Instructions to use ktl-bmrc/MedReg-SLM-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ktl-bmrc/MedReg-SLM-v0.1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ktl-bmrc/MedReg-SLM-v0.1",
	filename="MedReg-SLM-v0.1.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ktl-bmrc/MedReg-SLM-v0.1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ktl-bmrc/MedReg-SLM-v0.1
# Run inference directly in the terminal:
llama-cli -hf ktl-bmrc/MedReg-SLM-v0.1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ktl-bmrc/MedReg-SLM-v0.1
# Run inference directly in the terminal:
llama-cli -hf ktl-bmrc/MedReg-SLM-v0.1

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ktl-bmrc/MedReg-SLM-v0.1
# Run inference directly in the terminal:
./llama-cli -hf ktl-bmrc/MedReg-SLM-v0.1

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ktl-bmrc/MedReg-SLM-v0.1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ktl-bmrc/MedReg-SLM-v0.1

Use Docker

docker model run hf.co/ktl-bmrc/MedReg-SLM-v0.1

LM Studio
Jan

vLLM

How to use ktl-bmrc/MedReg-SLM-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ktl-bmrc/MedReg-SLM-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ktl-bmrc/MedReg-SLM-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ktl-bmrc/MedReg-SLM-v0.1

Ollama
How to use ktl-bmrc/MedReg-SLM-v0.1 with Ollama:
```
ollama run hf.co/ktl-bmrc/MedReg-SLM-v0.1
```

Unsloth Studio new

How to use ktl-bmrc/MedReg-SLM-v0.1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ktl-bmrc/MedReg-SLM-v0.1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ktl-bmrc/MedReg-SLM-v0.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ktl-bmrc/MedReg-SLM-v0.1 to start chatting

Docker Model Runner
How to use ktl-bmrc/MedReg-SLM-v0.1 with Docker Model Runner:
```
docker model run hf.co/ktl-bmrc/MedReg-SLM-v0.1
```

Lemonade

How to use ktl-bmrc/MedReg-SLM-v0.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ktl-bmrc/MedReg-SLM-v0.1

Run and chat with the model

lemonade run user.MedReg-SLM-v0.1-{{QUANT_TAG}}

List all available models

lemonade list

MedReg-SLM-v0.1 (MXFP4 Quantization)

This model is a GGUF version of openai/gpt-oss-120b, fine-tuned for medical regulation tasks by KTL (Korea Testing Laboratory) Bio Medical Research Center. It has been quantized in the MXFP4 format for efficient deployment.

Model Details

Model Name: MedReg-SLM-v0.1
Base Model: openai/gpt-oss-120b
Quantization: MXFP4
Organization: KTL (Korea Testing Laboratory) - BMRC
Language: Korean (ko)
Description: This model is developed by Korea Testing Laboratory (KTL) through a project funded by the Ministry of Trade, Industry & Resources. It is based on openai/gpt-oss-120b and has been fine-tuned specifically for medical regulation tasks.

Acknowledgements

This work was supported by the Technology Innovation Program(or Industrial Strategic Technology Development Program)(Development and Validation of Generative AI-based Digital Medical Product Verification Technology) funded By the Ministry of Trade, Industry & Resources(MOTIR, Korea)

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GGUF

Model size

117B params

Architecture

gptoss

Hardware compatibility

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