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

Libraries

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

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

pipe = pipeline("text-generation", model="texdata/Vran-27B-SLO-BioMed-Research")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("texdata/Vran-27B-SLO-BioMed-Research")
model = AutoModelForMultimodalLM.from_pretrained("texdata/Vran-27B-SLO-BioMed-Research", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use texdata/Vran-27B-SLO-BioMed-Research 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use texdata/Vran-27B-SLO-BioMed-Research 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 "texdata/Vran-27B-SLO-BioMed-Research" \
    --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": "texdata/Vran-27B-SLO-BioMed-Research",
		"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 "texdata/Vran-27B-SLO-BioMed-Research" \
        --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": "texdata/Vran-27B-SLO-BioMed-Research",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Vran-27B-SLO-BioMed-Research / README.md

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metadata

license: other
license_name: qwen-research-noncommercial
license_link: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE
language:
  - sl
  - en
base_model:
  - Qwen/Qwen3.6-27B
tags:
  - medical
  - biomedical
  - slovenian
  - research
  - qwen3
  - not-for-clinical-use
pipeline_tag: text-generation
library_name: transformers
extra_gated_prompt: >-
  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.
extra_gated_fields:
  Full name: text
  Affiliation / organization: text
  Country: country
  Intended research use: text
  I will use this strictly for research and non-commercial purposes, not for clinical use: checkbox
  I accept sole responsibility for regulatory and legal compliance: checkbox
extra_gated_button_content: Request research access

Vran — Slovenian Biomedical Research Language Model (27B)

Vran is an experimental research language model for Slovenian medical / biomedical text, built by supervised fine-tuning of Qwen3.6-27B (dense) on a full Slovenian medical corpus. It explains medical and scientific concepts in fluent, formal Slovenian for research and educational purposes, and supports native tool / function calling (Qwen3.6 XML format) for agentic research use.

⛔ Not a medical device. Not a clinical assistant.

Vran has no medical intended purpose. It is not a medical device under EU MDR 2017/745, is not CE-marked or FDA-cleared, and is not intended for diagnosis, treatment, triage, monitoring, prescribing, clinical decision-making, or patient care. It is released for research and non-commercial use only. It does not replace a qualified clinician.

Intended purpose (regulatory framing)

The intended purpose is research in natural-language processing and medical/biomedical NLP, and education. Any use that informs an actual clinical or health decision — for a patient, by a clinician, or by a member of the public — is out of scope and not permitted. Deployers who repurpose the model for a medical intended purpose assume all resulting regulatory obligations.

⚠️ Disclaimers

Not medical advice. Outputs are generated text and may be incorrect, incomplete, outdated, or fabricated (hallucinated); they must not be relied on for any health decision.
No clinician substitute. Always consult a licensed clinician.
No warranty, no liability. Provided "AS IS". The authors accept no liability for any harm arising from use. The user assumes all responsibility and risk.
Compliance is the user's. You are responsible for the EU AI Act, GDPR, MDR and any other applicable law in your jurisdiction.

Evaluation

Internal Slovenian medical eval suite (130 questions, 13 clinical domains), multi-judge (two independent LLM judges, averaged to de-bias), 1–5, run with the research system prompt:

metric	score (1–5)
pravilnost (correctness)	4.99
popolnost (completeness)	4.97

Per-domain (pravilnost / popolnost):

domena	pravilnost	popolnost
endokrinologija	5.00	5.00
farmakologija	5.00	4.95
gastroenterologija	5.00	4.95
ginekologija/porodništvo	5.00	5.00
hematologija	5.00	4.95
infektologija	4.95	4.90
kardiologija	4.95	4.90
nefrologija	5.00	5.00
nevrologija	5.00	5.00
pediatrija	5.00	5.00
pulmologija	5.00	4.95
toksikologija	5.00	4.95
urgentna medicina	5.00	5.00

Tool / function calling

Vran emits native Qwen3.6 XML tool calls when tools are provided, e.g.:

<tool_call><function=get_weather><parameter=city>Ljubljana</parameter></function></tool_call>

Trained on Hermes / APIGen-MT / ToolACE tool-call data alongside the medical corpus, so agentic/function-calling ability is retained without degrading Slovenian medical quality.

General Slovenian ability — Slovenian-LLM-Eval acc_norm (n=100/task), confirms the model retains general Slovenian competence:

arc_easy	arc_challenge	hellaswag	piqa	openbookqa	winogrande	boolq	AVG
0.67	0.52	0.58	0.67	0.46	0.66	0.86	0.631

(Internal benchmarks; not a substitute for clinical validation.)

Model & identity

Presents itself as "Vran", a Slovenian research language model — explicitly not a doctor and not a medical device. Multimodal (text + vision) Qwen3.6 architecture; MTP tensors preserved for speculative decoding.

Training data & lineage

Base: Qwen/Qwen3.6-27B (dense).
Slovenian medical corpus — our curated "golden" corpus, machine-translated from open English medical datasets (MedMCQA, MedQA-style, PubMedQA, MedReason, MedQuAD) with a strict term-preservation gate. Released separately so others can reproduce/extend: texdata/med-slo-sft.
Slovenian conversational data from cjvt/GaMS-Nemotron-Chat (retention, to keep general Slovenian fluency). Vran is not the GaMS model; only some conversational data is shared.
Tool-calling data — Hermes / APIGen-MT / ToolACE (English), for native function calling.
Trained as a full SFT (~~161k examples: 138k medical + 19k tool-calls + identity + retention); medical stays dominant (~~86%) so tool-calls don't dilute Slovenian medical quality.

License

Derivative of Qwen3.6-27B; the upstream Qwen license applies. As a derivative work, this release adds a research-only, non-commercial restriction. No commercial use.

Citation

@misc{vran_27b_slo_medical_research,
  title  = {Vran: a Slovenian biomedical research language model (27B)},
  author = {texdata},
  year   = {2026},
  note   = {Research artifact, non-commercial. Derivative of Qwen3.6-27B. Not a medical device.}
}