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Shockem/froggeric-terse-coder
froggeric-terse-coder is a machine learning model from Shockem. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
The recommended chat template for the Terse-Coder family (also bundled inside each model repo as chattemplate.jinja).
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Updated Sep 23, 2026
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From the Hugging Face model README
The recommended chat template for the
Terse-Coder family
(also bundled inside each model repo as chat_template.jinja).
Lineage: derived from froggeric/Qwen-Fixed-Chat-Templates —
the house qwen3.8-froggeric-v5 template, tuned for terse-reasoning
models. It is what our benchmarks and the daily driver run — serving the model
without it (or with a scaffold that overrides the system prompt) changes
behavior, most noticeably around tool-call discipline:
reasoning_effort: high maps to the model's extended thinking tier;
default thinking is on.<tool_call><function=...>), parsed
natively by vLLM (qwen3_coder_mcp), SGLang, and TabbyAPI.<think> content is not replayed unless preserve_thinking
is set.vLLM:
vllm serve Shockem/Qwen3.8-27b-Terse-Coder-NVFP4 \
--chat-template chat_template.jinja \
--speculative-config '{"method":"mtp","num_speculative_tokens":3}'
llama.cpp:
llama-server -m Qwen3.8-27b-Terse-Coder.Q4_K_M.gguf \
--chat-template chat_template.jinja -ngl 99
SGLang:
sglang serve --model-path Shockem/Qwen3.8-27b-Terse-Coder-NVFP4 \
--chat-template chat_template.jinja
Note: the think-budget ladder (caps per reasoning_effort level) is applied
server-side in our deployment, not by the template itself — see the model
cards for the measured setup. Licensed Apache 2.0.