Instructions to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="texdata/Vran-27B-SLO-BioMed-Research-GGUF",
	filename="mmproj-qwen3.6-27b-med-slo-vran-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

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 texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

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 texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Use Docker

docker model run hf.co/texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "texdata/Vran-27B-SLO-BioMed-Research-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "texdata/Vran-27B-SLO-BioMed-Research-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Ollama
How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with Ollama:
```
ollama run hf.co/texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M
```

Unsloth Studio

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF 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 texdata/Vran-27B-SLO-BioMed-Research-GGUF 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 texdata/Vran-27B-SLO-BioMed-Research-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for texdata/Vran-27B-SLO-BioMed-Research-GGUF to start chatting

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with Docker Model Runner:
```
docker model run hf.co/texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M
```

Lemonade

How to use texdata/Vran-27B-SLO-BioMed-Research-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull texdata/Vran-27B-SLO-BioMed-Research-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vran-27B-SLO-BioMed-Research-GGUF-Q4_K_M

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

By requesting access you acknowledge that Vran is an experimental RESEARCH language model with NO medical intended purpose. It is not a medical device (EU MDR), is not CE/FDA-cleared, and is not for diagnosis, treatment, triage, prescribing, clinical decision-making, or patient care. You agree to use it only for research and non-commercial purposes, not to deploy it as a clinical or "health assistant", and you accept sole responsibility for compliance with applicable law (EU AI Act, GDPR, EU MDR). Provided AS IS, without warranty; the authors accept no liability.

Vran — Slovenian Biomedical Research LM (27B) — GGUF

GGUF builds of texdata/Vran-27B-SLO-BioMed-Research for llama.cpp / LM Studio. Vran is an experimental research language model for Slovenian medical / biomedical text.

⛔ Not a medical device. Not a clinical assistant.

No medical intended purpose. Not a medical device under EU MDR, not CE/FDA-cleared, not for diagnosis, treatment, triage, prescribing, clinical decision-making or patient care. Research and non-commercial use only. Does not replace a clinician.

Files

file	quant	size	note
`qwen3.6-27b-med-slo-vran-Q4_K_M.gguf`	Q4_K_M	~16 GB	recommended daily driver
`qwen3.6-27b-med-slo-vran-Q8_0.gguf`	Q8_0	~28 GB	near-lossless
`mmproj-qwen3.6-27b-med-slo-vran-F16.gguf`	—	~0.9 GB	vision projector (load alongside for image input)

MTP tensors are included (speculative decoding where supported).

⚠️ Disclaimers

Outputs may be incorrect, incomplete or fabricated. Not medical advice. Provided "AS IS", no warranty, no liability — the user assumes all responsibility. Compliance with the EU AI Act, GDPR and MDR is the user's responsibility.

License

Derivative of Qwen3.6-27B (Qwen license applies) + a research-only, non-commercial restriction. No commercial use.

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

Model tree for texdata/Vran-27B-SLO-BioMed-Research-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

texdata/Vran-27B-SLO-BioMed-Research

Quantized

(1)

this model