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ayoubkirouane
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Model Details
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ayoubkirouane
Model Tree
Qwen/Qwen3.6-35B-A3B
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Output Modalities
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The instructed el→EN override reached 53.9%, up from 44.8% but short of the pre-registered 60% trainability target: improved, not solved. Not evaluated for safety-critical, legal, or medical use.
Arrows mark the desirable direction for every metric: ↑ higher is better, ↓ lower is better.
Every movement above was adjudicated against a random-reward control arm trained on the same data, steps and seed: the control reproduced the v1 baseline on every axis (fallback 22.1%, leak 3.61%, override 44.1%), so none of the movements are elicitation artifacts. The arm trained with no language protection ended at 98.22% fidelity, above its starting point: the Greek reasoning habit survived an accuracy-only gradient.
Accuracy is shown for completeness only: +3.3 is inside the 7.7 pp seed floor and is claimed by nobody. The forgetting check (Titan-1 suite, 14 bilingual NLU benchmarks) was not re-run for v1.1: the SFT release it refines measured −0.01 Greek / +0.08 English against its own base, statistically flat, i.e. no forgetting, and the RLVR round's own gates held fidelity and both language directions throughout training.
Sophea-Qwen3.6-v1.1 is a reinforcement-learning refinement of Sophea-Qwen3.6-v1 using verifiable rewards, pre-registered before training and adjudicated against a random-reward control. Full method, defenses and decision rules are in the paper.
Stated honestly: the pre-registered override target of 60% was missed; 53.9% was reached, so the paper words the steerability result as reward-responsive, not trainable.
Serve with vLLM (OpenAI-compatible; the fused-expert LoRA is already merged into dense weights):
bash
vllm serve KIEFERSA/Sophea-Qwen3.6-v1.1 --served-model-name sophea-qwen3.6-v1_1 --reasoning-parser qwen3
Client (OpenAI SDK), thinking left ON:
python
from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")resp = client.chat.completions.create(model="sophea-qwen3.6-v1_1",messages=[{"role": "user", "content": "Ένα κατάστημα πουλά στυλό προς 1,50 € το ένα. Πόσο κοστίζουν 12 στυλό;"}],temperature=0.7,extra_body={"chat_template_kwargs": {"enable_thinking": True}}, # thinking model)print(resp.choices[0].message.reasoning_content) # the trace, in the question's languageprint(resp.choices[0].message.content) # the final answer
Transformers:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("KIEFERSA/Sophea-Qwen3.6-v1.1")model = AutoModelForCausalLM.from_pretrained("KIEFERSA/Sophea-Qwen3.6-v1.1", torch_dtype=torch.bfloat16, device_map="auto")messages = [{"role": "user", "content": "Ένα τρένο διανύει 240 χλμ σε 3 ώρες. Ποια είναι η μέση ταχύτητά του;"}]text = tok.apply_chat_template(messages, tokenize=False,add_generation_prompt=True, enable_thinking=True)inputs = tok(text, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=2048, temperature=0.7, do_sample=True)print(tok.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=False))
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 steering suffix on 53.9% of Greek items and the Greek suffix on 85.7% of English items.
This model ships the multi-token-prediction head: 19 mtp.* tensors (~1.7 GB, bf16) in
model-mtp.safetensors, the single-layer draft stack that config.json declares through
mtp_num_hidden_layers: 1. The head is the base model's own; speculative decoding verifies every
drafted token against the main model, so it changes throughput only, never the output
distribution.
Enable it with vLLM:
bash
vllm serve KIEFERSA/Sophea-Qwen3.6-v1.1 --served-model-name sophea-qwen3.6-v1.1 --trust-remote-code \--speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":2}'
The full vision stack of the multimodal base is included, byte-identical to the base: image inputs work exactly as on Qwen3.6-35B-A3B. The adaptation touched only the language decoder; vision performance has not been separately re-evaluated for this release.
Apache-2.0. The base model, Qwen/Qwen3.6-35B-A3B, is released under Apache-2.0 and this fine-tune inherits those terms. Verify base-model terms before use.
This model is a release artifact of arXiv:2608.17744. If you use it, please cite:
bibtex
@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},}