README.md

---
base_model: google/gemma-3-1b-it
library_name: peft
---

# Model Card for Julia, an AI for Medical Reasoning

Julia Medical Reasoning is a fine-tuned version of Google's Gemma-3 model, optimized for clinical reasoning, diagnostic support, and medical question answering in English. It has been adapted through supervised fine-tuning using a curated dataset composed of medical case studies, question-answer pairs, and evidence-based medicine protocols.

## Model Details

### Model Description

- **Developed by:** Miguel Araújo Julio
- **Shared by:** Miguel Araújo Julio
- **Model type:** Causal Language Model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** google/gemma-3-1b-it

### Model Sources [optional]

- **Repository:** N/A

## Uses

### Direct Use

- Medical education and training.
- Assisting clinicians with reasoning through differential diagnoses.
- Generating answers to patient queries and common clinical questions.

### Downstream Use

- Integration into clinical decision support tools.
- Augmenting chatbot interfaces for hospitals or telemedicine platforms.

### Out-of-Scope Use

- Final medical diagnosis or treatment recommendation without human oversight.
- Use in high-risk clinical environments without regulatory clearance.

## Bias, Risks, and Limitations

The model may reproduce biases found in the training data and should not be considered a replacement for licensed medical professionals. There is a risk of hallucinated or outdated information being presented as fact.

### Recommendations

Users should validate outputs against trusted medical sources and consult with qualified professionals before making clinical decisions.

## How to Get Started with the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-username/julia-medical-reasoning")
tokenizer = AutoTokenizer.from_pretrained("your-username/julia-medical-reasoning")

inputs = tokenizer("What are common symptoms of diabetes?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```

## Training Details

### Training Data

The model was trained using a mix of open medical question-answer datasets, synthetic case-based reasoning examples, and filtered PubMed articles.

### Training Procedure

#### Preprocessing

Data was filtered to remove out-of-domain or unsafe content, and pre-tokenized using Gemma's tokenizer.

#### Training Hyperparameters

- **Training regime:** bf16 mixed precision

#### Speeds, Sizes, Times [optional]

- Fine-tuned for 3 epochs on a L4x4 GPU.

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

Curated benchmark sets of medical reasoning and multiple-choice questions (FreedomIntelligence/medical-o1-reasoning-SFT).

## Technical Specifications [optional]

### Model Architecture and Objective

Decoder-only transformer architecture following Gemma specifications.

### Compute Infrastructure

#### Hardware

L4 x 4 (96 GB)

#### Software

- PyTorch 2.1
- PEFT 0.14.0
- Transformers 4.40

## Citation [optional]

**BibTeX:**

```bibtex
@misc{julia2025,
  title={Julia Medical Reasoning: Fine-tuning Gemma for Medical Understanding},
  author={Miguel Araújo Julio},
  year={2025},
  url={https://huggingface.co/Miguell-J/julia-medical-reasoning}
}
```

## Model Card Authors [optional]

Miguel Araújo Julio

## Model Card Contact

- Email: julioaraujo.guel@gmail.com

### Framework versions

- PEFT 0.14.0