Sophea-OSS-v1

ayoubkirouane/Sophea-OSS-v1

Sophea-OSS-v1 is a text generation model from ayoubkirouane. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.

<div style="display:flex;align-items:center;gap:18px;padding:16px 20px;margin-bottom:16px;border-radius:14px;background:linear-gradient(100deg,e8f5ec 0%,f7fbf8 62%);border:1px solid c6e7d1"<img alt="KIEFERSA" style="f…

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.safetensors41.8 GB · 100%

At a glance

Task: Text Generation
Library: transformers
License: apache-2.0
Model type: gpt_oss
Access: Public
Created: Sep 16, 2026
Updated: Sep 16, 2026
SHA: fde6bf8f

Base models

openai/gpt-oss-20b

Model card

From the Hugging Face model README

<div style="display:flex;align-items:center;gap:18px;padding:16px 20px;margin-bottom:16px;border-radius:14px;background:linear-gradient(100deg,#e8f5ec 0%,#f7fbf8 62%);border:1px solid #c6e7d1"><img alt="KIEFERSA" style="flex:0 0 auto;height:58px;width:58px;border-radius:12px;object-fit:contain" 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style="font-size:1.24rem;font-weight:700;color:#003713;line-height:1.2">Sophea-OSS-v1</div><div style="font-size:.86rem;color:#3a5a47;margin-top:2px">Greek and English reasoning fine-tune of gpt-oss-20b: harmony-channel traces, format-repaired</div></div></div>

📄 Released with the paper Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot See (arXiv:2608.17744). Every number on this card is measured, contextualized, and caveated in the paper; read it for the full evaluation protocol.

Creator: Kiefer SA (Sophea AI Lab, Athens)
Base model: openai/gpt-oss-20b (sparse MoE transformer, 20.9B total / 3.60B active, 4-of-32 routing, MXFP4-packed experts)
Languages: Greek and English (the reasoning trace follows the question's language)
Decoding: thinking, always on: the base opens its analysis channel on 100% of rows even at reasoning_effort=none, and this fine-tune does not add a switch the base never had

Harmony channels, not <think>. The trace arrives in the analysis channel and the answer in assistantfinal; a scorer or client written for <think> carriers silently mis-reads this family. Split on the channel markers whenever they appear, and read the final answer from the final channel only. A detailed thinking off system message does not disable reasoning (it returns content=null with the text in reasoning).

Intended use

Greek and English reasoning assistant with the study's best instructed trace-language steerability (62.5% el→EN, the highest of the five releases; 93.3% en→EL)
Deployments that read the trace: it is on-language at 98.1% and never switches mid-question

Two family costs to weigh: the answer-channel leak is 10.3% (the trace-register boundary holds at different tightness per family; Qwen sits at 3.5%, Nano at 0.0%), and serving is more expensive than the base (see the budget note below). Not evaluated for safety-critical, legal, or medical use.

Fine-tuned from gpt-oss-20b, then format-repaired; the pre-repair arm is reported as reference only.

Evaluation

Arrows mark the desirable direction for every metric: ↑ higher is better, ↓ lower is better.

Headline scorecard

Budget, disclosed honestly: the token saving changes sign on this family. The release spends 1.6× more tokens than its English-reasoning base (median 640 vs 396): its traces are already slightly longer in words than the terse base's, and Greek's 2.3–2.5× token fertility widens the difference. The word-level shortening the recipe buys elsewhere does not survive translation into tokens here.

The format story: before repair, the Greek-lane anchored accuracy read 56.2 with 70.2% of rows never emitting the requested answer line: a format floor, not a reasoning one (the traces were present and on-language). The repair dose collapses fallback 70% → 26% and recovers 4.1 of the 7.3 Greek NLU macro points the pre-repair arm had lost; English retention recovers to +1.0 above base.

Per-benchmark detail

Per-domain, Greek think lane

The pre-repair fallback concentrated in the open-form domains (83.5% commonsense, 77.4% logic) while math's short numeric answer stayed format-robust (34.9%): the shape a format defect takes, not a capability loss. Conditioning on rows that emit the requested answer line, the release reads 94.4 / 58.2 / 30.0 (math / commonsense / logic) against raw 78.6 / 53.3 / 26.5.

Forgetting check: Titan-1 suite (non-reasoning mode)

This suite exists to answer one question: did the Greek fine-tune damage general ability? Against its own base, across 14 bilingual NLU benchmarks: −3.2 Greek / +1.0 English. English is fully retained (recovered above base by the format repair), and the paper localises the Greek residual to answer-format compliance rather than lost knowledge. Read the numbers below as a forgetting check, not a capability leaderboard.

<div style="overflow-x:auto"> <table style="width:100%;border-collapse:collapse;font-size:13px;font-variant-numeric:tabular-nums;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif"> <thead><tr> <th style="padding:10px 8px;text-align:left;font-weight:500;border-bottom:2px solid #0a8043;color:#0a8043;font-size:14px;">Benchmark (Titan-1 suite)</th> <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #0a8043;color:#0a8043;font-size:14px;">Base</th> <th style="padding:10px 8px;text-align:center;font-weight:500;border-bottom:2px solid #0a8043;color:#0a8043;font-size:14px;">Sophea-OSS-v1</th> </tr></thead><tbody> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">ARC-Challenge-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">77.1</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">68.6</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">ARC-Easy-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">85.4</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">79.9</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">Belebele-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">86.0</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">81.3</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">GreekMMLU</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">66.3</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">61.3</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">HellaSwag-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">42.9</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">52.1</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">Medical-MCQA-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">42.1</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">38.7</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">TruthfulQA-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">39.7</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">35.5</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">Winogrande-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">55.4</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">59.6</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">MMLU-el</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">55.5</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">45.4</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);"><em>Greek macro</em></td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);"><em>61.2</em></td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;"><em>58.0</em></td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">ARC-Challenge (en)</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">90.0</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">86.8</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">ARC-Easy (en)</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">96.0</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">95.7</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">HellaSwag (en)</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">57.0</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">68.4</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">Winogrande (en)</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">64.6</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">66.7</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);">MMLU (en)</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);">70.8</td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;">65.8</td></tr> <tr><td style="padding:7px 8px;padding-left:20px;text-align:left;border-bottom:1px solid rgba(128,128,128,.15);"><em>English macro</em></td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);"><em>75.7</em></td><td style="padding:7px 8px;text-align:center;border-bottom:1px solid rgba(128,128,128,.15);font-weight:700;"><em>76.7</em></td></tr> </tbody></table></div>

Register control is flat at 39/46; grammaticality improves 32 → 40 of 58 (judge-scored counts, directional evidence only).

Usage

Serve with vLLM (OpenAI-compatible):

vllm serve KIEFERSA/Sophea-OSS-v1 --served-model-name sophea-oss-v1

Client (OpenAI SDK):

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
    model="sophea-oss-v1",
    messages=[{"role": "user", "content": "Ένα κατάστημα πουλά στυλό προς 1,50 € το ένα. Πόσο κοστίζουν 12 στυλό;"}],
    temperature=0.7,
)
print(resp.choices[0].message.reasoning)  # analysis channel: the trace, in the question's language
print(resp.choices[0].message.content)    # assistantfinal channel: the answer

Transformers: load with attn_implementation="eager" (this family rejects sdpa) and apply the harmony chat template via tok.apply_chat_template(...) as usual; reasoning cannot be switched off.

Steering the trace language (partial, measured per checkpoint): append "Think step by step IN ENGLISH." to a Greek question, or "Σκέψου βήμα προς βήμα ΣΤΑ ΕΛΛΗΝΙΚΑ." to an English one. This checkpoint follows the English suffix on 62.5% of Greek items (the best of the five releases) and the Greek suffix on 93.3% of English items.

License

Apache-2.0. The base model, openai/gpt-oss-20b, is released under Apache-2.0 and this fine-tune inherits those terms. Verify base-model terms before use.

Citation

This model is a release artifact of arXiv:2608.17744. If you use it, please cite:

@misc{kirouane2026thinking,
      title={Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot See},
      author={Ayoub Kirouane and Christos Petrocheilos},
      year={2026},
      eprint={2608.17744},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2608.17744},
}

Task: Text Generation
Library: transformers
Type: gpt_oss
License: apache-2.0
Languages: el, en
Created: Sep 16, 2026
Updated: Sep 16, 2026