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Model: OussamaEL/MedGemma-4B-ECG
Source: Original Platform

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+base_model: unsloth/medgemma-4b-pt
+tags:
+- medical
+- ecg
+- cardiology
+- report-generation
+- unsloth
+- gemma
+- fine-tuned
+pipeline_tag: text-generation
+---
+
+# MedGemma-4B ECG Report Generator
+
+This is a **fully merged, standalone model** fine-tuned from `unsloth/medgemma-4b-pt` for ECG interpretation and clinical report generation. It was trained using the Unsloth library for high-efficiency, memory-optimized fine-tuning.
+
+This model is designed to take structured output from a primary ML classifier (which provides findings like "Atrial Fibrillation: 82% confidence, Present") and synthesize it into a coherent, human-readable clinical report, complete with an impression, detailed analysis, and clinical recommendations.
+
+## Model Details
+
+- **Base Model:** `unsloth/medgemma-4b-pt`
+- **Fine-tuning Method:** Unsloth + LoRA (merged into base model)
+- **Training Data:** 500 curated ECG interpretation examples.
+- **Evaluation Score:** The model achieved an average structural correctness score of **1.000 / 1.0** on a hold-out set.
+
+## Usage
+
+This model follows a standard instruction format. Provide the instruction and the structured input to get a clinical report.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_name = "OussamaEL/MedGemma-4B-ECG"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# Alpaca prompt format is required
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{}
+### Input:
+{}
+### Response:
+{}"""
+
+instruction = "You are a medical AI assistant specializing in ECG interpretation. Analyze the ECG findings and patient context to generate a clinical report."
+
+input_text = """ECG FINDINGS:
+- Atrial Fibrillation (AFIB): 95% confidence, Present
+- Sinus Tachycardia (STACH): 88% confidence, Present
+
+PATIENT CONTEXT:
+68-year-old male with diabetes and hypertension presents with 2 days of worsening shortness of breath and leg swelling."""
+
+inputs = tokenizer(
+    alpaca_prompt.format(instruction, input_text, ""),
+    return_tensors="pt"
+).to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:")[1].strip())
+```
+    
+This model is intended for research and development purposes and is not a substitute for professional medical advice.