Shockem/Qwen3.8-27b-Terse-Coder
Shockem/Qwen3.8-27b-Terse-Coder is a 27 billion parameter model, fine-tuned from Qwen/Qwen3.8-27B, specifically optimized to reduce verbose chain-of-thought reasoning in coding tasks while preserving correctness. Developed by Shockem, this model significantly cuts down on reasoning tokens, making it more efficient for local deployments on consumer-grade hardware. It excels in code generation and related tasks by delivering concise yet accurate outputs, addressing the challenge of overly verbose reasoning in its base model.
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What is Shockem/Qwen3.8-27b-Terse-Coder?
This model is a 27 billion parameter full fine-tune of the Qwen/Qwen3.8-27B base model, developed by Shockem. Its primary innovation lies in its ability to drastically reduce the verbosity of chain-of-thought reasoning during coding tasks, achieving approximately 1/10th the reasoning tokens compared to the base model, without compromising correctness. This optimization makes it particularly suitable for local deployments where long reasoning chains can lead to significant delays.
Key Capabilities & Features
- Reduced Reasoning Verbosity: Achieves a 95% reduction in reasoning tokens against the NVIDIA quant and 52% against Signal-3.8-27B, making outputs faster and more efficient.
- Coding Task Excellence: Maintains high performance on coding benchmarks like HumanEval+ (91.5%) and MBPP+ (79.4%), demonstrating its capability in generating correct code.
- Adaptive Deliberation: Despite terseness, the model can still scale deliberation for complex problems, as shown by its 79.8% score on GPQA-Diamond, indicating it cuts waste, not capability.
- Optimized for Deployment: The merged model offers a straightforward deployment path, with the NVFP4 quant being the tested and recommended option for vLLM serving.
- Speculative Decoding Support: Designed to work efficiently with MTP speculative decoding, preserving decode throughput and acceptance rates.
Should I use this for my use case?
- Good for: Developers seeking a highly efficient coding assistant that provides concise, accurate code and reasoning. Ideal for environments with consumer-grade hardware where minimizing token generation time is crucial. Excellent for tasks requiring code generation, debugging, and related programming challenges.
- Consider alternatives if: Your primary use case involves general chat, complex mathematical derivations, or tasks where extensive, verbose reasoning is explicitly desired and not a bottleneck. While it performs well on some non-coding benchmarks, its core optimization is for coding tasks.