English ⇄ Slovenian medical machine translation (MT = strojno prevajanje), merged full model. A dense Qwen3.6-27B fine-tune specialised for translating medical / clinical / scientific text while preserving doses, units, drug names and negation, and using the formal Slovenian medical register.

The dense 27B base already produces strong Slovenian morphology (its ~27B active params handle case/agreement better than a 3B-active MoE); this fine-tune adds formal medical terminology and cleans register/borrowing issues. MTP (multi-token-prediction) tensors are preserved for speculative decoding.

Intermediate research artifact: a domain translator used to build a Slovenian medical dataset. Not a medical assistant, not a clinical product.

⚠️ Disclaimer — read before use

NOT MEDICAL ADVICE. Released for research and educational purposes only. A translation tool, not a source of medical information, diagnosis or treatment.

No clinical use. Do not use this model or its output to diagnose, treat, or advise any person. It does not replace a clinician ("ne nadomešča zdravnika").
MT can be wrong in dangerous ways — it can mistranslate a dose, unit, drug name, or negation. Every translation must be verified by a qualified human before any real-world use.
Uncensored base. The base is an uncensored ("heretic") variant with safety alignment removed — intentional for translation fidelity (won't refuse/soften clinical content), but it means no safety guardrails. Do not deploy in any user-facing or generative role.
Hallucination & bias, as with any LLM. Output is not guaranteed faithful, complete, or accurate. Known weakness: spelled-out large numbers (e.g. "eighty-eight") are sometimes mistranslated (digit numbers like "88" / "500 mg" are reliable).
No warranty, no liability. Provided "as is"; the authors accept no liability for any loss or harm. Use entirely at your own risk and comply with all applicable laws (medical-device, data-protection, etc.). Not a medical device; not evaluated by any regulator (FDA/EMA/…).

By downloading or using this model you accept the above.

Model details

Type: merged full model (bf16), dense. LoRA merged into the base + MTP tensors grafted back (export drops them).
Base: llmfan46/Qwen3.6-27B-uncensored-heretic-v2-Native-MTP-Preserved (dense Qwen3.6-27B, uncensored, hybrid linear-attention, vision-capable, MTP).
Task: EN↔SL translation of medical / clinical / scientific text.
Adapter (before merge): QLoRA rank 128 / alpha 256, lora_target: all, 1 full epoch (6191 steps). Also published separately as Qwen3.6-27B-slo-med-mt-LoRA.
Chat template: qwen3_5. MTP: 15 mtp.* tensors preserved (bf16).

Intended use

Research: building/curating Slovenian medical parallel data; batch-translating an English medical corpus for later human review. Out of scope: clinical decision support, patient-facing chat, generating medical advice, unsupervised/production translation without human review.

Training data

Public / permissive EN–SL parallel corpora from OPUS, quality-filtered (boilerplate / length-ratio / dedup). ~1.0M sentence pairs (both directions), medical ≈ 57%:

corpus	~pairs (cap)	domain	source / license
EMEA	242k	medical (drug leaflets / EPARs)	Public — European Medicines Agency · OPUS-EMEA
ELRC health sets (vaccination, EU-medi, wiki-health, antibiotic, COVID)	44k	medical	CC-BY-4.0 · ELRC-SHARE / OPUS
ECDC	2k	public health	Public — ECDC · OPUS-ECDC
ELRC-SciPar	120k	scientific (theses/abstracts)	CC-BY-4.0 · ELRC / OPUS
Europarl	100k	general (fluency regularizer)	Public — European Parliament · OPUS-Europarl

Attribute the above. No proprietary, scraped, or patient data. SciELO / UFAL-Medical have no en-sl pairs; this exhausts the medical en-sl bitext on OPUS.

Training procedure

QLoRA (4-bit bnb NF4) + Liger, LLaMA-Factory, template: qwen3_5, neat_packing: false, cutoff_len 1024. lr 2e-5 cosine, warmup 0.03, effective batch 16, 1 full epoch (6191 steps), bf16, gradient checkpointing. Dense Qwen3.6-27B has no broken-bf16 forward and no MoE mask crash, so training/eval are straightforward.

Evaluation

FLORES-200 devtest (en→sl, 1012 sentences, beam=5, sacreBLEU) — a clean, general-domain benchmark the model never saw:

decoding	model	BLEU	chrF
beam=5	Qwen3.6-27B-slo-med-mt (this)	29.47	57.62
beam=5	base dense 27B (no fine-tune)	29.36	57.96
greedy	Qwen3.6-27B-slo-med-mt (this)	27.36	55.62
greedy	base dense 27B (no fine-tune)	28.03	56.90

On general-domain FLORES the fine-tune is ≈ the base — within noise at beam=5 (BLEU +0.11) and marginally below at greedy (BLEU −0.67): the medical fine-tune neither meaningfully helps nor hurts general translation. Its value is medical-domain terminology, not general MT. Beam=5 gains ~2 BLEU over greedy for both models; GGUF/llama.cpp typically decodes greedy.

Qualitative (100 clinical sentences, en→sl): terminology aligned to the formal medical register (odmerek not doza, pediatrični not otroška), Croatian-ish borrowings cleaned (Jedno→eno, Sumnjivo→Sumljivo, Higiene→Higiena), doses / units (°C, mg) and negation preserved. Residual weakness: spelled-out large numbers (digit numbers are reliable).

Recommended settings (MT)

This is a reasoning-capable base — for translation you want the reasoning trace off and near-deterministic decoding:

setting	value	why
thinking / reasoning	OFF — `enable_thinking=False`, or put `<	think_off
temperature	0 (greedy) — or ≤ 0.3	faithful, reproducible output; no creative drift
top_p / top_k	0.9 / 20 (only if sampling)	—
repetition_penalty	1.05	avoids loops without hurting fidelity
num_beams	5 (transformers)	best Slovenian morphology; GGUF/llama.cpp → use greedy
max_new_tokens	256–512	translations are short; cap to avoid rambling

Prompt (exact): Translate the following English medical text into Slovenian. Output only the translation:\n\n{src} (swap the languages for sl→en). Send it as the user message; no custom system prompt needed.

How to use

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

repo = "<your-org>/Qwen3.6-27B-slo-med-mt"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                         bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)
model = AutoModelForCausalLM.from_pretrained(repo, quantization_config=bnb,
                                             device_map="auto", trust_remote_code=True).eval()

prompt = "Translate the following English medical text into Slovenian. Output only the translation:\n\n{src}"
msg = [{"role": "user", "content": prompt.format(src="Store the vaccine at 2-8 °C and do not freeze.")}]
enc = tok.apply_chat_template(msg, add_generation_prompt=True, return_tensors="pt",
                              return_dict=True, enable_thinking=False)
enc = {k: v.to(model.device) for k, v in enc.items()}
out = model.generate(**enc, max_new_tokens=256, num_beams=5, repetition_penalty=1.05)
print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))

Reverse direction: swap the instruction to "Translate the following Slovenian medical text into English." Use num_beams=5 for best Slovenian grammar. A GGUF build (-GGUF) preserves MTP for speculative decoding in llama.cpp.

License

Derivative of the Qwen base — released under the Qwen license; comply with it and with the training-corpus licenses/terms above. Verify the base model's terms permit your use before redistributing.

Acknowledgements

Qwen team (Qwen3.6-27B) and the llmfan46 uncensored/MTP-preserved dense variant; data from EMA, ELRC / ELRC-SHARE, ECDC, the European Parliament, and OPUS (Tiedemann, 2012).

Citation

Cite this work (Tadej Fius, MediaAtlas Ltd):

bibtex

@misc{fius2026qwen27bmt,
  title        = {Qwen3.6-27B Slovenian Medical Machine Translation (merged)},
  author       = {Fius, Tadej},
  year         = {2026},
  publisher    = {MediaAtlas Ltd},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/texdata/Qwen3.6-27B-slo-med-mt}
}

Upstream / source citations:

If you use this model, please cite the base model, the parallel corpora, and the evaluation resources:

bibtex

@misc{qwen3,
  title  = {Qwen3 Technical Report},
  author = {{Qwen Team}},
  year   = {2025},
  url    = {https://huggingface.co/Qwen}
}
@inproceedings{tiedemann2012opus,
  title     = {Parallel Data, Tools and Interfaces in {OPUS}},
  author    = {Tiedemann, J{\"o}rg},
  booktitle = {Proc. of LREC},
  year      = {2012}
}
@article{nllbflores2022,
  title   = {No Language Left Behind: Scaling Human-Centered Machine Translation},
  author  = {{NLLB Team}},
  journal = {arXiv:2207.04672},
  year    = {2022}
}
@inproceedings{post2018sacrebleu,
  title     = {A Call for Clarity in Reporting {BLEU} Scores},
  author    = {Post, Matt},
  booktitle = {Proc. of WMT},
  year      = {2018}
}

Corpora: EMEA, ELRC-SHARE health sets, ECDC, ELRC-SciPar, and Europarl, distributed via OPUS. Evaluation on FLORES-200 with sacreBLEU.