Instructions to use Tonic/med-gpt-oss-20b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tonic/med-gpt-oss-20b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Tonic/med-gpt-oss-20b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tonic/med-gpt-oss-20b")
model = AutoModelForCausalLM.from_pretrained("Tonic/med-gpt-oss-20b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Tonic/med-gpt-oss-20b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tonic/med-gpt-oss-20b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tonic/med-gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tonic/med-gpt-oss-20b

SGLang

How to use Tonic/med-gpt-oss-20b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Tonic/med-gpt-oss-20b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tonic/med-gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Tonic/med-gpt-oss-20b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tonic/med-gpt-oss-20b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tonic/med-gpt-oss-20b with Docker Model Runner:
```
docker model run hf.co/Tonic/med-gpt-oss-20b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

Context Length: Limited by the model's maximum sequence length
Bias: May inherit biases from the training data
Factual Accuracy: May generate incorrect or outdated information
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

Citation

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

@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

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

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

# 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

pip install torch transformers accelerate

Hardware Requirements

Main Model: GPU with 8GB+ VRAM recommended

Changelog

v1.0.0: Initial release with fine-tuned model

Downloads last month: 25

Safetensors

Model size

21B params

Tensor type

BF16

Model tree for Tonic/med-gpt-oss-20b

Base model

openai/gpt-oss-20b

Finetuned

(511)

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Quantizations

2 models

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med-gpt-oss-20b