Update README.md

README.md

@@ -19,11 +19,37 @@ tags:
 ---
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+<!-- 🩺 Medical Diagnosis AI Model - Powered by Mistral-7B & LoRA 🚀
+🔹 Model Overview:
+
+Base Model: Mistral-7B (7.7 billion parameters)
+Fine-Tuning Method: LoRA (Low-Rank Adaptation)
+Quantization: bnb_4bit (reduces memory footprint while retaining performance)
+🔹 Parameter Details:
+
+Original Mistral-7B Parameters: 7.7 billion
+LoRA Fine-Tuned Parameters: ~4.48% of total model parameters (~340 million)
+Final Merged Model Size (bnb_4bit Quantized): ~4.5GB
+🔹 Key Features:
+✅ Accurate Diagnoses for symptoms like chest pain, dizziness, and breathlessness
+✅ Step-by-Step Medical Reasoning using Chain-of-Thought (CoT) prompting
+✅ Efficient Inference with reduced VRAM usage (ideal for GPUs with limited memory)
+
+🔹 Use Case:
+Designed to assist healthcare professionals by offering clear, evidence-backed insights for improved clinical decision-making.
+
+📝 Note: While this model offers valuable insights, it's intended to support — not replace — professional medical judgment. -->
 
 This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
 ## Model Details
+Original Mistral-7B Parameters: 7.7 billion
+LoRA Fine-Tuned Parameters: ~4.48% of total model parameters (~340 million)
+Final Merged Model Size (bnb_4bit Quantized): ~4.5GB
+🔹 Key Features:
+✅ Accurate Diagnoses for symptoms like chest pain, dizziness, and breathlessness
+✅ Step-by-Step Medical Reasoning using Chain-of-Thought (CoT) prompting
+✅ Efficient Inference with reduced VRAM usage (ideal for GPUs with limited memory)
 
 ### Model Description
 
@@ -31,13 +57,13 @@ This modelcard aims to be a base template for new models. It has been generated
 
 
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** [Ritvik Gaur]
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+- **Model type:** [Medical LLM]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** [Mistral-7B-Instruct-v3]
 
 ### Model Sources [optional]
 
@@ -48,13 +74,12 @@ This modelcard aims to be a base template for new models. It has been generated
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
-
+Python code for usage:
+ 
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
 # ✅ Load the uploaded model
 model = AutoModelForCausalLM.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct")
 tokenizer = AutoTokenizer.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct")
-
 # ✅ Sample inference
 prompt = "Patient reports chest pain and dizziness. What’s the likely diagnosis?"
 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)