barozp/Qwen3.8-27B-Opus-Distill-v2
barozp/Qwen3.8-27B-Opus-Distill-v2 is a 27 billion parameter language model, fine-tuned from Qwen/Qwen3.8-27B, specifically optimized for enhanced reasoning capabilities through distillation from verified Claude Opus traces. This version addresses a critical reasoning-loop bug present in its predecessor, ensuring reliable output under complex constraint stacking. It maintains the base model's native vision-language and MTP capabilities while significantly improving performance on reasoning benchmarks like ARC Challenge and GPQA Diamond.
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Qwen3.8-27B-Opus-Distill-v2: Enhanced Reasoning with Bug Fixes
This model is a fine-tuned version of the Qwen/Qwen3.8-27B base model, a dense 27 billion parameter model with native vision-language and MTP capabilities. The primary goal of this iteration is to significantly improve reasoning quality while resolving a critical bug found in the previous version (v1).
Key Enhancements & Bug Fixes
- Reasoning Loop Fix: Addresses a specific bug where v1 would enter a non-converging self-verification loop when faced with stacked output-format constraints (e.g., "no prose" + "no markdown"), leading to zero visible output and token budget exhaustion. This fix was achieved by meticulously reconstructing the training data with 100% verified-genuine Claude Opus reasoning traces, removing fabricated or unverified content.
- Improved Reasoning Quality: Despite fixing the bug, the model retains and slightly refines the reasoning-quality gains. Benchmarks show notable improvements in
arc_challenge(acc_norm +0.034) andgpqa_diamond(acc_norm +0.237) compared to the base model. - Data Provenance: The training dataset,
barozp/opus-reasoning-distill-v2, consists of 11,716 examples, all traced to verified-genuine Opus sources, ensuring higher data integrity.
Performance Highlights
Benchmarking with lm-evaluation-harness (0-shot, loglikelihood, chat template OFF, QUICK mode) demonstrates:
arc_challenge:0.6220acc_norm (vs.0.5880base)gpqa_diamond:0.4697acc_norm (vs.0.2323base)- Language modeling (wikitext) and general knowledge (MMLU) remain largely unaffected.
Should I use this for my use case?
- Yes, if you require robust reasoning capabilities: Especially for tasks demanding complex logical deduction or adherence to multiple output constraints, where the previous version might have failed.
- Yes, if you need a reliable model for structured outputs: The fix for the reasoning-loop bug makes it more dependable for generating code, structured data, or other formats requiring strict adherence to instructions.
- Consider if vision-language or MTP are important: These capabilities are carried over from the base Qwen3.8-27B model, making it suitable for multimodal applications.