This is the link to the model: https://huggingface.co/ukisai/Swift-Qwen3.8-27b / It's at 80k downloads in 3 days with independent evals here: https://www.reddit.com/r/LocalLLaMA/comments/1wg7dd5/ukisai_...
We are also providing a Free Research Purpose API (OpenAI compatible), courtesy of Nvidia who were kind enough to provide us with the GPUs. It's limited at 5RPM. https://ukisai.com/api/swift/v1/models
We also made a GGUF (Q1-Q8): https://huggingface.co/ukisai/Swift-Qwen3.8-27B-GGUF and there's also a few nice community quants with even lower/higher precision (Bartowski: https://huggingface.co/bartowski/ukisai_Swift-Qwen3.8-27b-GG...). The community also created amazing MLX, NVFP4, W4A16 and Uncensored versions you can find on Huggingface.
IMPORTANT: Our training approach is not a replacement for the reasoning effort settings, chat templates or token caps but is complementary and targets a completely separate issue (overthinking and "anxiety-like" reasoning loops prior seen in PTQ, but as far as we identified also prominent in BF16 of this size class LLMs as well). Contrary to popular belief, these specific patterns do not contribute to answer quality when properly targeted. (our thesis being: reasoning length IS extremely important and should NOT be shortened by force, but rather optimized). This is also demonstrated bellow in our xhigh vs medium effort benchmark. The goal is to keep xhigh accuracy while reducing only the unnecessary part of thinking.
The TLDR of our thought process, research, training and a link to the Meta paper that inspired us is in the first comment.
The benchmarks:
Swift-27B vs Qwen3.8-27B (BF16, all benchmarks ran x5, thinking effort xhigh)
GPQA-Diamond: 88.4% -> 88.3%, 58% fewer median tokens
LiveCodeBench v6: 76.8% -> 81.6% (+4.8pp, due to default truncation in LCB it is not performance gain), 46% fewer median thinking tokens
Terminal-Bench 2.1: 66.7% -> 65.8%, 39% fewer median tokens
MMLU-Pro: 85.5% -> 85.0%, 28% fewer median tokens
C-Eval: 90.0% -> 90.6%, 19% fewer median tokens
IFBench: 73.5% -> 71.8%, 51% fewer median tokens
AIME 2026: 98.7% -> 94.0%, 50% fewer median tokens
HMMT (Nov 2025): 99.3% -> 96.0%, 46% fewer median tokens
ERQA (vision): 67.5% -> 66.3%, 55% fewer median tokens
Token savings hold at every reasoning effort (mean thinking reduction): xhigh 41%, medium 23%, low 26% (albeit with accuracy loses of 1-4% on medium and 1-2% on low which we need further testing for)
Swift at xhigh vs the base's own effort settings on GPQA-Diamond (198 questions x 5 seeds):
Base xhigh: 88.4%, 6,642 median tokens
Swift xhigh: 88.3%, 2,771 median tokens
Base medium: 84.1%, 1,753 median tokens
So Swift keeps xhigh accuracy at under half the tokens, and beats base-medium by 4pp at roughly 1.6x its tokens.
End note:
While we are keen on complete open-source, we still need to keep a part of our training and data private, being a new lab. The license is not Apache 2.0, but it only affects companies >$1M. We hope this does not pose a problem for the community and are open to feedback on it.
We want to contribute as much as possible to the community and would really appreciate feedback on our work, quantization or Swift model requests. We are working on Swift 3.8 Flash Next and have so far gotten up to -53% thinking token usage. We have strong indicators our methodology is reproducible on other model families as well and are asking the community which ones you want us to optimize next.
kisjovan•46m ago
1. When running our quantized Qwen 3.8 27B instances we were very annoyed by random reasoning loops (in the paper bellow refered to as "overthinking errors". These random loops were persistent throughout medium and low reasoning settings.
2. We found a paper by Meta that's supposed to target this phenomenon in PTQ, but when used straight out of the box got mixed results. https://arxiv.org/abs/2606.00206
3. We figured to try if it's a matter of the targeting the right keywords and tuning the parameters, so we used our 8xH100 box and and generated a large amount of different (ofc out of distribution) domain (coding, language, vision, agentic) traces.
4. We then grouped the ones with overthinking and found "common denominator" tokens between them and targeted the most prominent ones.
5. We then built an inference-time penalizer of those tokens as seen in the paper with the hopes of simply generating traces and doing cross-entropy SFT over them.
6. Did not work at all, but the penalizer seemed to work much better than the tokens provided in the paper and not only for lower precision models but for bf16 as well. Hence we kept experimenting with it. We built a loss function using the tokens we identified and ran LoRa SFT over the traces prev generated and reasoning seemed to be falling off significantly but the accuracy seemed to follow. The reasoning reduction seemed to be generalizing.
7. After a significant amount of tinkering (literally since the day of Qwen 3.8 27B release) we were satisfied with the reasoning reduction. After that we searched for ways of restoring the accuracy. We experimented with several methods, including RL(GSPO), On-Policy Distillation and using the ThinkingCap 3.6 27B adapter chunks until we were satisfied with our accuracy loss. We managed to restore it to <1% loss on almost all of our OOD in house tests
8. We then performed intensive intensive benchmarks, across several reasoning efforts, precision variants etc. We ran into a few problems, one of which is that to get a reliable score we needed to run each benchmark 10x (5x on base + 5x with our adapter, this being the standard procedure on the Qwen 3.6 27B model card on Terminal Bench which we followed). After running it, the performance converged to 40-60% token reduction with <1% accuracy loss across GPQA, MMLU, Terminal Bench 2.1, LiveCodeBench v6, ERQA, C-Eval, IFBench, HMMT25, with an exception being AIME26 with an accuracy loss of 4.6%, which we later linked to a bug during training with a specific token relevant for math-related reasoning being penalized and are planning to fix it in an updated release.