74 lines
2.6 KiB
Markdown
74 lines
2.6 KiB
Markdown
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---
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language: en
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license: apache-2.0
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library_name: transformers
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base_model: unsloth/medgemma-4b-pt
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tags:
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- medical
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- ecg
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- cardiology
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- report-generation
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- unsloth
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- gemma
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- fine-tuned
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pipeline_tag: text-generation
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---
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# MedGemma-4B ECG Report Generator
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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.
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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.
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## Model Details
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- **Base Model:** `unsloth/medgemma-4b-pt`
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- **Fine-tuning Method:** Unsloth + LoRA (merged into base model)
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- **Training Data:** 500 curated ECG interpretation examples.
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- **Evaluation Score:** The model achieved an average structural correctness score of **1.000 / 1.0** on a hold-out set.
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## Usage
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This model follows a standard instruction format. Provide the instruction and the structured input to get a clinical report.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_name = "OussamaEL/MedGemma-4B-ECG"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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# Alpaca prompt format is required
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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.
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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instruction = "You are a medical AI assistant specializing in ECG interpretation. Analyze the ECG findings and patient context to generate a clinical report."
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input_text = """ECG FINDINGS:
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- Atrial Fibrillation (AFIB): 95% confidence, Present
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- Sinus Tachycardia (STACH): 88% confidence, Present
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PATIENT CONTEXT:
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68-year-old male with diabetes and hypertension presents with 2 days of worsening shortness of breath and leg swelling."""
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inputs = tokenizer(
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alpaca_prompt.format(instruction, input_text, ""),
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return_tensors="pt"
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).to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:")[1].strip())
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```
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This model is intended for research and development purposes and is not a substitute for professional medical advice.
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