Hugging Face trending modelspublished Sep 8, 13:35 UTC (7d ago)ingested Sep 14, 23:57 UTCukisai

ukisai/Swift-Qwen3.8-27b — new model trending #30 on Hugging Face

AI summary · glm-5.3

UkisAI releases Swift-Qwen3.8-27B, a Qwen3.8-27B derivative using 58.3% fewer thinking tokens with <1% performance loss and ~1.95x speed-up.

UkisAI released Swift-Qwen3.8-27B, a reasoning-efficient derivative of Qwen3.8-27B that cuts thinking-token usage by 58.3% while staying within 1% of base performance, yielding a 1.95x speed-up on several tasks. The model was fine-tuned by penalizing reasoning-marker tokens that trigger overthinking, plus a transfer component from BottleCap AI's ThinkingCap-Qwen3.6-27B. Benchmarks include GPQA-Diamond 88.28% (base 88.38%), MMLU-Pro 84.95% (base 85.47%), and AIME 2026 94.00% (base 98.67%), with mean-token reductions of roughly 27-46% across tests. GGUF weights are available on Hugging Face alongside enterprise licensing options.

Fine-tune reduces thinking tokens 58.3% with under 1% performance loss
Training penalizes reasoning-marker tokens that trigger overthinking
Includes transfer component from BottleCap AI's ThinkingCap-Qwen3.6-27B
Mean-token reductions of 27-46% across GPQA-Diamond, MMLU-Pro, AIME, and more

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Tagsqwen ukisai reasoning-efficiency fine-tune open-weights gguf llm inference-optimization

Full article1,295 words · extracted from huggingface.co · click to collapse

<a href="https://ukisai.com"><strong>Website</strong></a>  • 

<a href="https://ukisai.com/products/swift"><strong>Learn more</strong></a>  • 

<a href="https://huggingface.co/ukisai/Swift-Qwen3.8-27B-GGUF"><strong>GGUF</strong></a>  • 

<a href="#license-and-access"><strong>Enterprise licensing</strong></a>

</div>

# Swift-Qwen3.8-27B

Swift-Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B,

using **58.3% fewer thinking tokens** while maintaining near-identical performance

(**<1% loss**) and as a result getting a **x1.95 speed-up** on several tasks.

<p align="center" style="font-size:13px;color:#8C94A8;margin:-0.6em 0 1.4em;">The prompt is a sample from LiveCodeBench v6</p>

## Training approach

We built Swift by identifying reasoning-marker tokens that, in our analysis, trigger overthinking in Qwen’s

reasoning rollouts. We then fine-tuned Qwen by penalizing usage of those tokens while it reasons.

Swift produces shorter reasoning traces. In our testing, we also observe fewer overthinking errors.

For maximum gains, Swift also includes a transfer component derived from

[BottleCap AI's ThinkingCap-Qwen3.6-27B](https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B).

## Evaluation scope

> All results below compare the Qwen3.8-27B BF16 base with the same base plus the

> Swift adapter.

## Benchmarks

<style>

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.swift-table th { padding:13px 8px; text-align:center; font-weight:700; color:#AEB5C7; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; }

.swift-table td { padding:14px 8px; text-align:center; color:#BFBDBD; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; vertical-align:middle; overflow-wrap:break-word; }

.swift-table tr > :last-child { border-right:0; }

.swift-table tbody tr:last-child td { border-bottom:0; }

.swift-table .benchmark-heading { color:#B7BDCD; background:#0D111B; border-bottom:3px solid #7D45B5; }

.swift-table .score-heading { color:#F0C5FF; background:#52239E; border-bottom:3px solid #7D45B5; }

.swift-table .tokens-heading, .swift-table .median-heading { color:#D4E8FF; background:#304FC2; border-bottom:3px solid #5687E6; }

.swift-table .benchmark { padding-left:18px; text-align:left; color:#FFFFFF; font-weight:600; }

.swift-table strong { color:#FFFFFF; }

.swift-table .section { padding:12px 18px; text-align:left; color:#B489FF; background:#2A2541; font-weight:700; letter-spacing:.08em; text-transform:uppercase; border-top:1px solid #3A3159; border-bottom:1px solid #3A3159; }

.swift-table .swift { background:#171127; }

.swift-table thead tr:nth-child(2) .swift { color:#D3A0FF; }

.swift-table .reduction { color:#69BFFF; background:#101B2C; font-weight:700; }

.swift-table .detail { color:#8C94A8; font-size:12px; font-weight:500; }

@media (max-width: 640px) {

.swift-table { display:block !important; width:100% !important; max-width:100%; overflow-x:auto !important; -webkit-overflow-scrolling:touch; table-layout:auto !important; }

.swift-table th, .swift-table td { min-width:100px; }

.swift-table th:first-child, .swift-table td:first-child { min-width:160px; }

}

</style>

<thead>

<tr>

<th rowspan="2" class="benchmark-heading" style="width:32%;text-align:left;padding-left:18px;vertical-align:bottom;">Benchmark</th>

<th colspan="2" class="score-heading">Score</th>

<th colspan="3" class="tokens-heading">Mean tokens</th>

<th class="median-heading" style="width:14%;">Median tokens</th>

</tr>

<tr>

<th class="swift">Swift</th>

<th class="swift">Swift</th>

<th class="reduction">Reduction</th>

</tr>

</thead>

<tbody>

<tr><td class="section" colspan="7">General reasoning</td></tr>

<tr><td class="benchmark">GPQA-Diamond</td><td>88.38%</td><td class="swift">88.28%</td><td>15,014</td><td class="swift"><strong>8,855</strong></td><td class="reduction">↓ 41.0%</td><td class="reduction">↓ 58.3%</td></tr>

<tr><td class="benchmark">IFBench</td><td>73.53%</td><td class="swift">71.80%</td><td>8,052</td><td class="swift"><strong>4,657</strong></td><td class="reduction">↓ 42.2%</td><td class="reduction">↓ 50.5%</td></tr>

<tr><td class="section" colspan="7">Mathematics</td></tr>

<tr><td class="section" colspan="7">Multimodal</td></tr>

<tr><td class="section" colspan="7">Agentic coding</td></tr>

<tr><td class="benchmark">Terminal-Bench 2.1</td><td>66.74%</td><td class="swift">65.84%</td><td>37,086</td><td class="swift"><strong>27,272</strong></td><td class="reduction">↓ 26.5%</td><td class="reduction">↓ 38.7%</td></tr>

<tr><td class="benchmark">LiveCodeBench v6</td><td>76.76%</td><td class="swift">81.55%</td><td>11,374</td><td class="swift"><strong>8,615</strong></td><td class="reduction">↓ 24.3%</td><td class="reduction">↓ 45.8%</td></tr>

</tbody>

</table>

<summary><strong>How to reproduce</strong></summary>

<p style="font-size:13px;line-height:1.5;margin:8px 0;"><strong>Serving:</strong> BF16 · vLLM 0.27.1 · Qwen3 parser · context 262,144 · thinking xhigh.<br>

<strong>Sampling:</strong> temperature 1.0 · top_p 0.95 · top_k 20 · min_p 0 · presence_penalty 0 · repetition_penalty 1.<br>

<strong>Benchmarks:</strong> averages over five seeds (0–4) per model; five trials per task for Terminal-Bench.</p>

<thead><tr><th style="padding:4px 8px;text-align:left;">Benchmark</th><th style="padding:4px 8px;text-align:right;">Output cap</th></tr></thead>

<tbody>

<tr><td style="padding:3px 8px;">GPQA-Diamond</td><td style="padding:3px 8px;text-align:right;">100,000</td></tr>

<tr><td style="padding:3px 8px;">IFBench</td><td style="padding:3px 8px;text-align:right;">81,920</td></tr>

<tr><td style="padding:3px 8px;">Terminal-Bench 2.1</td><td style="padding:3px 8px;text-align:right;">Agent/task limits</td></tr>

<tr><td style="padding:3px 8px;">LiveCodeBench v6</td><td style="padding:3px 8px;text-align:right;">32,768</td></tr>

</tbody>

</table>

</details>

## Efficiency across and versus reasoning efforts

Qwen3.8's `reasoning_effort` setting lets users choose how much the model thinks.

For Swift to be useful across these settings, it needs to reduce thinking while

keeping accuracy close to the base. We therefore tested `xhigh`, `medium`, and `low`:

thinking-token savings persist at every level.

<thead>

<tr>

<th class="benchmark-heading" style="width:50%;text-align:left;padding-left:18px;white-space:normal;">Reasoning effort</th>

<th class="tokens-heading" style="width:50%;white-space:normal;">Mean thinking reduction</th>

</tr>

</thead>

<tbody>

<tr><td class="benchmark">Xhigh</td><td class="reduction">↓ 41.0%</td></tr>

<tr><td class="benchmark">Medium</td><td class="reduction">↓ 22.7%</td></tr>

</tbody>

</table>

The efficiency also holds up against the base's own lower effort settings. On

GPQA-Diamond (198 questions, 5 seeds, 990 paired calls), Swift at `xhigh` is

Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/ukisai/Swift-Qwen3.8-27b