README
Swift 1.5 Qwen3.8-27B
GGUF quantizations. Derived directly from Swift 1.5 with llama.cpp. Run a chosen quantization tier with a current llama.cpp-compatible runtime such as llama-server.
Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses 58.5% fewer thinking tokens while scoring 0.35% higher than the base, for a 9.18× speed-up on several tasks.
Swift 1.5 is a direct upgrade from Swift 1.0, our model with 350k+ downloads, delivering stronger overall performance than both base and Swift 1.0 in various tasks, especially coding and agentic, while using fewer thinking tokens. We accomplished that by scaling up the post-training (RL and OPD) from the previous version.
Demo
We gave base Qwen3.8-27B and Swift 1.5 27B the same prompt:
create a 3d little planet globe where I (player can walk around) and it has all these biomes to explore, the globe doesn't have to be too big, but still fun to go around. It's about a boy scout who is camping and goes around exploring.
Try the game yourself here: https://ukisai.com/swift-games/27b
Base Qwen3.8-27B took 104.6 minutes to build its game. Swift 1.5 took 11.39 minutes.
Training approach
We made Swift 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 was made from Swift 1.0, on whom we scaled up the post-training methods that previously improved Swift1.0 model performance, this time with the main focus on long-horizon, agentic, and coding tasks, as seen in the LiveCodeBench and Terminal Bench 2.1 improvements. 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
The external results below compare Qwen3.8-27B, the foundation base model, and Swift 1.5. Both models use the same saved evaluation protocols, and all scores are reported as final aggregate percentages.
.swift15-table { width:100%; table-layout:fixed; border-collapse:separate; border-spacing:0; overflow:hidden; border:1px solid #27344A; border-radius:20px; background:#0D111B; font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,sans-serif; font-size:14px; color:#BFBDBD; } .swift15-table th { padding:13px 8px; text-align:center; font-weight:700; color:#AEB5C7; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; } .swift15-table td { padding:14px 8px; text-align:center; color:#BFBDBD; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; vertical-align:middle; overflow-wrap:break-word; } .swift15-table tr > :last-child { border-right:0; } .swift15-table tbody tr:last-child td { border-bottom:0; } .swift15-table .benchmark-heading { color:#B7BDCD; background:#0D111B; border-bottom:3px solid #7D45B5; } .swift15-table .score-heading { color:#F0C5FF; background:#52239E; border-bottom:3px solid #7D45B5; } .swift15-table .tokens-heading, .swift15-table .median-heading { color:#D4E8FF; background:#304FC2; border-bottom:3px solid #5687E6; } .swift15-table .change-heading { color:#61B9FF; background:#101D2D; border-bottom:3px solid #5687E6; } .swift15-table .change { color:#61B9FF; background:#101D2D; font-weight:700; } .swift15-table .gain { color:#DDA8FF; background:#171127; font-weight:700; } .swift15-table .model { padding-left:18px; text-align:left; color:#FFFFFF; font-weight:600; } .swift15-table strong { color:#FFFFFF; } .swift15-table .section { padding:12px 18px; text-align:left; color:#B489FF; background:#2A2541; font-weight:700; letter-spacing:.08em; text-transform:uppercase; border-top:1px solid #3A3159; border-bottom:1px solid #3A3159; } .swift15-table .swift15 { background:#171127; } .swift15-table thead tr:nth-child(2) .swift15 { color:#D3A0FF; } .swift15-table .detail { color:#8C94A8; font-size:12px; font-weight:500; } .swift15-table.swift15-compact { display:table !important; width:100% !important; table-layout:fixed !important; } @media (max-width: 640px) { .swift15-table { display:block !important; width:100% !important; max-width:100%; overflow-x:auto !important; -webkit-overflow-scrolling:touch; table-layout:auto !important; } .swift15-table th, .swift15-table td { min-width:104px; } .swift15-table th:first-child, .swift15-table td:first-child { min-width:170px; } }| Benchmark | Final score | Mean tokens | Median tokens | |||
|---|---|---|---|---|---|---|
| Qwen3.8 | Swift 1.5 | Qwen3.8 | Swift 1.5 | Reduction | Reduction | |
| General reasoning | ||||||
| GPQA-Diamond | 88.28% | 88.59% | 15,014 | 8,717 | ↓ 41.9% | ↓ 58.5% |
| C-Eval | 90.00% | 90.92% | 1,492 | 819 | ↓ 45.1% | ↓ 16.9% |
| IFBench | 73.53% | 72.07% | 8,052 | 4,955 | ↓ 38.5% | ↓ 47.3% |
| ERQA | 67.45% | 65.40% | 4,137 | 1,906 | ↓ 53.9% | ↓ 56.2% |
| Mathematics | ||||||
| AIME 2026 | 98.67% | 96.00% | 22,014 | 13,203 | ↓ 40.0% | ↓ 48.5% |
| HMMT November 2025 | 99.33% | 97.33% | 22,032 | 14,957 | ↓ 32.1% | ↓ 47.8% |
| Coding | ||||||
| LiveCodeBench v6 | 76.76% | 81.71% | 11,184 | 8,448 | ↓ 24.5% | ↓ 46.3% |
| Agent tasks | ||||||
| Terminal-Bench 2.1 | 69.21% | 72.13% | 52,265 | 43,733 | ↓ 16.3% | ↓ 0.1% |
Scores are final five-repeat aggregates under matched evaluation protocols. Mean-token columns report reasoning tokens per trial; Terminal-Bench sums reasoning across agent calls.
Benchmark methodology and reproduction settings
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, base and Swift 1.5 served at context 131,072 on the same Harbor build.
| Benchmark | Output cap |
|---|---|
| GPQA-Diamond | 100,000 |
| C-Eval | 16,384 |
| IFBench | 81,920 |
| ERQA | 100,000 |
| AIME 2026 | 250,000 |
| HMMT November 2025 | 250,000 |
| LiveCodeBench v6 | 32,768 |
| Terminal-Bench 2.1 | Agent/task limits |
Efficiency across reasoning efforts
Qwen3.8's reasoning_effort setting lets users choose how much the model thinks.
For Swift 1.5 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.
| Reasoning effort | Qwen3.8 | Swift 1.5 | Mean thinking reduction |
|---|---|---|---|
| Xhigh | 88.28% | 88.59% | ↓ 41.9% |
| Medium | 84.14% | 82.22% | ↓ 24.8% |
| Low | 84.04% | 84.85% | ↓ 28.7% |
At low, Swift 1.5 scores above the base while using about 29% fewer thinking tokens.
Quantized Swift 1.5 models
| Format | Repository | Runtime |
|---|---|---|
| GGUF | Swift-1.5-Qwen3.8-27B-GGUF | llama.cpp |
| GSQ-RCO GGUF (compact 2–3 bit) | Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF | llama.cpp |
| AWQ INT4 (W4A16) | Swift-1.5-Qwen3.8-27b-W4A16-AWQ | vLLM (compressed-tensors) |
| AutoRound INT4 (W4A16) | Swift-1.5-Qwen3.8-27b-W4A16-AutoRound | vLLM (auto-round) |
| AWQ + GPTQ INT4 (W4A16) | Swift-1.5-Qwen3.8-27b-INT4 | vLLM (compressed-tensors) |
| NVFP4 | Swift-1.5-Qwen3.8-27b-NVFP4 | NVIDIA Blackwell |
| AMD Quark FP8 (W8A8) | Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD | AMD Quark |
| MLX 5-bit | Swift-1.5-5bit-MLX | Apple MLX |
| MLX 4-bit | Swift-1.5-4bit-MLX | Apple MLX |
| MLX 3-bit (text only) | Swift-1.5-3bit-MLX-TextOnly | Apple MLX |
These results evaluate the merged Swift 1.5 checkpoint and three INT4 exports on GPQA-Diamond (198 questions), IFBench (300 prompts), and AIME 2026 (30 problems). Each model completed the full datasets with one sample per prompt, seed 0, and zero request errors. This is a single-seed evaluation, separate from the five-repeat BF16 release results above.
The Qwen-base columns use the saved seed/sample 0 runs. Quantization recipes and serving settings differ from the new Swift 1.5 runs, so these are reference comparisons rather than a controlled measurement of the Swift adaptation. Token reductions below are recomputed from those same reference samples.
| Benchmark / Swift 1.5 quantization | Qwen base accuracy | Swift 1.5 quant accuracy | Mean token reduction | Median token reduction |
|---|---|---|---|---|
| GPQA-Diamond AWQ INT4 | 86.36% | 88.38% | ↓ 51.5% | ↓ 64.4% |
| GPQA-Diamond AutoRound INT4 | 86.36% | 89.39% | ↓ 50.5% | ↓ 57.8% |
| GPQA-Diamond AWQ + GPTQ INT4 | 86.36% | 90.91% | ↓ 45.8% | ↓ 64.4% |
| IFBench AWQ INT4 | 72.00% | 72.00% | ↓ 36.9% | ↓ 49.3% |
| IFBench AutoRound INT4 | 72.00% | 69.33% | ↓ 29.3% | ↓ 39.2% |
| IFBench AWQ + GPTQ INT4 | 72.00% | 70.00% | ↓ 31.8% | ↓ 52.7% |
| AIME 2026 AWQ INT4 | 70.00% | 86.67% | ↓ 29.2% | ↓ 36.2% |
| AIME 2026 AutoRound INT4 | 76.67% | 83.33% | ↓ 17.7% | ↓ 32.4% |
| AIME 2026 AWQ + GPTQ INT4 | 76.67% | 83.33% | ↓ 22.4% | ↓ 34.0% |
AIME scoring: truncated responses count as incorrect for both columns.
The AMD Quark INT4 and FP8 exports have separate sanity evaluations; completed results on these three reasoning benchmarks are not available for them.
Quantized evaluation settings and BF16 reference
Serving: vLLM 0.29.0, tensor parallelism 1, eager execution, BF16 activations, context 131,072, template-default thinking without an effort override. The AWQ + GPTQ export uses FP8 KV cache; BF16, AWQ, and AutoRound use auto KV dtype. Sampling: temperature 1, top-p 0.95, top-k 20, min-p 0, presence penalty 0, repetition penalty 1, seed 0. Output caps: GPQA 100,000, IFBench 81,920, AIME 32,768. IFBench uses official strict prompt-level scoring.
GPQA token counts cover re-tokenized reasoning; IFBench and AIME count the full generated response. Statistics include all responses, including truncations; medians use the midpoint of the two central values when the sample count is even.
Saved Qwen references: W4A16 for GPQA and IFBench; Qwen AWQ for the AWQ AIME row; Qwen W4A16 for the AutoRound and AWQ + GPTQ AIME rows. The latter is a W4A16 reference for AutoRound, not an AutoRound base run. The new runs do not reproduce the original software stack.
The fresh Swift 1.5 BF16 reference and all quantized exports scored as follows under this single-seed protocol:
| Model | GPQA-Diamond | IFBench strict | AIME 2026 |
|---|---|---|---|
| Swift 1.5 BF16 | 91.41% | 72.00% | 86.67% |
| AWQ INT4 | 88.38% | 72.00% | 86.67% |
| AutoRound INT4 | 89.39% | 69.33% | 83.33% |
| AWQ + GPTQ INT4 | 90.91% | 70.00% | 83.33% |
Truncation counts are recorded in the linked evaluation data. These single-seed results do not establish quality parity or replace the broader multi-seed evaluation.
Verified counts, token statistics, settings, and evidence hashes.
GGUF quantizations
| File | Size | KLD wikitext @512 | KLD wikitext @32k | 99% KLD @32k | Top-p @32k |
|---|---|---|---|---|---|
| Q8_0 | 29.0 GB | 0.0008 | 0.0006 | 0.005 | 98.85% |
| Q6_K_L | 25.0 GB | 0.0015 | 0.0014 | 0.010 | 98.16% |
| Q6_K | 23.9 GB | 0.0018 | 0.0016 | 0.014 | 98.30% |
| Q6_K_S | 22.9 GB | 0.0020 | 0.0016 | 0.014 | 98.24% |
| Q5_K_M | 20.9 GB | 0.0052 | 0.0061 | 0.050 | 96.92% |
| Q5_K_S | 19.6 GB | 0.0060 | 0.0069 | 0.058 | 96.91% |
| Q4_K_L | 18.8 GB | 0.0106 | 0.0103 | 0.105 | 95.79% |
| Q4_K_M | 17.4 GB | 0.0137 | 0.0134 | 0.163 | 95.03% |
| IQ4_NL | 17.4 GB | 0.0152 | 0.0140 | 0.175 | 95.39% |
| Q4_K_S | 16.4 GB | 0.0164 | 0.0154 | 0.175 | 94.84% |
| IQ4_XS | 15.5 GB | 0.0179 | 0.0173 | 0.187 | 94.96% |
| IQ3_M | 14.9 GB | 0.0410 | 0.0380 | 0.409 | 91.83% |
| Q3_K_L | 14.1 GB | 0.0442 | 0.0410 | 0.415 | 91.63% |
| Q3_K_M | 13.4 GB | 0.0570 | 0.0562 | 0.614 | 90.30% |
| IQ3_XS | 12.8 GB | 0.0583 | 0.0885 | 1.130 | 88.54% |
| Q3_K_S | 12.7 GB | 0.0648 | 0.0658 | 0.712 | 89.53% |
| IQ3_XXS | 12.3 GB | 0.0742 | 0.0844 | 0.996 | 88.70% |
| Q2_K | 10.8 GB | 0.1655 | 0.1546 | 1.728 | 84.00% |
| IQ2_M | 10.5 GB | 0.1523 | 0.1493 | 1.568 | 84.17% |
| IQ2_S | 9.7 GB | 0.2095 | 0.2589 | 3.024 | 80.58% |
| IQ2_XS | 9.1 GB | 0.2433 | 0.2622 | 2.951 | 79.99% |
| IQ2_XXS | 8.9 GB | 0.2866 | 0.2769 | 2.902 | 78.48% |
Mean KL divergence against the Swift 1.5 BF16 source, lower is better. wikitext @512 is wikitext-2
test, 100 windows of 512 tokens. wikitext @32k is wikitext-2 train, 16 windows of 32,768 tokens, scoring
only the last 512 tokens of each window, so every scored token sees at least 32k tokens of context.
99% KLD is the 99th-percentile divergence on the same 32k run. Top-p is top-token agreement with BF16
on the 32k run.
Long context costs very little on this release: for every tier from Q8_0 through Q4_K_S, the 32k mean
is within 10% of the 512-token value (Q5_K_M and Q5_K_S rise about 15%), and Q4_K_M keeps the same
top token as BF16 on 95% of positions at 32k. The pick below follows the 99th-percentile tail at 32k:
0.163 for Q4_K_M, 0.050 for Q5_K_M, 0.014 for Q6_K, 0.005 for Q8_0.
| Use case | Pick |
|---|---|
| 24 GB cards, everyday use | Q4_K_M |
| Long agentic runs, strict tool-call formatting | Q6_K or higher |
| Maximum fidelity | Q8_0 |
Recipe
All 22 tiers were built with llama.cpp commit 6f41ac5 from a BF16 conversion of the published Swift 1.5
safetensors. They reuse the importance matrix and the per-tensor type layouts (--tensor-type-file) that
bartowski computed for Swift 1.0 with his
quantization-config; Swift 1.5 has the same
architecture and tensor shapes. Every file was checked against BF16 on the harness above.
License and access
Swift 1.5 is a derivative of Qwen3.8-27B (Copyright 2026 Alibaba Cloud, Apache License 2.0). 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 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.
Nothing in the Swift Open License limits rights in Qwen3.8-27B itself under Apache 2.0.
Citation
@misc{swift-1.5-qwen3.8-27b,
title = {Swift 1.5 Qwen3.8-27B},
author = {UkisAI},
year = {2026},
url = {https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b}
}
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.
Similar repos
README
Swift 1.5 Qwen3.8-27B
GGUF quantizations. Derived directly from Swift 1.5 with llama.cpp. Run a chosen quantization tier with a current llama.cpp-compatible runtime such as llama-server.
Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses 58.5% fewer thinking tokens while scoring 0.35% higher than the base, for a 9.18× speed-up on several tasks.
Swift 1.5 is a direct upgrade from Swift 1.0, our model with 350k+ downloads, delivering stronger overall performance than both base and Swift 1.0 in various tasks, especially coding and agentic, while using fewer thinking tokens. We accomplished that by scaling up the post-training (RL and OPD) from the previous version.
Demo
We gave base Qwen3.8-27B and Swift 1.5 27B the same prompt:
create a 3d little planet globe where I (player can walk around) and it has all these biomes to explore, the globe doesn't have to be too big, but still fun to go around. It's about a boy scout who is camping and goes around exploring.
Try the game yourself here: https://ukisai.com/swift-games/27b
Base Qwen3.8-27B took 104.6 minutes to build its game. Swift 1.5 took 11.39 minutes.
Training approach
We made Swift 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 was made from Swift 1.0, on whom we scaled up the post-training methods that previously improved Swift1.0 model performance, this time with the main focus on long-horizon, agentic, and coding tasks, as seen in the LiveCodeBench and Terminal Bench 2.1 improvements. 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
The external results below compare Qwen3.8-27B, the foundation base model, and Swift 1.5. Both models use the same saved evaluation protocols, and all scores are reported as final aggregate percentages.
.swift15-table { width:100%; table-layout:fixed; border-collapse:separate; border-spacing:0; overflow:hidden; border:1px solid #27344A; border-radius:20px; background:#0D111B; font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,sans-serif; font-size:14px; color:#BFBDBD; } .swift15-table th { padding:13px 8px; text-align:center; font-weight:700; color:#AEB5C7; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; } .swift15-table td { padding:14px 8px; text-align:center; color:#BFBDBD; background:#0D111B; border-right:1px solid #27344A; border-bottom:1px solid #27344A; vertical-align:middle; overflow-wrap:break-word; } .swift15-table tr > :last-child { border-right:0; } .swift15-table tbody tr:last-child td { border-bottom:0; } .swift15-table .benchmark-heading { color:#B7BDCD; background:#0D111B; border-bottom:3px solid #7D45B5; } .swift15-table .score-heading { color:#F0C5FF; background:#52239E; border-bottom:3px solid #7D45B5; } .swift15-table .tokens-heading, .swift15-table .median-heading { color:#D4E8FF; background:#304FC2; border-bottom:3px solid #5687E6; } .swift15-table .change-heading { color:#61B9FF; background:#101D2D; border-bottom:3px solid #5687E6; } .swift15-table .change { color:#61B9FF; background:#101D2D; font-weight:700; } .swift15-table .gain { color:#DDA8FF; background:#171127; font-weight:700; } .swift15-table .model { padding-left:18px; text-align:left; color:#FFFFFF; font-weight:600; } .swift15-table strong { color:#FFFFFF; } .swift15-table .section { padding:12px 18px; text-align:left; color:#B489FF; background:#2A2541; font-weight:700; letter-spacing:.08em; text-transform:uppercase; border-top:1px solid #3A3159; border-bottom:1px solid #3A3159; } .swift15-table .swift15 { background:#171127; } .swift15-table thead tr:nth-child(2) .swift15 { color:#D3A0FF; } .swift15-table .detail { color:#8C94A8; font-size:12px; font-weight:500; } .swift15-table.swift15-compact { display:table !important; width:100% !important; table-layout:fixed !important; } @media (max-width: 640px) { .swift15-table { display:block !important; width:100% !important; max-width:100%; overflow-x:auto !important; -webkit-overflow-scrolling:touch; table-layout:auto !important; } .swift15-table th, .swift15-table td { min-width:104px; } .swift15-table th:first-child, .swift15-table td:first-child { min-width:170px; } }| Benchmark | Final score | Mean tokens | Median tokens | |||
|---|---|---|---|---|---|---|
| Qwen3.8 | Swift 1.5 | Qwen3.8 | Swift 1.5 | Reduction | Reduction | |
| General reasoning | ||||||
| GPQA-Diamond | 88.28% | 88.59% | 15,014 | 8,717 | ↓ 41.9% | ↓ 58.5% |
| C-Eval | 90.00% | 90.92% | 1,492 | 819 | ↓ 45.1% | ↓ 16.9% |
| IFBench | 73.53% | 72.07% | 8,052 | 4,955 | ↓ 38.5% | ↓ 47.3% |
| ERQA | 67.45% | 65.40% | 4,137 | 1,906 | ↓ 53.9% | ↓ 56.2% |
| Mathematics | ||||||
| AIME 2026 | 98.67% | 96.00% | 22,014 | 13,203 | ↓ 40.0% | ↓ 48.5% |
| HMMT November 2025 | 99.33% | 97.33% | 22,032 | 14,957 | ↓ 32.1% | ↓ 47.8% |
| Coding | ||||||
| LiveCodeBench v6 | 76.76% | 81.71% | 11,184 | 8,448 | ↓ 24.5% | ↓ 46.3% |
| Agent tasks | ||||||
| Terminal-Bench 2.1 | 69.21% | 72.13% | 52,265 | 43,733 | ↓ 16.3% | ↓ 0.1% |
Scores are final five-repeat aggregates under matched evaluation protocols. Mean-token columns report reasoning tokens per trial; Terminal-Bench sums reasoning across agent calls.
Benchmark methodology and reproduction settings
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, base and Swift 1.5 served at context 131,072 on the same Harbor build.
| Benchmark | Output cap |
|---|---|
| GPQA-Diamond | 100,000 |
| C-Eval | 16,384 |
| IFBench | 81,920 |
| ERQA | 100,000 |
| AIME 2026 | 250,000 |
| HMMT November 2025 | 250,000 |
| LiveCodeBench v6 | 32,768 |
| Terminal-Bench 2.1 | Agent/task limits |
Efficiency across reasoning efforts
Qwen3.8's reasoning_effort setting lets users choose how much the model thinks.
For Swift 1.5 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.
| Reasoning effort | Qwen3.8 | Swift 1.5 | Mean thinking reduction |
|---|---|---|---|
| Xhigh | 88.28% | 88.59% | ↓ 41.9% |
| Medium | 84.14% | 82.22% | ↓ 24.8% |
| Low | 84.04% | 84.85% | ↓ 28.7% |
At low, Swift 1.5 scores above the base while using about 29% fewer thinking tokens.
Quantized Swift 1.5 models
| Format | Repository | Runtime |
|---|---|---|
| GGUF | Swift-1.5-Qwen3.8-27B-GGUF | llama.cpp |
| GSQ-RCO GGUF (compact 2–3 bit) | Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF | llama.cpp |
| AWQ INT4 (W4A16) | Swift-1.5-Qwen3.8-27b-W4A16-AWQ | vLLM (compressed-tensors) |
| AutoRound INT4 (W4A16) | Swift-1.5-Qwen3.8-27b-W4A16-AutoRound | vLLM (auto-round) |
| AWQ + GPTQ INT4 (W4A16) | Swift-1.5-Qwen3.8-27b-INT4 | vLLM (compressed-tensors) |
| NVFP4 | Swift-1.5-Qwen3.8-27b-NVFP4 | NVIDIA Blackwell |
| AMD Quark FP8 (W8A8) | Swift-1.5-Qwen3.8-27b-Quark-FP8-dynamic-AMD | AMD Quark |
| MLX 5-bit | Swift-1.5-5bit-MLX | Apple MLX |
| MLX 4-bit | Swift-1.5-4bit-MLX | Apple MLX |
| MLX 3-bit (text only) | Swift-1.5-3bit-MLX-TextOnly | Apple MLX |
These results evaluate the merged Swift 1.5 checkpoint and three INT4 exports on GPQA-Diamond (198 questions), IFBench (300 prompts), and AIME 2026 (30 problems). Each model completed the full datasets with one sample per prompt, seed 0, and zero request errors. This is a single-seed evaluation, separate from the five-repeat BF16 release results above.
The Qwen-base columns use the saved seed/sample 0 runs. Quantization recipes and serving settings differ from the new Swift 1.5 runs, so these are reference comparisons rather than a controlled measurement of the Swift adaptation. Token reductions below are recomputed from those same reference samples.
| Benchmark / Swift 1.5 quantization | Qwen base accuracy | Swift 1.5 quant accuracy | Mean token reduction | Median token reduction |
|---|---|---|---|---|
| GPQA-Diamond AWQ INT4 | 86.36% | 88.38% | ↓ 51.5% | ↓ 64.4% |
| GPQA-Diamond AutoRound INT4 | 86.36% | 89.39% | ↓ 50.5% | ↓ 57.8% |
| GPQA-Diamond AWQ + GPTQ INT4 | 86.36% | 90.91% | ↓ 45.8% | ↓ 64.4% |
| IFBench AWQ INT4 | 72.00% | 72.00% | ↓ 36.9% | ↓ 49.3% |
| IFBench AutoRound INT4 | 72.00% | 69.33% | ↓ 29.3% | ↓ 39.2% |
| IFBench AWQ + GPTQ INT4 | 72.00% | 70.00% | ↓ 31.8% | ↓ 52.7% |
| AIME 2026 AWQ INT4 | 70.00% | 86.67% | ↓ 29.2% | ↓ 36.2% |
| AIME 2026 AutoRound INT4 | 76.67% | 83.33% | ↓ 17.7% | ↓ 32.4% |
| AIME 2026 AWQ + GPTQ INT4 | 76.67% | 83.33% | ↓ 22.4% | ↓ 34.0% |
AIME scoring: truncated responses count as incorrect for both columns.
The AMD Quark INT4 and FP8 exports have separate sanity evaluations; completed results on these three reasoning benchmarks are not available for them.
Quantized evaluation settings and BF16 reference
Serving: vLLM 0.29.0, tensor parallelism 1, eager execution, BF16 activations, context 131,072, template-default thinking without an effort override. The AWQ + GPTQ export uses FP8 KV cache; BF16, AWQ, and AutoRound use auto KV dtype. Sampling: temperature 1, top-p 0.95, top-k 20, min-p 0, presence penalty 0, repetition penalty 1, seed 0. Output caps: GPQA 100,000, IFBench 81,920, AIME 32,768. IFBench uses official strict prompt-level scoring.
GPQA token counts cover re-tokenized reasoning; IFBench and AIME count the full generated response. Statistics include all responses, including truncations; medians use the midpoint of the two central values when the sample count is even.
Saved Qwen references: W4A16 for GPQA and IFBench; Qwen AWQ for the AWQ AIME row; Qwen W4A16 for the AutoRound and AWQ + GPTQ AIME rows. The latter is a W4A16 reference for AutoRound, not an AutoRound base run. The new runs do not reproduce the original software stack.
The fresh Swift 1.5 BF16 reference and all quantized exports scored as follows under this single-seed protocol:
| Model | GPQA-Diamond | IFBench strict | AIME 2026 |
|---|---|---|---|
| Swift 1.5 BF16 | 91.41% | 72.00% | 86.67% |
| AWQ INT4 | 88.38% | 72.00% | 86.67% |
| AutoRound INT4 | 89.39% | 69.33% | 83.33% |
| AWQ + GPTQ INT4 | 90.91% | 70.00% | 83.33% |
Truncation counts are recorded in the linked evaluation data. These single-seed results do not establish quality parity or replace the broader multi-seed evaluation.
Verified counts, token statistics, settings, and evidence hashes.
GGUF quantizations
| File | Size | KLD wikitext @512 | KLD wikitext @32k | 99% KLD @32k | Top-p @32k |
|---|---|---|---|---|---|
| Q8_0 | 29.0 GB | 0.0008 | 0.0006 | 0.005 | 98.85% |
| Q6_K_L | 25.0 GB | 0.0015 | 0.0014 | 0.010 | 98.16% |
| Q6_K | 23.9 GB | 0.0018 | 0.0016 | 0.014 | 98.30% |
| Q6_K_S | 22.9 GB | 0.0020 | 0.0016 | 0.014 | 98.24% |
| Q5_K_M | 20.9 GB | 0.0052 | 0.0061 | 0.050 | 96.92% |
| Q5_K_S | 19.6 GB | 0.0060 | 0.0069 | 0.058 | 96.91% |
| Q4_K_L | 18.8 GB | 0.0106 | 0.0103 | 0.105 | 95.79% |
| Q4_K_M | 17.4 GB | 0.0137 | 0.0134 | 0.163 | 95.03% |
| IQ4_NL | 17.4 GB | 0.0152 | 0.0140 | 0.175 | 95.39% |
| Q4_K_S | 16.4 GB | 0.0164 | 0.0154 | 0.175 | 94.84% |
| IQ4_XS | 15.5 GB | 0.0179 | 0.0173 | 0.187 | 94.96% |
| IQ3_M | 14.9 GB | 0.0410 | 0.0380 | 0.409 | 91.83% |
| Q3_K_L | 14.1 GB | 0.0442 | 0.0410 | 0.415 | 91.63% |
| Q3_K_M | 13.4 GB | 0.0570 | 0.0562 | 0.614 | 90.30% |
| IQ3_XS | 12.8 GB | 0.0583 | 0.0885 | 1.130 | 88.54% |
| Q3_K_S | 12.7 GB | 0.0648 | 0.0658 | 0.712 | 89.53% |
| IQ3_XXS | 12.3 GB | 0.0742 | 0.0844 | 0.996 | 88.70% |
| Q2_K | 10.8 GB | 0.1655 | 0.1546 | 1.728 | 84.00% |
| IQ2_M | 10.5 GB | 0.1523 | 0.1493 | 1.568 | 84.17% |
| IQ2_S | 9.7 GB | 0.2095 | 0.2589 | 3.024 | 80.58% |
| IQ2_XS | 9.1 GB | 0.2433 | 0.2622 | 2.951 | 79.99% |
| IQ2_XXS | 8.9 GB | 0.2866 | 0.2769 | 2.902 | 78.48% |
Mean KL divergence against the Swift 1.5 BF16 source, lower is better. wikitext @512 is wikitext-2
test, 100 windows of 512 tokens. wikitext @32k is wikitext-2 train, 16 windows of 32,768 tokens, scoring
only the last 512 tokens of each window, so every scored token sees at least 32k tokens of context.
99% KLD is the 99th-percentile divergence on the same 32k run. Top-p is top-token agreement with BF16
on the 32k run.
Long context costs very little on this release: for every tier from Q8_0 through Q4_K_S, the 32k mean
is within 10% of the 512-token value (Q5_K_M and Q5_K_S rise about 15%), and Q4_K_M keeps the same
top token as BF16 on 95% of positions at 32k. The pick below follows the 99th-percentile tail at 32k:
0.163 for Q4_K_M, 0.050 for Q5_K_M, 0.014 for Q6_K, 0.005 for Q8_0.
| Use case | Pick |
|---|---|
| 24 GB cards, everyday use | Q4_K_M |
| Long agentic runs, strict tool-call formatting | Q6_K or higher |
| Maximum fidelity | Q8_0 |
Recipe
All 22 tiers were built with llama.cpp commit 6f41ac5 from a BF16 conversion of the published Swift 1.5
safetensors. They reuse the importance matrix and the per-tensor type layouts (--tensor-type-file) that
bartowski computed for Swift 1.0 with his
quantization-config; Swift 1.5 has the same
architecture and tensor shapes. Every file was checked against BF16 on the harness above.
License and access
Swift 1.5 is a derivative of Qwen3.8-27B (Copyright 2026 Alibaba Cloud, Apache License 2.0). 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 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.
Nothing in the Swift Open License limits rights in Qwen3.8-27B itself under Apache 2.0.
Citation
@misc{swift-1.5-qwen3.8-27b,
title = {Swift 1.5 Qwen3.8-27B},
author = {UkisAI},
year = {2026},
url = {https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b}
}
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.