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Bio-Mistral-7B-finetuned

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 ---
-base_model: BioMistral/BioMistral-7B
-library_name: peft
-license: apache-2.0
 language:
-- en
-pipeline_tag: text-generation
 ---
-# Model Card for Model ID
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 ## Model Details
 ### Model Description
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-### Framework versions
-- PEFT 0.13.2

 ---
+base_model: BioMistral/BioMistral-7B
+library_name: peft
+license: apache-2.0
 language:
+- en
+pipeline_tag: text-generation
 ---
+# 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
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("YourModelPath/BioMistral-7B-Finetuned")
+model = AutoModelForCausalLM.from_pretrained("YourModelPath/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