ukisai/Swift-Qwen3.8-27b · Hugging Face

Source: https://huggingface.co/ukisai/Swift-Qwen3.8-27b

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#swift-qwen38-27bSwift-Qwen3.8-27B

Swift-Qwen3.8-27B is UkisAI’s reasoning-efficient derivative of Qwen3.8-27B, using58.3% fewer thinking tokenswhile maintaining near-identical performance (<1% loss) and as a result getting ax1.95 speed-upon several tasks.

The prompt is a sample from LiveCodeBench v6

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#training-approachTraining 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 fromBottleCap AI’s ThinkingCap-Qwen3.6-27B.

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#evaluation-scopeEvaluation scope

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

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#benchmarksBenchmarks

BenchmarkScoreMean tokensMedian tokensBaseSwiftBaseSwiftReductionReductionGeneral reasoningGPQA-Diamond88.38%88.28%15,0148,855↓ 41.0%↓ 58.3%MMLU-Pro85.47%84.95%2,9801,603↓ 46.2%↓ 28.3%C-Eval90.00%90.62%1,492804↓ 46.1%↓ 19.3%IFBench73.53%71.80%8,0524,657↓ 42.2%↓ 50.5%MathematicsAIME 202698.67%94.00%22,01416,143↓ 26.7%↓ 50.2%HMMT (Nov 2025)99.33%96.00%22,03215,189↓ 31.1%↓ 45.9%MultimodalERQA67.45%66.30%4,1372,045↓ 50.6%↓ 54.6%Agentic codingTerminal-Bench 2.166.74%65.84%37,08627,272↓ 26.5%↓ 38.7%LiveCodeBench v676.76%81.55%11,3748,615↓ 24.3%↓ 45.8%**How to reproduce****Serving:**BF16 · vLLM 0.27.1 · Qwen3 parser · context 262,144 · thinking xhigh. **Sampling:**temperature 1.0 · top_p 0.95 · top_k 20 · min_p 0 · presence_penalty 0 · repetition_penalty 1. **Benchmarks:**averages over five seeds (0–4) per model; five trials per task for Terminal-Bench.

BenchmarkOutput capGPQA-Diamond100,000MMLU-Pro100,000C-Eval16,384IFBench81,920AIME 2026250,000HMMT Nov 2025250,000ERQA100,000Terminal-Bench 2.1Agent/task limitsLiveCodeBench v632,768## https://huggingface.co/ukisai/Swift-Qwen3.8-27b#efficiency-across-and-versus-reasoning-effortsEfficiency across and versus reasoning efforts

Qwen3.8’sreasoning\_effortsetting 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 testedxhigh,medium, andlow: thinking-token savings persist at every level.

Reasoning effortMean thinking reductionXhigh↓ 41.0%Medium↓ 22.7%Low↓ 25.8%The efficiency also holds up against the base’s own lower effort settings. On GPQA-Diamond (198 questions, 5 seeds, 990 paired calls), Swift atxhighis compared with the base atxhighand atmedium:

GPQA-DiamondScoreMean tokensMedian tokensBase · xhigh88.38%15,0146,642Swift · xhigh88.28%8,8552,771Base · medium84.14%4,4511,753Swift retains the accuracy ofxhighwhile using about half the tokens, although it uses about double the tokens ofmedium.

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#quantized-modelsQuantized models

Quantized deployment is the intended use for Swift: lower-memory weights paired with shorter reasoning. The INT4 evaluations below retain token savings across GPQA, IFBench, and AIME. On AIME, Swift matches or improves accuracy and reduces output-cap failures by31–33%.

Benchmark / quantizationBase accuracySwift accuracyMean token reductionMedian token reductionGPQA-Diamond Mixed-precision quant W4A16 · thinking tokens88.69%88.38%↓ 32.1%↓ 50.2%IFBench Mixed-precision quant W4A16 · completion tokens72.58%71.25%↓ 30.1%↓ 38.0%AIME 2026 Mixed-precision quant W4A16 · completion tokens84.00%84.00%↓ 19.0%↓ 37.5%AIME 2026 AWQ INT4 · completion tokens82.67%84.00%↓ 22.8%↓ 34.8%Quantized evaluation settingsEach 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. Output caps: GPQA 100,000; IFBench 81,920; AIME 32,768. GPQA and IFBench use saved historical base runs. AIME uses template-default effort and counts truncated answers as incorrect. Its shorter cap makes it a separate comparison from the BF16 table.

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#how-to-useHow to use

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#gguf-downloadGGUF download

The**GGUF version**is available for compatible llama.cpp-based runtime.

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#ukisai-apiUkisAI API

Swift is served through an OpenAI-compatible API athttps://ukisai\.com/api/swift/v1. It isfree for research purposesand needs no API key. The model id isswift.

from openai import OpenAI

client = OpenAI(base_url="https://ukisai.com/api/swift/v1", api_key="none")
response = client.chat.completions.create(
    model="swift",
    messages=[{"role": "user", "content": "Explain speculative decoding in two sentences."}],
)
print(response.choices[0].message.content)

curl https://ukisai.com/api/swift/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "swift", "messages": [{"role": "user", "content": "Hello, Swift."}]}'

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#transformersTransformers

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "ukisai/Swift-Qwen3.8-27b"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#vllmvLLM

vllm serve ukisai/Swift-Qwen3.8-27b \
  --dtype bfloat16 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --port 8000

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#sglangSGLang

Alternatively, use a current SGLang build with Qwen3.8 support:

python -m sglang.launch_server \
  --model-path ukisai/Swift-Qwen3.8-27b \
  --dtype bfloat16 \
  --tp-size 1 \
  --context-length 262144 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --port 8000

Adjust tensor parallelism and context length to your GPU memory. See the base model’svLLM recipeandSGLang recipefor installation and hardware-specific settings.

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#optional-mtp-decodingOptional MTP decoding

The published weights include the base model’s MTP head. To enable self-speculative decoding, append the corresponding flags to the server command above:

# vLLM
--speculative-config '{"method":"mtp","num_speculative_tokens":3}'

# SGLang
--speculative-algorithm EAGLE --speculative-num-steps 3 \
  --speculative-eagle-topk 1 --speculative-num-draft-tokens 4

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#license-and-accessLicense and access

Swift weights are distributed under theSwift Open License v1.0. Personal, research, educational, evaluation, and commercial use are free for individuals and organizations with annual recurring revenue, including affiliates, of up to US$1,000,000. Above that threshold, commercial use requires a separateSwift Enterprise License. ContactUkisAIfor terms.

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#citationCitation

@misc{swift-qwen3.8-27b,
  title  = {Swift-Qwen3.8-27B},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-Qwen3.8-27b}
}

https://huggingface.co/ukisai/Swift-Qwen3.8-27b#acknowledgementsAcknowledgements

We acknowledge theNVIDIA Innovation Labfor providing access to8× NVIDIA H100 GPUsto train Swift.