📄 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 theanalysischannel and the answer inassistantfinal; 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. Adetailed thinking offsystem message does not disable reasoning (it returnscontent=nullwith the text inreasoning).
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.
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},
}