---
base_model: openai/gpt-oss-20b
datasets:
- kingabzpro/dermatology-qa-firecrawl-dataset
library_name: transformers
model_name: gpt-oss-20b-dermatology-qa
tags:
- generated_from_trainer
- trl
- sft
- dermatology
- medical
licence: license
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---

# Model Card for gpt-oss-20b-dermatology-qa

This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [kingabzpro/dermatology-qa-firecrawl-dataset](https://huggingface.co/kingabzpro/gpt-oss-20b-medical-qa) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline

question = "How does the source suggest clinicians approach the diagnosis of rosacea?"

# Load pipeline
generator = pipeline(
    "text-generation",
    model="kingabzpro/gpt-oss-20b-dermatology-qa",
    device="cuda"  # or device=0
)


# Run inference (passing in chat-style format)
output = generator(
    [{"role": "user", "content": question}],
    max_new_tokens=200,
    return_full_text=False
)[0]

print(output["generated_text"])

# The source says that clinicians should use a combination of clinical signs and symptoms when diagnosing rosacea, rather than relying on a single feature.
```

## Training procedure

 This model was trained with SFT.

### Framework versions

- TRL: 0.21.0
- Transformers: 4.55.4
- Pytorch: 2.8.0.dev20250319+cu128
- Datasets: 4.0.0
- Tokenizers: 0.21.4

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```