Instructions to use texdata/Vran-27B-SLO-BioMed-Research-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use texdata/Vran-27B-SLO-BioMed-Research-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("texdata/Qwen3.6-27B-slo-med-mt") model = PeftModel.from_pretrained(base_model, "texdata/Vran-27B-SLO-BioMed-Research-LoRA") - Notebooks
- Google Colab
- Kaggle
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Vran is an experimental RESEARCH model with NO medical intended purpose — not a medical device (EU MDR), not for diagnosis, treatment or clinical use. Research and non-commercial use only. You accept sole responsibility for regulatory/legal compliance (EU AI Act, GDPR).
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Vran-27B-SLO-BioMed-Research — LoRA adapter
The LoRA adapter (PEFT, rank 128 / alpha 256, lora_target: all) for
texdata/Vran-27B-SLO-BioMed-Research
— a Slovenian biomedical research language model with native tool-calling.
⛔ Not a medical device, not for clinical use. Research / non-commercial only.
Base model — apply to the right one
This adapter was trained on texdata/Qwen3.6-27B-slo-med-mt
(a Qwen3.6-27B SL-medical MT base), not plain Qwen3.6-27B. Applying it to a different
base will not reproduce Vran.
from peft import AutoPeftModelForCausalLM
model = AutoPeftModelForCausalLM.from_pretrained("texdata/Vran-27B-SLO-BioMed-Research-LoRA",
trust_remote_code=True) # pulls the base automatically
For a ready-to-use merged model use texdata/Vran-27B-SLO-BioMed-Research
(safetensors) or its GGUF build.
Training
Full SFT: ~161k examples (138k Slovenian medical + 19k tool-calls + identity + retention), 2 epochs. Eval (multi-judge): pravilnost 4.99 / popolnost 4.97. See the merged model card for details, disclaimers, and lineage.
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Model tree for texdata/Vran-27B-SLO-BioMed-Research-LoRA
Base model
Qwen/Qwen3.6-27B