Swift-1.5-Qwen3.8-Flash-Next-W4A16-AutoRound

ukisai/Swift-1.5-Qwen3.8-Flash-Next-W4A16-AutoRound

Swift-1.5-Qwen3.8-Flash-Next-W4A16-AutoRound is a image-text-to-text model from ukisai. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.

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.safetensors177 GB · 100%

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How the weights are stored.

BF1656.5B · 77%

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Base models

Datasets

ukisai/Qwen3.8-27B-multi-turn-agent-sft

Model card

From the Hugging Face model README

<div align="center"> <a href="https://ukisai.com"><img src="ukisai-banner.png" alt="UkisAI" style="width:100%;max-width:100%;height:auto;display:block;margin-bottom:0.6em;" /></a> <div style="display:flex;justify-content:center;gap:0.6em;margin-bottom:1em;"> <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-1.5-Qwen3.8-Flash-Next-GGUF"><strong>GGUF</strong></a>  •  <a href="https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF"><strong>GSQ-RCO GGUF</strong></a>  •  <a href="#evaluation"><strong>Evaluation</strong></a>  •  <a href="#license-and-access"><strong>Enterprise licensing</strong></a> </div> </div>

Swift 1.5 Qwen3.8-Flash-Next

AutoRound INT4 (W4A16). Derived directly from Swift 1.5 Qwen3.8-Flash-Next with Intel AutoRound. Run it with a runtime that supports the included auto-round configuration.

Swift 1.5 Qwen3.8-Flash-Next is UkisAI's reasoning-efficient derivative of Qwen3.8-Flash-Next. It uses 63.4% fewer thinking tokens, with a 1.8x speed up while keeping the accuracy loss <1% vs base on xhigh.

Demo

We gave base Qwen3.8-Flash-Next and Swift 1.5 Qwen3.8-Flash-Next the same prompt:

Create a 3D endless runner that has the fast, playful feel of Subway Surfers, but make the world and characters your own. I want to run through a lively place, dodge things, collect rewards, and feel the pace build the longer I survive. Make it fun to control and visually memorable. Use your judgment for the setting, mechanics, and little details that make it feel like a real game. Build it so I can launch and play it locally, then tell me how to run it.

Try the game yourself here: https://ukisai.com/swift-games/flash-next

Base Qwen3.8-Flash-Next took 8 minutes 52 seconds to build its game. Swift 1.5 took 4 minutes 56 seconds.

Training approach

We made Swift Flash Next efficient by figuring out which tokens were linked to pathological overthinking and penalizing them without "attacking" the reasoning length directly then regained the accuracy with RL and OPD, leading to "compressed" token usage while maintaining accuracy.

Swift 1.5 produces shorter reasoning traces. In our testing, we also observe fewer overthinking errors.

This release also features our previously mentioned post-training methods adapted specifically for coding and long-horizon agent work such as personal agents, terminal use and software engineering.

Our training data is viewable here: https://huggingface.co/datasets/ukisai/Qwen3.8-27B-multi-turn-agent-sft albeit it is not used out of the box, but rather re-sampled, turned into proper RL environments etc.

Evaluation

All scores compare the Qwen3.8-Flash-Next BF16 base with the Swift 1.5 BF16 checkpoint. Token columns report thinking tokens, except Terminal-Bench 2.1, which reports total generated output tokens.

Efficiency across reasoning efforts

GPQA-Diamond at each reasoning_effort setting, Swift 1.5 against the base at the same setting:

<table class="swift-table" style="display:table;width:100%;table-layout:fixed;"> <colgroup><col style="width:16%"><col style="width:12%"><col style="width:12%"><col style="width:12%"><col style="width:12%"><col style="width:18%"><col style="width:18%"></colgroup> <thead> <tr> <th rowspan="2" class="benchmark-heading" style="width:16%;text-align:left;padding-left:18px;vertical-align:bottom;">Reasoning effort</th> <th colspan="2" class="score-heading" style="width:24%;">Score</th> <th colspan="3" class="tokens-heading" style="width:42%;">Mean tokens</th> <th class="median-heading" style="width:18%;">Median tokens</th> </tr> <tr> <th>Base</th><th class="swift">Swift 1.5</th> <th>Base</th><th class="swift">Swift 1.5</th> <th class="reduction">Reduction</th><th class="reduction">Reduction</th> </tr> </thead> <tbody> <tr><td class="benchmark">Xhigh</td><td>89.80%</td><td class="swift">89.60%</td><td>17,683</td><td class="swift">7,823</td><td class="reduction">↓ 55.8%</td><td class="reduction">↓ 63.4%</td></tr> <tr><td class="benchmark">Medium</td><td>86.36%</td><td class="swift">83.74%</td><td>4,157</td><td class="swift">2,483</td><td class="reduction">↓ 40.3%</td><td class="reduction">↓ 25.1%</td></tr> <tr><td class="benchmark">Low</td><td>87.17%</td><td class="swift">84.75%</td><td>3,966</td><td class="swift">2,645</td><td class="reduction">↓ 33.3%</td><td class="reduction">↓ 19.7%</td></tr> </tbody> </table>

At xhigh, Swift 1.5 trails base by 0.20 percentage points while using 55.8% fewer mean and 63.4% fewer median thinking tokens. Medium and low save tokens but also lose 2.62 and 2.42 percentage points respectively.

Quantized models

Format	Repository	Runtime
AWQ INT4 (W4A16)	Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ	vLLM (`compressed-tensors`)
AutoRound INT4 (W4A16)	Swift-1.5-Qwen3.8-Flash-Next-W4A16-AutoRound	vLLM (`auto-round`)
NVFP4	Swift-1.5-Qwen3.8-Flash-Next-NVFP4	NVIDIA Blackwell
GGUF	Swift-1.5-Qwen3.8-Flash-Next-GGUF	llama.cpp
GSQ-RCO GGUF (compact 2–3 bit)	Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF	llama.cpp

License and access

Swift 1.5 Qwen3.8-Flash-Next is a derivative of Qwen3.8-Flash-Next (Copyright (c) 2026 Qwen, Qwen Community License 1.0). UkisAI's contribution, including the adapted weights, is licensed under the Swift Open License v1.0. See NOTICE for the change notice and attribution details.

Personal, research, educational, evaluation, and commercial use of the Swift contribution are free for individuals and organizations with gross annual 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.

The base model's terms still apply to it. Under the Qwen Community License 1.0, organizations that run a Model-as-a-Service or AI Work Assistant business need a separate license from Qwen before any commercial use, and products above 100 million monthly active users or US$20 million monthly revenue must prominently display the model name. Nothing in the Swift Open License limits your rights in Qwen3.8-Flash-Next itself under the Qwen Community License.

Citation

@misc{swift-qwen3.8-flash-next,
  title  = {Swift 1.5 Qwen3.8-Flash-Next},
  author = {UkisAI},
  year   = {2026},
  url    = {https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next}
}

Acknowledgements

We acknowledge the NVIDIA Innovation Lab, Amazon Web Services, and Google Cloud for providing compute credits and infrastructure support for Swift's development, training, and evaluation.

Task: Image-Text-to-Text
Library: transformers
Type: qwen4_exp
License: other
Created: Sep 22, 2026
Updated: Sep 24, 2026