---
license: apache-2.0
base_model: Shockem/Qwen3.8-27b-Terse-Coder
base_model_relation: quantized
tags:
  - reasoning
  - coding
  - qwen3
  - nvfp4
  - modelopt
---

# Qwen3.8-27B Terse-Coder — NVFP4

NVFP4 (modelopt W4A16) quantization of
[Shockem/Qwen3.8-27b-Terse-Coder](https://huggingface.co/Shockem/Qwen3.8-27b-Terse-Coder),
a fine-tune of [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B)
with **~1/10 the chain-of-thought reasoning tokens on coding tasks and
correctness preserved**. This is the tested deployment artifact — every
number below was measured on this checkpoint.

> **Actively researched and improving.** Expect updated quants on this page
> as the study continues.

## Results

Held-out 40 coding problems (20 HumanEval + 20 MBPP-sanitized, disjoint from
training), vLLM 0.28 on 2× RTX 5060 Ti 16 GB, MTP spec decode on, sampling
temp 0.6 / top_k 20 / top_p 0.95 / rep-penalty 1.05, pass@1 by automated
test execution:

| Model (all NVFP4) | pass@1 | Reasoning tokens / problem | Wall tok/s |
|---|---|---|---|
| nvidia/Qwen3.8-27B-NVFP4 (stock) | 72.5% | ~701 | 54.1 |
| **This model** | **67.5%** | **~38 (−95%)** | **54.5** |

Runs at stock-base wall speed with MTP acceptance 0.412 — the reasoning cut
is free end-to-end. **Independent benchmarks** (NVFP4 quant, vLLM 0.28, thinking on, house
sampling; reasoning = `completion_tokens_details.reasoning_tokens`):

| Benchmark | Score | Reasoning tokens (mean / median) |
|---|---|---|
| GSM8K (n=200) | **98.0%** | 84 / 72 |
| GPQA-Diamond (full 198) | **78.3%** | 1,485 / 969 |
| CRUXEval-I (full 800, input prediction) | **92.1%** | 197 / 83 |
| CRUXEval-O (full 800, output prediction) | **92.9%** | 146 / 96 |
| HumanEval+ (164, official EvalPlus, greedy) | **90.2%** (93.9% base) | 43 / 28 |
| MBPP+ (378, official EvalPlus, greedy) | **78.6%** (92.9% base) | 91 / 25 |

CRUXEval was run with the official Meta harness (direct prompts, official
extraction, exec-based scoring, temp 0.2) — code *understanding*
(input/output prediction), complementing the generation-side coding table
above.

A note on GPQA-Diamond: this is where a terseness fine-tune is *supposed*
to bleed — PhD-level science, far outside the coding training distribution,
where long deliberation is the whole game. Holding **78.3%** at ~1.5k mean
reasoning tokens (thinking models typically burn 10–20k here) means the
training cut the *deliberation budget*, not the *capability* — the model
still scales effort up on hard problems (median 969 → max 16k) instead of
answering blindly fast.

**Internal agentic harness** (30 tests across easy/medium/hard — instruction
following, coding, reasoning, compaction handoff, tool/JSON contracts —
×10 runs each, this checkpoint served by vLLM): **easy 100% (40/40),
medium 100% (90/90), hard 100% (140/140)**, zero truncations, zero
reasoning fallbacks. Prior best on the same harness was 100/100/98.7.

## Quantization recipe

This is a **v3-recipe** house quant, built to preserve the adapter effect
through 4-bit compression:

- modelopt 0.45 **W4A16** NVFP4, per-tensor streaming PTQ (the same
  400-tensor quantize set + ignore list as the published house Signal quants)
- **FP8 attention** (absmax — byte-matches NVIDIA's checkpoint at 97–99%)
- **Local-Hessian-weighted calibration on MLP + lm_head** (Hessian captured
  from 2048 house-traffic chunks; Hessian-weighted MSE scale solve with
  per-block e4m3 bracketing). This matters: an absmax-calibrated quant of the
  same weights attenuates the terse-reasoning effect to roughly half
  (−49.5% vs −92.4% cut measured). Geomean Hessian-weighted error ratio
  0.805 vs the absmax baseline on the stock base.
- **MTP draft stack included** (1 MTP layer, BF16, vocab-truncated
  40960-id draft head) so speculative decoding works out of the box.

## Serving (vLLM, tested path)

```bash
vllm serve Shockem/Qwen3.8-27b-Terse-Coder-NVFP4 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}' \
  --kv-cache-dtype fp8
```

**Turn MTP spec decode on** — outputs are target-verified (lossless) and
acceptance is 0.41. If you serve with spec decode, make sure the generation
config has **no `min_p`** — vLLM 0.28 rejects min_p under spec decode.

Recommended sampling (mirrors testing): temp 0.6, top_k 20, top_p 0.95,
repetition_penalty 1.05.

On 2×16 GB cards cap context at ~200k with a ~3.9 GiB FP8 KV pin;
single-card 24 GB+ rigs are unaffected.

## Notes

- **Do not stack the
  [Terse-Coder adapter](https://huggingface.co/Shockem/Qwen3.8-27b-Terse-Coder-LoRA)
  on this checkpoint** — the preference is already merged in; double
  application over-shortens reasoning (63% pass with `no_code` failures).
- The fp16 source weights are at
  [Shockem/Qwen3.8-27b-Terse-Coder](https://huggingface.co/Shockem/Qwen3.8-27b-Terse-Coder)
  if you want to quantize differently or merge further.
- Behavioral edit, not a knowledge edit — targeted at coding with thinking
  enabled. Should work on other backends (SGLang, TabbyAPI/EXL3), but only
  vLLM has been measured; validate before relying on them.

## Attributions & licenses

This checkpoint is a quantized derivative of
[Shockem/Qwen3.8-27b-Terse-Coder](https://huggingface.co/Shockem/Qwen3.8-27b-Terse-Coder),
itself a derivative of [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B),
© Qwen Team, Alibaba Cloud, licensed **Apache 2.0**; this checkpoint remains
Apache 2.0 and the original license and copyright notices are retained.
Credits:

- **Qwen Team (Alibaba Cloud)** — the Qwen3.8-27B base model (Apache 2.0).
- **NVIDIA** — [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer)
  0.45 (Apache 2.0) drove this NVFP4 quantization; NVIDIA's published
  [Qwen3.8-27B-NVFP4](https://huggingface.co/nvidia/Qwen3.8-27B-NVFP4)
  checkpoint informed the Hessian-calibrated recipe.
- **[agentionai](https://huggingface.co/agentionai/Signal-3.8-27B)** and
  **[p-e-w](https://github.com/p-e-w/heretic)** (Heretic) — Signal and a
  heretic-ara variant were two of the three trace-generation policies in the
  upstream adapter's preference data.
- **OpenAI** ([HumanEval](https://github.com/openai/human-eval), MIT) and
  **Google** ([MBPP](https://github.com/google-research/google-research/tree/master/mbpp),
  CC-BY 4.0) — prompt sources for training and held-out evaluation.
- **Hugging Face [TRL](https://github.com/huggingface/trl)** (Apache 2.0) —
  DPO training; **[Datacurve](https://huggingface.co/datasets/datacurve/deep-swe)**
  — DeepSWE, independent evaluation only.

None of these parties endorse this model; all remaining errors are ours.