Hugging Face trending modelspublished Sep 11, 14:58 UTC (4d ago)ingested Sep 16, 09:09 UTCukisai

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

AI summary · glm-5.3-flash

UkisAI released Swift-Qwen3.8-27B GGUF, a Qwen3.8-27B derivative cutting thinking tokens by 58.3% with under 1% performance loss and roughly 1.95x speedup.

UkisAI released Swift-Qwen3.8-27B as GGUF on Hugging Face, a reasoning-efficient derivative of Qwen3.8-27B using a Swift adapter that reduces median thinking tokens by up to 58.3% while keeping performance losses under 1% and delivering a 1.95x speed-up on several tasks. Reported benchmarks include GPQA-Diamond 88.28%, MMLU-Pro 84.95%, C-Eval 90.62%, AIME 2026 94.00% and Terminal-Bench 2.1 65.84%. The model is trending at #30 on Hugging Face, with BF16 weights and enterprise licensing also available.

Cuts median thinking tokens by 58.3% on GPQA-Diamond versus the Qwen3.8-27B base with only a 0.10-point score drop.
Delivers a 1.95x speed-up on several tasks while maintaining near-identical performance.
Benchmarks include GPQA-Diamond 88.28%, MMLU-Pro 84.95%, AIME 2026 94.00% and Terminal-Bench 2.1 65.84%.
Released as GGUF quantization on Hugging Face, trending at #30; BF16 weights and enterprise licensing also offered.

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VendorsUkisAI

ProductsSwift-Qwen3.8-27B

OrganizationsHugging Face

AI modelsSwift-Qwen3.8-27B Qwen3.8-27B

Tagsqwen3.8 ukisai swift gguf reasoning-efficiency open-weights quantization hugging-face

Full article1,386 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"><strong>BF16 model</strong></a>  • 

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

</div>

# Swift-Qwen3.8-27B GGUF

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>

<style>

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## Evaluation scope

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

> Swift adapter.

## Benchmarks

<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>

## Quantized evaluations

Quantized deployment is the intended use for Swift: lower-memory weights paired with

shorter reasoning. The INT4 evaluations below come from the

[source model card](https://huggingface.co/ukisai/Swift-Qwen3.8-27b) and were run on

W4A16 and AWQ checkpoints, not on this F16 GGUF. They retain token savings across GPQA,

IFBench, and AIME. On AIME, Swift matches or improves accuracy and reduces output-cap

failures by **31–33%**.

<th class="benchmark-heading" style="width:32%;text-align:left;padding-left:18px;white-space:normal;">Benchmark / quantization</th>

<th class="score-heading" style="width:16%;white-space:normal;">Base accuracy</th>

<th class="score-heading" style="width:16%;white-space:normal;">Swift accuracy</th>

<th class="tokens-heading" style="width:18%;white-space:normal;">Mean token reduction</th>

<th class="median-heading" style="width:18%;white-space:normal;">Median token reduction</th>

</tr></thead>

<tbody>

<tr><td class="benchmark">GPQA-Diamond<br><span class="detail">Mixed-precision quant W4A16 · thinking tokens</span></td><td>88.69%</td><td class="swift">88.38%</td><td class="reduction">↓ 32.1%</td><td class="reduction">↓ 50.2%</td></tr>

<tr><td class="benchmark">IFBench<br><span class="detail">Mixed-precision quant W4A16 · completion tokens</span></td><td>72.58%</td><td class="swift">71.25%</td><td class="reduction">↓ 30.1%</td><td class="reduction">↓ 38.0%</td></tr>

<tr><td class="benchmark">AIME 2026<br><span class="detail">Mixed-precision quant W4A16 · completion tokens</span></td><td>84.00%</td><td class="swift">84.00%</td><td class="reduction">↓ 19.0%</td><td class="reduction">↓ 37.5%</td></tr>

<tr><td class="benchmark">AIME 2026<br><span class="detail">AWQ INT4 · completion tokens</span></td><td>82.67%</td><td class="swift">84.00%</td><td class="reduction">↓ 22.8%</td><td class="reduction">↓ 34.8%</td></tr>

</tbody>

</table>

<summary><strong>Quantized evaluation settings</strong></summary>

Each row compares the same quantized base with and without the Swift adapter.

GPQA and AIME use five seeds; IFBench uses four samples per prompt and strict scoring.

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