Upload fine-tuned MedGemma-4B ECG model with evaluation score: 1.000
Browse files- .gitattributes +2 -0
- README.md +73 -0
- added_tokens.json +3 -0
- checkpoint-60/README.md +202 -0
- checkpoint-60/adapter_config.json +39 -0
- checkpoint-60/adapter_model.safetensors +3 -0
- checkpoint-60/added_tokens.json +3 -0
- checkpoint-60/optimizer.pt +3 -0
- checkpoint-60/preprocessor_config.json +29 -0
- checkpoint-60/processor_config.json +4 -0
- checkpoint-60/rng_state.pth +3 -0
- checkpoint-60/scheduler.pt +3 -0
- checkpoint-60/special_tokens_map.json +33 -0
- checkpoint-60/tokenizer.json +3 -0
- checkpoint-60/tokenizer.model +3 -0
- checkpoint-60/tokenizer_config.json +0 -0
- checkpoint-60/trainer_state.json +454 -0
- checkpoint-60/training_args.bin +3 -0
- config.json +63 -0
- evaluation_results.json +32 -0
- generation_config.json +10 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +890 -0
- preprocessor_config.json +29 -0
- processor_config.json +4 -0
- runs/Jun25_07-04-39_5c45422eb8d3/events.out.tfevents.1750835082.5c45422eb8d3.954.0 +3 -0
- runs/Jun25_07-31-32_5c45422eb8d3/events.out.tfevents.1750836696.5c45422eb8d3.954.1 +3 -0
- special_tokens_map.json +33 -0
- tokenizer.json +3 -0
- tokenizer.model +3 -0
- tokenizer_config.json +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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checkpoint-60/tokenizer.json filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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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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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/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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| 173 |
+
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
| 174 |
+
|
| 175 |
+
**BibTeX:**
|
| 176 |
+
|
| 177 |
+
[More Information Needed]
|
| 178 |
+
|
| 179 |
+
**APA:**
|
| 180 |
+
|
| 181 |
+
[More Information Needed]
|
| 182 |
+
|
| 183 |
+
## Glossary [optional]
|
| 184 |
+
|
| 185 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
| 186 |
+
|
| 187 |
+
[More Information Needed]
|
| 188 |
+
|
| 189 |
+
## More Information [optional]
|
| 190 |
+
|
| 191 |
+
[More Information Needed]
|
| 192 |
+
|
| 193 |
+
## Model Card Authors [optional]
|
| 194 |
+
|
| 195 |
+
[More Information Needed]
|
| 196 |
+
|
| 197 |
+
## Model Card Contact
|
| 198 |
+
|
| 199 |
+
[More Information Needed]
|
| 200 |
+
### Framework versions
|
| 201 |
+
|
| 202 |
+
- PEFT 0.15.2
|
checkpoint-60/adapter_config.json
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"alpha_pattern": {},
|
| 3 |
+
"auto_mapping": null,
|
| 4 |
+
"base_model_name_or_path": "unsloth/medgemma-4b-pt",
|
| 5 |
+
"bias": "none",
|
| 6 |
+
"corda_config": null,
|
| 7 |
+
"eva_config": null,
|
| 8 |
+
"exclude_modules": null,
|
| 9 |
+
"fan_in_fan_out": false,
|
| 10 |
+
"inference_mode": true,
|
| 11 |
+
"init_lora_weights": true,
|
| 12 |
+
"layer_replication": null,
|
| 13 |
+
"layers_pattern": null,
|
| 14 |
+
"layers_to_transform": null,
|
| 15 |
+
"loftq_config": {},
|
| 16 |
+
"lora_alpha": 16,
|
| 17 |
+
"lora_bias": false,
|
| 18 |
+
"lora_dropout": 0,
|
| 19 |
+
"megatron_config": null,
|
| 20 |
+
"megatron_core": "megatron.core",
|
| 21 |
+
"modules_to_save": null,
|
| 22 |
+
"peft_type": "LORA",
|
| 23 |
+
"r": 16,
|
| 24 |
+
"rank_pattern": {},
|
| 25 |
+
"revision": null,
|
| 26 |
+
"target_modules": [
|
| 27 |
+
"up_proj",
|
| 28 |
+
"gate_proj",
|
| 29 |
+
"o_proj",
|
| 30 |
+
"k_proj",
|
| 31 |
+
"q_proj",
|
| 32 |
+
"down_proj",
|
| 33 |
+
"v_proj"
|
| 34 |
+
],
|
| 35 |
+
"task_type": "CAUSAL_LM",
|
| 36 |
+
"trainable_token_indices": null,
|
| 37 |
+
"use_dora": false,
|
| 38 |
+
"use_rslora": false
|
| 39 |
+
}
|
checkpoint-60/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4471de03750b5e73f26857feec9f916f975dc2b8b91a10752b8d028baf32f692
|
| 3 |
+
size 131252288
|
checkpoint-60/added_tokens.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"<image_soft_token>": 262144
|
| 3 |
+
}
|
checkpoint-60/optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:caf8d4533637a407703a96e0860a1f7155561b794c5dfcb4e4456fa267b8acbe
|
| 3 |
+
size 61094293
|
checkpoint-60/preprocessor_config.json
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"do_convert_rgb": null,
|
| 3 |
+
"do_normalize": true,
|
| 4 |
+
"do_pan_and_scan": null,
|
| 5 |
+
"do_rescale": true,
|
| 6 |
+
"do_resize": true,
|
| 7 |
+
"image_mean": [
|
| 8 |
+
0.5,
|
| 9 |
+
0.5,
|
| 10 |
+
0.5
|
| 11 |
+
],
|
| 12 |
+
"image_processor_type": "Gemma3ImageProcessor",
|
| 13 |
+
"image_seq_length": 256,
|
| 14 |
+
"image_std": [
|
| 15 |
+
0.5,
|
| 16 |
+
0.5,
|
| 17 |
+
0.5
|
| 18 |
+
],
|
| 19 |
+
"pan_and_scan_max_num_crops": null,
|
| 20 |
+
"pan_and_scan_min_crop_size": null,
|
| 21 |
+
"pan_and_scan_min_ratio_to_activate": null,
|
| 22 |
+
"processor_class": "Gemma3Processor",
|
| 23 |
+
"resample": 2,
|
| 24 |
+
"rescale_factor": 0.00392156862745098,
|
| 25 |
+
"size": {
|
| 26 |
+
"height": 896,
|
| 27 |
+
"width": 896
|
| 28 |
+
}
|
| 29 |
+
}
|
checkpoint-60/processor_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"image_seq_length": 256,
|
| 3 |
+
"processor_class": "Gemma3Processor"
|
| 4 |
+
}
|
checkpoint-60/rng_state.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f1d565802a8e26c4e8a31328752b7a7fdc186d9401aa008e65697d0ad8c22e33
|
| 3 |
+
size 14645
|
checkpoint-60/scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:8fd7a3e7c36d3a0c58b62d5ad1ac54d1ff59b338836f3a1c760238fa25a50039
|
| 3 |
+
size 1465
|
checkpoint-60/special_tokens_map.json
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"boi_token": "<start_of_image>",
|
| 3 |
+
"bos_token": {
|
| 4 |
+
"content": "<bos>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false
|
| 9 |
+
},
|
| 10 |
+
"eoi_token": "<end_of_image>",
|
| 11 |
+
"eos_token": {
|
| 12 |
+
"content": "<eos>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false
|
| 17 |
+
},
|
| 18 |
+
"image_token": "<image_soft_token>",
|
| 19 |
+
"pad_token": {
|
| 20 |
+
"content": "<pad>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false
|
| 25 |
+
},
|
| 26 |
+
"unk_token": {
|
| 27 |
+
"content": "<unk>",
|
| 28 |
+
"lstrip": false,
|
| 29 |
+
"normalized": false,
|
| 30 |
+
"rstrip": false,
|
| 31 |
+
"single_word": false
|
| 32 |
+
}
|
| 33 |
+
}
|
checkpoint-60/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:a872e3bb510a751b26bd65f61aad05f948c9cf78fe4f787aebd197b393cc4081
|
| 3 |
+
size 33384667
|
checkpoint-60/tokenizer.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
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|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
|
| 3 |
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size 4689074
|
checkpoint-60/tokenizer_config.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
checkpoint-60/trainer_state.json
ADDED
|
@@ -0,0 +1,454 @@
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|
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checkpoint-60/training_args.bin
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size 6097
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config.json
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|
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|
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|
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|
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evaluation_results.json
ADDED
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{
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"example_id": 0,
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"score": 1.0,
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"generated": "**IMPRESSION**\n\ud83d\udea8 Normal Sinus Rhythm is present.\n\n**DETAILED ANALYSIS**\n- Normal Sinus Rhythm (NORM): Present with high confidence (96%).\n- Conduction Disturbance (CD): Possible, with 45% confidence.\n\n**CLINICAL RECOMMENDATIONS**\nThis ECG is entirely expected in an asymptomatic patient on chronic amiodarone maintenance. The only potential concern is the possible CD, which could indicate myocardial fibrosis, requiring cardiology consultation and possible stress testing to monitor for conduction system changes.",
|
| 17 |
+
"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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{
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|
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"score": 1.0,
|
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"generated": "**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 is a classic presentation of a sick sinus rhythm. The bradycardia is not enough to cause syncope, but the underlying conduction system disease (a sick sinus rhythm) can cause a life-threatening arrhythmia called a ventricular tachyarrhythmia. The patient must be admitted and ECG monitored with telemetry. He needs an urgent pacemaker insertion before discharge to prevent future syncopal events.",
|
| 23 |
+
"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."
|
| 24 |
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},
|
| 25 |
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{
|
| 26 |
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"example_id": 2,
|
| 27 |
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"score": 1.0,
|
| 28 |
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"generated": "**IMPRESSION**\n\ud83d\udea8 Normal Sinus Rhythm is present.\n\n**DETAILED ANALYSIS**\n- Normal Sinus Rhythm (NORM): Present with very high confidence (98%).\n\n**CLINICAL RECOMMENDATIONS**\nThis ECG is a reassuring and normal finding. In a healthy, active, asymptomatic patient, the absence of pathologic findings is the most appropriate treatment. No further cardiac workup is needed. The patient can continue to follow an exercise-based lifestyle.",
|
| 29 |
+
"reference": "**IMPRESSION**\n\ud83d\udea8 Normal Sinus Rhythm is present.\n\n**DETAILED ANALYSIS**\n- Normal Sinus Rhythm (NORM): Present with very high confidence (98%).\n\n**CLINICAL RECOMMENDATIONS**\nThis is a completely normal ECG for a healthy middle-aged adult. It provides a valuable baseline for any future comparisons. He should be encouraged to continue his healthy lifestyle. No further cardiac evaluation is indicated."
|
| 30 |
+
}
|
| 31 |
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]
|
| 32 |
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}
|
generation_config.json
ADDED
|
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}
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model-00001-of-00002.safetensors
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preprocessor_config.json
ADDED
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@@ -0,0 +1,29 @@
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|
| 1 |
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|
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| 19 |
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| 22 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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processor_config.json
ADDED
|
@@ -0,0 +1,4 @@
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runs/Jun25_07-04-39_5c45422eb8d3/events.out.tfevents.1750835082.5c45422eb8d3.954.0
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special_tokens_map.json
ADDED
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| 1 |
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| 3 |
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| 4 |
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| 9 |
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| 31 |
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|
| 32 |
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tokenizer.json
ADDED
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version https://git-lfs.github.com/spec/v1
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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tokenizer_config.json
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