task: Biomedical Q&A, text generation
• Quantization: 4-bit precision with BitsAndBytes for efficient deployment

Uses

Direct Use

The model is suitable for biomedical question-answering and other related language generation tasks.

Out-of-Scope Use

Not recommended for general-purpose NLP tasks outside the biomedical domain or for clinical decision-making.

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How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("BeastGokul/BioMistral-7B-Finetuned")
model = AutoModelForCausalLM.from_pretrained("BeastGokul/BioMistral-7B-Finetuned")

# Example usage
input_text = "What are the symptoms of diabetes?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Training Procedure

The model was fine-tuned using the LoRA (Low-Rank Adaptation) method, with a configuration set for biomedical question-answering.

Training Hyperparameters Precision: 4-bit quantization with BitsAndBytes Learning Rate: 2e-5 Batch Size: Effective batch size of 16 (4 per device, gradient accumulation steps of 4) Number of Epochs: 3

Framework versions

PEFT 0.13.2