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Model: OussamaEL/MedGemma-4B-ECG Source: Original Platform
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README.md
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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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"<image_soft_token>": 262144
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}
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checkpoint-60/README.md
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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
|
||||
|
||||
<!-- Provide a quick summary of what the model is/does. -->
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||||
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||||
|
||||
|
||||
## Model Details
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||||
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### Model Description
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||||
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||||
<!-- Provide a longer summary of what this model is. -->
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||||
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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]
|
||||
- **Language(s) (NLP):** [More Information Needed]
|
||||
- **License:** [More Information Needed]
|
||||
- **Finetuned from model [optional]:** [More Information Needed]
|
||||
|
||||
### Model Sources [optional]
|
||||
|
||||
<!-- 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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||||
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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]
|
||||
|
||||
### 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]
|
||||
|
||||
### Out-of-Scope Use
|
||||
|
||||
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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||||
|
||||
[More Information Needed]
|
||||
|
||||
## Bias, Risks, and Limitations
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||||
|
||||
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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||||
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||||
[More Information Needed]
|
||||
|
||||
### Recommendations
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||||
|
||||
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
||||
|
||||
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]
|
||||
|
||||
## Training Details
|
||||
|
||||
### Training Data
|
||||
|
||||
<!-- 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. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Training Procedure
|
||||
|
||||
<!-- 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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||||
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||||
[More Information Needed]
|
||||
|
||||
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||||
#### Training Hyperparameters
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||||
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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]
|
||||
|
||||
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Evaluation
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||||
|
||||
<!-- This section describes the evaluation protocols and provides the results. -->
|
||||
|
||||
### 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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||||
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||||
[More Information Needed]
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||||
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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]
|
||||
|
||||
#### Metrics
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||||
|
||||
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Results
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||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Summary
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||||
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||||
|
||||
|
||||
## Model Examination [optional]
|
||||
|
||||
<!-- Relevant interpretability work for the model goes here -->
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||||
|
||||
[More Information Needed]
|
||||
|
||||
## Environmental Impact
|
||||
|
||||
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
||||
|
||||
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).
|
||||
|
||||
- **Hardware Type:** [More Information Needed]
|
||||
- **Hours used:** [More Information Needed]
|
||||
- **Cloud Provider:** [More Information Needed]
|
||||
- **Compute Region:** [More Information Needed]
|
||||
- **Carbon Emitted:** [More Information Needed]
|
||||
|
||||
## Technical Specifications [optional]
|
||||
|
||||
### Model Architecture and Objective
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Compute Infrastructure
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Hardware
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Software
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Citation [optional]
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
|
||||
**BibTeX:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
**APA:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Glossary [optional]
|
||||
|
||||
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## More Information [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Authors [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Contact
|
||||
|
||||
[More Information Needed]
|
||||
### Framework versions
|
||||
|
||||
- PEFT 0.15.2
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checkpoint-60/adapter_config.json
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checkpoint-60/adapter_config.json
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checkpoint-60/special_tokens_map.json
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evaluation_results.json
Normal file
32
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Normal file
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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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29
preprocessor_config.json
Normal file
29
preprocessor_config.json
Normal file
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4
processor_config.json
Normal file
4
processor_config.json
Normal file
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{
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33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
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3
tokenizer.json
Normal file
3
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Normal file
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3
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Normal file
3
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Normal file
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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