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Model: OussamaEL/MedGemma-4B-ECG Source: Original Platform
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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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{
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"<image_soft_token>": 262144
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}
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checkpoint-60/README.md
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---
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base_model: unsloth/medgemma-4b-pt
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library_name: peft
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.15.2
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checkpoint-60/adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "unsloth/medgemma-4b-pt",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_bias": false,
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"lora_dropout": 0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"up_proj",
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"gate_proj",
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"o_proj",
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"k_proj",
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"q_proj",
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"down_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_rslora": false
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}
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checkpoint-60/adapter_model.safetensors
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checkpoint-60/adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:4471de03750b5e73f26857feec9f916f975dc2b8b91a10752b8d028baf32f692
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size 131252288
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checkpoint-60/added_tokens.json
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checkpoint-60/added_tokens.json
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{
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"<image_soft_token>": 262144
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}
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checkpoint-60/optimizer.pt
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checkpoint-60/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:caf8d4533637a407703a96e0860a1f7155561b794c5dfcb4e4456fa267b8acbe
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size 61094293
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checkpoint-60/preprocessor_config.json
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{
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"do_convert_rgb": null,
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"do_normalize": true,
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"do_pan_and_scan": null,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.5,
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0.5,
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0.5
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],
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"image_processor_type": "Gemma3ImageProcessor",
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"image_seq_length": 256,
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"image_std": [
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0.5,
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63
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Normal file
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32
evaluation_results.json
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"reference": "**IMPRESSION**\n\ud83d\udea8 Normal Sinus Rhythm is present.\n\n**DETAILED ANALYSIS**\n- Normal Sinus Rhythm (NORM): Present with very high confidence (96%).\n- Conduction Disturbance (CD): Possible, with 45% confidence.\n\n**CLINICAL RECOMMENDATIONS**\nWhile the ECG is reassuringly in sinus rhythm, it does not rule out subclinical cardiotoxicity. The possible conduction disturbance could be non-specific T-wave flattening. Routine surveillance with an echocardiogram to assess her left ventricular ejection fraction is a critical part of her follow-up care, as LV dysfunction can develop insidiously long after chemotherapy is completed."
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||||||
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"reference": "**IMPRESSION**\n\ud83d\udea8 Conduction Disturbance is present. Sinus Bradycardia is also present.\n\n**DETAILED ANALYSIS**\n- Conduction Disturbance (CD): Present with very high confidence (99%).\n- Sinus Bradycardia (SBRAD): Present with very high confidence (99%).\n\n**CLINICAL RECOMMENDATIONS**\nThis ECG shows complete (third-degree) AV block, which is the cause of his syncope. This is a life-threatening emergency. The atria and ventricles are beating independently, with a very slow ventricular escape rhythm. He requires immediate admission, application of external pacing pads, and an urgent cardiology/EP consultation for permanent pacemaker implantation."
|
||||||
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||||||
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|
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10
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}
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|
}
|
||||||
29
preprocessor_config.json
Normal file
29
preprocessor_config.json
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
{
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
"image_processor_type": "Gemma3ImageProcessor",
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|
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|
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|
"image_std": [
|
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|
0.5,
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|
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|
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|
"width": 896
|
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|
}
|
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|
}
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4
processor_config.json
Normal file
4
processor_config.json
Normal file
@@ -0,0 +1,4 @@
|
|||||||
|
{
|
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|
"image_seq_length": 256,
|
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|
"processor_class": "Gemma3Processor"
|
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|
}
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|
version https://git-lfs.github.com/spec/v1
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oid sha256:9ce176b37ad4d8a01565515adaccf9308e4d91a114db148c3a3697cf948bfe9c
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size 19622
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|
size 12857
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33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
|
|||||||
|
{
|
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|
"boi_token": "<start_of_image>",
|
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|
"bos_token": {
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
"image_token": "<image_soft_token>",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
}
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|
}
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
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|
version https://git-lfs.github.com/spec/v1
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|
oid sha256:a872e3bb510a751b26bd65f61aad05f948c9cf78fe4f787aebd197b393cc4081
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|
size 33384667
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3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
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|
version https://git-lfs.github.com/spec/v1
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|
oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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|
size 4689074
|
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51346
tokenizer_config.json
Normal file
51346
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user