---
language:
- en
- fr
license: apache-2.0
library_name: transformers
tags:
- smollm3
- fine-tuned
- causal-lm
- text-generation
- tonic
- legml
pipeline_tag: text-generation
base_model: openai/gpt-oss-20b
datasets:
- FreedomIntelligence/medical-o1-reasoning-SFT
author: Joseph "Tonic" Pollack
experiment_name: exp_20250809_122413
trackio_url: https://huggingface.co/spaces/Tonic/track-tonic
hardware: GPU (H100/A100)
training_config: GPT-OSS Configuration
trainer_type: SFTTrainer
batch_size: 4
learning_rate: 0.0002
max_epochs: 2
max_seq_length: 2048
dataset_size: ~20K samples
dataset_format: Reasoning
gradient_accumulation_steps: 16
---

# med-gpt-oss-20b

A fine-tuned version of OpenAI's GPT-OSS-20B model for medical reasoning and instruction following.

## Model Details

- **Base Model**: openai/gpt-oss-20B
- **Model Type**: Causal Language Model
- **Languages**: English
- **License**: Apache 2.0


## Usage

### Main Model

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the main model
model = AutoModelForCausalLM.from_pretrained(
    "Tonic/med-gpt-oss-20b",
    device_map="auto",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained("Tonic/med-gpt-oss-20b")

# Generate text
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device.type)
output = model.generate(**input_ids, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Training Information

### Training Configuration
- **Base Model**: openai/gpt-oss-20b
- **Dataset**: FreedomIntelligence/medical-o1-reasoning-SFT
- **Training Config**: GPT-OSS Configuration
- **Trainer Type**: SFTTrainer


### Training Parameters
- **Batch Size**: 4
- **Gradient Accumulation**: 16
- **Learning Rate**: 2e-4
- **Max Epochs**: 1
- **Sequence Length**: 2048

### Training Infrastructure
- **Hardware**: GPU (H100/A100)
- **Monitoring**: Trackio integration
- **Experiment**: med-track

## Model Architecture

This is a fine-tuned version of the SmolLM3-3B model with the following specifications:

- **Base Model**: SmolLM3-3B
- **Parameters**: ~3B
- **Context Length**: 2048
- **Languages**: English, French
- **Architecture**: Transformer-based causal language model

## Performance

The model provides:
- **Medical Reasoning**: High-quality medical reasoning
- **Conversation**: Medical instruction following 


## Limitations

1. **Context Length**: Limited by the model's maximum sequence length
2. **Bias**: May inherit biases from the training data
3. **Factual Accuracy**: May generate incorrect or outdated information
4. **Safety**: Should be used responsibly with appropriate safeguards


## Training Data

The model was fine-tuned on:
- **Dataset**: FreedomIntelligence/medical-o1-reasoning-SFT
- **Size**: ~20K samples
- **Format**: reasoning
- **Languages**: English

## Monitoring

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62a3bb1cd0d8c2c2169f0b88/rM6DkvUeME2EgQQkhwu9E.png)

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{med_gpt_oss_20B,
  title={{med-gpt-oss-20b}},
  author={Joseph "Tonic" Pollack},
  year={2024},
  url={https://huggingface.co/Tonic/med-gpt-oss-20b}
}
```

## License

This model is licensed under the Apache 2.0 License.

## Acknowledgments

- **Base Model**: SmolLM3-3B by HuggingFaceTB
- **Training Framework**: PyTorch, Transformers, PEFT
- **Monitoring**: Trackio integration
- **Quantization**: torchao library

## Support

For questions and support:
- Open an issue on the Hugging Face repository
- Check the model documentation
- Review the training logs and configuration

## Repository Structure

```
Tonic/med-gpt-oss-20b/
├── README.md (this file)
├── config.json
├── pytorch_model.bin
├── tokenizer.json
└── tokenizer_config.json
```

## Usage Examples

### Text Generation
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Tonic/med-gpt-oss-20b")
tokenizer = AutoTokenizer.from_pretrained("Tonic/med-gpt-oss-20b")

text = "The future of artificial intelligence is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Conversation
```python
def chat_with_model(prompt, max_length=100):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=max_length)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

response = chat_with_model("Hello, how are you today?")
print(response)
```

### Advanced Usage
```python
# With generation parameters
outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)
```

## Monitoring and Tracking

This model was trained with comprehensive monitoring:
- **Trackio Space**: N/A
- **Experiment**: med-track
- **Dataset Repository**: https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking
- **Training Logs**: Available in the experiment data

## Deployment

### Requirements
```bash
pip install torch transformers accelerate

```

### Hardware Requirements
- **Main Model**: GPU with 8GB+ VRAM recommended

## Changelog

- **v1.0.0**: Initial release with fine-tuned model