license: other
license_name: swift-open-license-1.0
library_name: transformers
pipeline_tag: image-text-to-text
gated: true
base_model: ukisai/Swift-Qwen3.8-27b
tags:

• qwen3_8
• swift
• uncensored
• abliterated
• bf16
• efficient-thinking
• reasoning
• multimodal
• tool-calling

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Swift-Qwen3.8-27B-Uncensored-BF16

Independent uncensored BF16 derivative of UkisAI Swift-Qwen3.8-27B, produced by rank-1 directional residual-stream ablation.

This release

• BF16 checkpoint, approximately 52 GB
• context configuration: 262,144 tokens
• ablation layer: 38
• rank: 1
• modified residual writers: 131
• vision tensors unchanged by the ablation
• MTP residual writers included
• refusal evaluation: 88 DIRECT / 10 SAFETY_DEFLECT / 2 OTHER_FAILURE / 0 REFUSE
• agentic benchmark: 80.24 tok/s on RTX PRO 6000 Blackwell 96 GB

Intelligence-preservation A/B

Fixed 298-example comparison against the original Swift BF16 checkpoint:

• original Swift BF16: 38.26% combined
• uncensored BF16: 39.93% combined
• delta: +1.68 percentage points
• McNemar: p=0.442068
• bootstrap 95% CI: [-1.68, +5.03] pp

No measurable intelligence degradation was detected in this validation set. Full results are provided in INTELLIGENCE_VALIDATION.json; structural validation is in STRUCTURAL_VALIDATION.json; transformation metadata is in ABLITERATION.json.

Upstream Swift model

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.

The prompt is a sample from LiveCodeBench v6

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.

Evaluation scope

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

Benchmarks

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.

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
compared with the base at xhigh and at medium:

Swift retains the accuracy of xhigh while using about half the tokens, although
it uses about double the tokens of medium.

Quantized 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 by 31–33%.

How to use

GGUF download

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

UkisAI API

Swift is served through an OpenAI-compatible API at https://ukisai.com/api/swift/v1.
It is free for research purposes and needs no API key. The model id is swift.

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."}]}'

Transformers

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",
)

vLLM

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

SGLang

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's
vLLM recipe and
SGLang recipe
for installation and hardware-specific settings.

Optional 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

License and access

Swift weights are distributed through gated access under the Swift 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 separate Swift Enterprise
License. Contact UkisAI for terms.

Citation

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

Acknowledgements

We acknowledge the NVIDIA Innovation Lab for providing access to 8× NVIDIA H100 GPUs to train Swift.