Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face
Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.
Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.
- Fine-tuned from Qwen3.8-27B for cheaper, faster agentic reasoning
- Two-stage pipeline: filtered SFT then NeMo-RL + GSPO reasoning RL
- Eval ensemble included Qwen3.7-Max, GLM-5, GPT-OSS-120B-High, Gemma4-27B
- Known issue: occasional wrong indentation in Python coding tasks
- MMLU-Pro mixed-set score lower than base model
- Vendors
- Hugging FaceNVIDIAUnsloth
- Products
- Qwopus3.8-27B-FlashQwen3.8-27B
From the original article
13 min read at huggingface.coimage-text-to-text · 113,295 downloads · 148 likes · license apache-2. 0
By Jackrong · huggingface.co
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.