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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

You need to agree to share your Hugging Face 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

Trained by MediaAtlas — LLM fine-tuning on your own data, trained in the EU, weights delivered. Pricing · All our models

Na kratko: Raziskovalni slovenski biomedicinski jezikovni model (27B), GGUF za llama.cpp in LM Studio. Samo za raziskave in nekomercialno rabo; ni medicinski pripomoček. Na cjvt/slovenian-llm-eval je izenačen z GaMS3-12B-Instruct po log-likelihood in pred njim pri neposrednem vprašanju (0,852 proti 0,773). Sprejme tudi slike.

Vision / slike: takes images too. Load the mmproj-*.gguf next to the model (llama.cpp --mmproj; LM Studio pairs it automatically). Our training was text-only: image understanding is the base model's and was not evaluated in Slovenian.

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).

Native tool / function calling (Qwen3.6 XML format) works via llama.cpp --jinja (the embedded chat template) — pass tools and the model emits <tool_call><function=…><parameter=…>…</function></tool_call>.

Evaluation (summary)

Slovenian-LLM-Eval (cjvt/slovenian-llm-eval), 0-shot, n = 500 examples/task (3,500 total), scored against GaMS3-12B-Instruct with the same code on the same examples:

protocol	Vran 27B	GaMS3-12B-Instruct
asked through the chat template (letter answer)	0.852	0.773
log-likelihood `acc_norm` (lm-eval convention)	0.670	0.663

Tied under the log-likelihood convention, ahead when both models are asked the question directly. Internal medical eval (130 questions, 13 domains, multi-judge): pravilnost 4.99 / popolnost 4.97. Per-task tables, confidence intervals and caveats are on the main model card.

Lineage

Qwen3.6-27B → uncensored "heretic" variant → texdata/Qwen3.6-27B-slo-med-mt → Vran. The chain passes through a model with safety alignment removed, so Vran carries no upstream guardrails — see the main card.

⚠️ 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.