base_model: BioMistral/BioMistral-7B
library_name: peft
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- biology
- medical
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.
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.
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))
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