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+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+ukisai-banner.png filter=lfs diff=lfs merge=lfs -text
+swift-1.5-flash-next-demo.mp4 filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,233 @@
+Swift Open License v1.0
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+APPENDIX: Notice for redistributors (quantizations, conversions, merges).
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+   Copyright 2026 UkisAI. Swift Contribution licensed under the Swift Open
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+   Derivative of Qwen3.8-Flash-Next, Copyright (c) 2026 Qwen, Qwen Community License 1.0.

LICENSE-QWEN

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+Qwen Community License 1.0
+
+Copyright (c) 2026 Qwen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software, including the model weights, parameters, configuration files, inference code and associated documentation files (collectively, the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, sell, deploy, host, fine-tune, and create derivative works from (collectively, "Use" or "Using") copies of the Software; and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+1. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. If the Software (or any derivative works thereof) is Used for any of the licensee's commercial products or services that have more than 100,000,000 monthly active users or US$ 20,000,000 (or equivalent in other currencies) monthly revenue, respective model name must be prominently displayed on the user interface of such product or service; and,
+
+2. If the licensee or any of its affiliates conducts a Model as a Service or AI Work Assistant business, the licensee shall obtain a separate license from Qwen before Using the Software or its derivative works for any commercial purpose. The foregoing requirement shall not apply to the licensee's internal Use of the Software, provided that such Use does not make the Software, its outputs, or its underlying model capabilities available to any third party.
+
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+“AI Work Assistant” means an independent AI-powered product primarily designed for AI-assisted coding or office productivity (e.g., Qoder and QwenWork). It does not include: (a) a single-purpose AI tool (such as an AI translation tool); (b) an AI assistant primarily designed for a domain other than coding or office productivity (such as Taobao AI Shopping Assistant or AMap AI Chat); or (c) an AI assistant that is a feature of a product whose primary purpose is not AI-assisted coding or office productivity.
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+
+For any questions regarding this license, please contact model-business@notice.qwencloud.com.

NOTICE

@@ -0,0 +1,23 @@
+Swift-Qwen3.8-Flash-Next
+Copyright 2026 UkisAI
+
+UkisAI's contribution (the "Swift Contribution") is licensed under the
+Swift Open License v1.0. See LICENSE.
+
+This model is a derivative work of Qwen3.8-Flash-Next
+  https://huggingface.co/Qwen/Qwen3.8-Flash-Next
+  Copyright (c) 2026 Qwen
+  Licensed under the Qwen Community License 1.0. See LICENSE-QWEN.
+
+Changes made by UkisAI:
+  - model-*.safetensors: model weights were modified by UkisAI through fine-tuning.
+  - tokenizer.json, tokenizer_config.json: re-serialized from UkisAI's training
+    base. Vocabulary and merges are unchanged; the file format, some metadata
+    fields and the pre-tokenizer pattern differ from the Base Model files.
+  - README.md: replaced. LICENSE (Swift Open License v1.0), NOTICE,
+    benchmarks/ and ukisai-banner.png added.
+  - The Base Model's license text was moved from LICENSE to LICENSE-QWEN.
+  - All other files (config.json, generation_config.json, chat_template.jinja,
+    model.safetensors.index.json, vocab.json, merges.txt,
+    preprocessor_config.json, video_preprocessor_config.json) are unmodified
+    from Qwen3.8-Flash-Next and remain under the Qwen Community License 1.0.

README.md

@@ -0,0 +1,330 @@
+---
+license: other
+license_name: swift-open-license-1.0
+license_link: https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next/blob/main/LICENSE
+library_name: transformers
+pipeline_tag: image-text-to-text
+gated: true
+tags:
+- qwen3_8
+- moe
+- reasoning
+- efficient-thinking
+- token-efficient
+- post-training
+- agentic
+- terminal-bench
+datasets:
+- ukisai/Qwen3.8-27B-multi-turn-agent-sft
+base_model: Qwen/Qwen3.8-Flash-Next
+base_model_relation: finetune
+---
+
+<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> &nbsp;&bull;&nbsp;
+    <a href="https://ukisai.com/products/swift"><strong>Learn more</strong></a> &nbsp;&bull;&nbsp;
+    <a href="https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF"><strong>GGUF</strong></a> &nbsp;&bull;&nbsp;
+    <a href="https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF"><strong>GSQ-RCO GGUF</strong></a> &nbsp;&bull;&nbsp;
+    <a href="#evaluation"><strong>Evaluation</strong></a> &nbsp;&bull;&nbsp;
+    <a href="#license-and-access"><strong>Enterprise licensing</strong></a>
+  </div>
+</div>
+
+# Swift 1.5 Qwen3.8-Flash-Next
+
+Swift 1.5 Qwen3.8-Flash-Next is UkisAI's reasoning-efficient derivative of [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/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.
+
+<video src="https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next/resolve/main/swift-1.5-flash-next-demo.mp4" controls autoplay muted loop playsinline style="width:100%;height:auto;border-radius:12px;"></video>
+
+Try the game yourself here: [https://ukisai.com/swift-games/flash-next](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**.
+
+<style>
+.swift-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; }
+.swift-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; }
+.swift-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; }
+.swift-table tr > :last-child { border-right:0; }
+.swift-table tbody tr:last-child td { border-bottom:0; }
+.swift-table .benchmark-heading { color:#B7BDCD; background:#0D111B; border-bottom:3px solid #7D45B5; }
+.swift-table .score-heading { color:#F0C5FF; background:#52239E; border-bottom:3px solid #7D45B5; }
+.swift-table .tokens-heading, .swift-table .median-heading { color:#D4E8FF; background:#304FC2; border-bottom:3px solid #5687E6; }
+.swift-table .benchmark { padding-left:18px; text-align:left; color:#FFFFFF; font-weight:600; }
+.swift-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; }
+.swift-table .swift { background:#171127; }
+.swift-table thead tr:nth-child(2) .swift { color:#D3A0FF; }
+.swift-table .reduction { color:#69BFFF; background:#101B2C; font-weight:700; }
+.swift-table .detail { color:#8C94A8; font-size:12px; font-weight:500; }
+
+@media (max-width: 640px) {
+  .swift-table { display:block !important; width:100% !important; max-width:100%; overflow-x:auto !important; -webkit-overflow-scrolling:touch; table-layout:auto !important; }
+  .swift-table th, .swift-table td { min-width:100px; }
+  .swift-table th:first-child, .swift-table td:first-child { min-width:160px; }
+}
+</style>
+
+<table class="swift-table">
+  <thead>
+    <tr>
+      <th rowspan="2" class="benchmark-heading" style="width:32%;text-align:left;padding-left:18px;vertical-align:bottom;">Benchmark</th>
+      <th colspan="2" class="score-heading">Score</th>
+      <th colspan="3" class="tokens-heading">Mean tokens</th>
+      <th class="median-heading" style="width:14%;">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="section" colspan="7">General reasoning</td></tr>
+    <tr><td class="benchmark">GPQA-Diamond</td><td>89.80%</td><td class="swift">89.60%</td><td>17,683</td><td class="swift">7,823</td><td class="reduction">&darr; 55.8%</td><td class="reduction">&darr; 63.4%</td></tr>
+    <tr><td class="benchmark">MMLU-Pro</td><td>87.75%</td><td class="swift">87.20%</td><td>3,528</td><td class="swift">1,519</td><td class="reduction">&darr; 57.0%</td><td class="reduction">&darr; 24.0%</td></tr>
+    <tr><td class="benchmark">C-Eval</td><td>93.27%</td><td class="swift">93.60%</td><td>1,048</td><td class="swift">586</td><td class="reduction">&darr; 44.1%</td><td class="reduction">&darr; 7.1%</td></tr>
+    <tr><td class="benchmark">IFBench</td><td>73.20%</td><td class="swift">70.13%</td><td>8,310</td><td class="swift">4,411</td><td class="reduction">&darr; 46.9%</td><td class="reduction">&darr; 55.6%</td></tr>
+    <tr><td class="section" colspan="7">Mathematics</td></tr>
+    <tr><td class="benchmark">AIME 2026</td><td>98.67%</td><td class="swift">96.67%</td><td>23,015</td><td class="swift">15,806</td><td class="reduction">&darr; 31.3%</td><td class="reduction">&darr; 51.1%</td></tr>
+    <tr><td class="benchmark">HMMT (Nov 2025)</td><td>98.00%</td><td class="swift">97.33%</td><td>25,487</td><td class="swift">16,530</td><td class="reduction">&darr; 35.1%</td><td class="reduction">&darr; 54.7%</td></tr>
+    <tr><td class="section" colspan="7">Multimodal</td></tr>
+    <tr><td class="benchmark">ERQA</td><td>70.80%</td><td class="swift">69.30%</td><td>4,036</td><td class="swift">1,788</td><td class="reduction">&darr; 55.7%</td><td class="reduction">&darr; 47.7%</td></tr>
+    <tr><td class="section" colspan="7">Coding</td></tr>
+    <tr><td class="benchmark">LiveCodeBench v6</td><td>88.40%</td><td class="swift">90.39%</td><td>17,833</td><td class="swift">9,849</td><td class="reduction">&darr; 44.8%</td><td class="reduction">&darr; 52.0%</td></tr>
+    <tr><td class="section" colspan="7">Agentic coding</td></tr>
+    <tr><td class="benchmark">Terminal-Bench 2.1*</td><td>67.64%</td><td class="swift">69.66%</td><td>40,591</td><td class="swift">45,428</td><td class="reduction">&uarr; 11.9%</td><td class="reduction">&darr; 17.9%</td></tr>
+  </tbody>
+</table>
+
+<p><strong>* Note:</strong> Terminal-Bench 2.1 results for Flash-Next are not an apples-to-apples comparison with Swift 1.5 27B. Flash-Next used default task timeouts, leading to lower scores for both the base model and Swift Flash-Next.</p>
+
+<details>
+<summary><strong>How to reproduce</strong></summary>
+
+<p style="font-size:13px;line-height:1.5;margin:8px 0;"><strong>Serving:</strong> BF16 · Qwen3 reasoning parser · context 262,144 · thinking xhigh · MTP disabled.<br>
+<strong>Sampling:</strong> temperature 1.0 · top_p 0.95 · top_k 20 · min_p 0 · presence_penalty 0 · repetition_penalty 1.<br>
+<strong>Benchmarks:</strong> five seeds (0–4) for the seeded question benchmarks; Terminal-Bench 2.1 uses five attempts per task. LiveCodeBench is full release v6 mean pass@1 over seeds.<br>
+<strong>Terminal-Bench 2.1:</strong> Harbor 0.20.0 / Terminus-2 2.0.0, pinned 89-task dataset, JSON parser, interleaved thinking, temperature 1, top_p 1, 131,072-token server context, 3,600-second LLM call timeout and native per-task limits. Swift used concurrency 8; its context-recovery fix was applied during the run.</p>
+
+<table style="display:table;width:100%;border-collapse:collapse;font-size:13px;line-height:1.3;margin:8px 0;">
+  <thead><tr><th style="padding:4px 8px;text-align:left;">Benchmark</th><th style="padding:4px 8px;text-align:right;">Output cap</th></tr></thead>
+  <tbody>
+    <tr><td style="padding:3px 8px;">GPQA-Diamond</td><td style="padding:3px 8px;text-align:right;">100,000</td></tr>
+    <tr><td style="padding:3px 8px;">MMLU-Pro</td><td style="padding:3px 8px;text-align:right;">100,000</td></tr>
+    <tr><td style="padding:3px 8px;">C-Eval</td><td style="padding:3px 8px;text-align:right;">16,384</td></tr>
+    <tr><td style="padding:3px 8px;">IFBench</td><td style="padding:3px 8px;text-align:right;">81,920</td></tr>
+    <tr><td style="padding:3px 8px;">AIME 2026</td><td style="padding:3px 8px;text-align:right;">250,000</td></tr>
+    <tr><td style="padding:3px 8px;">HMMT Nov 2025</td><td style="padding:3px 8px;text-align:right;">250,000</td></tr>
+    <tr><td style="padding:3px 8px;">ERQA</td><td style="padding:3px 8px;text-align:right;">100,000</td></tr>
+    <tr><td style="padding:3px 8px;">LiveCodeBench v6</td><td style="padding:3px 8px;text-align:right;">100,000</td></tr>
+  </tbody>
+</table>
+
+</details>
+
+## 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">&darr; 55.8%</td><td class="reduction">&darr; 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">&darr; 40.3%</td><td class="reduction">&darr; 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">&darr; 33.3%</td><td class="reduction">&darr; 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](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ) | vLLM (`compressed-tensors`) |
+| AutoRound INT4 (W4A16) | [Swift-1.5-Qwen3.8-Flash-Next-W4A16-AutoRound](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-W4A16-AutoRound) | vLLM (`auto-round`) |
+| NVFP4 | [Swift-1.5-Qwen3.8-Flash-Next-NVFP4](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-NVFP4) | NVIDIA Blackwell |
+| GGUF | [Swift-1.5-Qwen3.8-Flash-Next-GGUF](https://huggingface.co/ukisai/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](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF) | llama.cpp |
+
+## How to use
+
+The BF16 checkpoint is about 360 GB, so it needs a multi-GPU node. We evaluated it with tensor parallelism 8.
+
+### GGUF download
+
+The **[GGUF version](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF)** is
+available for compatible llama.cpp-based runtimes. For the smallest files, use the
+**[GSQ-RCO GGUF version](https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF)**: 66.5–76 GB mixed-precision quants refined for Swift 1.5 Flash-Next.
+
+### UkisAI API
+
+Swift 1.5 Flash-Next is served through an OpenAI-compatible API at
+`https://ukisai.com/api/flash-next/v1`. It is **free for research purposes** and needs no
+API key. The model id is `flash-next`.
+
+~~~python
+from openai import OpenAI
+
+client = OpenAI(base_url="https://ukisai.com/api/flash-next/v1", api_key="none")
+response = client.chat.completions.create(
+    model="flash-next",
+    messages=[{"role": "user", "content": "Explain speculative decoding in two sentences."}],
+)
+print(response.choices[0].message.content)
+~~~
+
+~~~bash
+curl https://ukisai.com/api/flash-next/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "flash-next", "messages": [{"role": "user", "content": "Hello, Swift."}]}'
+~~~
+
+### vLLM
+
+Use a vLLM build with Qwen3.8-Flash-Next support:
+
+~~~bash
+vllm serve ukisai/Swift-Qwen3.8-Flash-Next \
+  --dtype bfloat16 \
+  --tensor-parallel-size 8 \
+  --max-model-len 262144 \
+  --reasoning-parser qwen3 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_xml \
+  --trust-remote-code \
+  --port 8000
+~~~
+
+Reasoning effort is set per request through the chat template, for example
+`"chat_template_kwargs": {"reasoning_effort": "low"}`. `xhigh` is the default; `medium` and `low` are also supported.
+
+### SGLang
+
+Alternatively, use an SGLang build with Qwen3.8-Flash-Next support, such as the
+`lmsysorg/sglang:qwen38flashnext` image:
+
+~~~bash
+sglang serve \
+  --model-path ukisai/Swift-Qwen3.8-Flash-Next \
+  --tp 8 \
+  --context-length 262144 \
+  --mem-fraction-static 0.85 \
+  --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](https://recipes.vllm.ai/Qwen/Qwen3.8-Flash-Next) and
+[SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/Qwen/Qwen3.8-Flash-Next)
+for installation and hardware-specific settings.
+
+### Transformers
+
+Use a Transformers release with Qwen3.8-Flash-Next support:
+
+~~~python
+import torch
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+model_id = "ukisai/Swift-Qwen3.8-Flash-Next"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+~~~
+
+To extend the context to 1,000,000 tokens, follow the YaRN settings on the
+[Qwen3.8-Flash-Next model card](https://huggingface.co/Qwen/Qwen3.8-Flash-Next).
+
+### Optional MTP decoding
+
+The checkpoint includes the base model's one-layer MTP head. To enable self-speculative
+decoding, append the corresponding flags to the server command above:
+
+~~~bash
+# vLLM
+--speculative-config '{"method":"mtp","num_speculative_tokens":1}'
+
+# SGLang
+--speculative-algorithm NEXTN --speculative-num-steps 3 \
+  --speculative-eagle-topk 1 --speculative-num-draft-tokens 4
+~~~
+
+The benchmark scores above were measured without MTP.
+
+## License and access
+
+Swift 1.5 Qwen3.8-Flash-Next is a derivative of [Qwen3.8-Flash-Next](https://huggingface.co/Qwen/Qwen3.8-Flash-Next)
+(Copyright (c) 2026 Qwen, [Qwen Community License 1.0](https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next/blob/main/LICENSE-QWEN)).
+UkisAI's contribution, including the adapted weights, is licensed under the
+**[Swift Open License v1.0](https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next/blob/main/LICENSE)**.
+See [NOTICE](https://huggingface.co/ukisai/Swift-Qwen3.8-Flash-Next/blob/main/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](https://ukisai.com/contact) 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
+
+~~~bibtex
+@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](https://www.nvidia.com/en-us/data-center/innovation-lab/),
+[Amazon Web Services](https://aws.amazon.com/), and
+[Google Cloud](https://cloud.google.com/) for providing compute credits and
+infrastructure support for Swift's development, training, and evaluation.

benchmarks/gpqa-diamond-20260921.json

@@ -0,0 +1,65 @@
+{
+  "model": "ukisai/Swift-Qwen3.8-Flash-Next",
+  "benchmark": "GPQA-Diamond",
+  "completed_utc": "2026-09-21",
+  "protocol": {
+    "questions": 198,
+    "seeds": [
+      0,
+      1,
+      2,
+      3,
+      4
+    ],
+    "responses_per_effort": 990,
+    "output_cap_tokens": 100000,
+    "request_timeout_seconds": 10800,
+    "client_concurrency": 8,
+    "sampling": {
+      "temperature": 1.0,
+      "top_p": 0.95,
+      "top_k": 20,
+      "min_p": 0,
+      "presence_penalty": 0,
+      "repetition_penalty": 1
+    },
+    "serving": "BF16 weights, vLLM (development build), tensor parallelism 8",
+    "scoring": "strict answer match over all responses, truncated responses included",
+    "thinking_tokens": "reasoning tokens per response"
+  },
+  "results": {
+    "xhigh": {
+      "responses": 990,
+      "correct": 887,
+      "accuracy": 0.896,
+      "request_errors": 0,
+      "truncations": 4,
+      "thinking_tokens_mean": 7823.5,
+      "thinking_tokens_median": 2325.0,
+      "thinking_tokens_total": 7745252,
+      "config_sha256": "f79ddb1ab0fe95682cba18beb502a467cdd39d6d4f7b15bfde8c1e2751ff14fc"
+    },
+    "medium": {
+      "responses": 990,
+      "correct": 829,
+      "accuracy": 0.8374,
+      "request_errors": 0,
+      "truncations": 0,
+      "thinking_tokens_mean": 2483.0,
+      "thinking_tokens_median": 1098.0,
+      "thinking_tokens_total": 2458213,
+      "config_sha256": "bdf180430e5453e05893e3123c7b758731547f72cbacbd11edeb9132d8c26c82"
+    },
+    "low": {
+      "responses": 990,
+      "correct": 839,
+      "accuracy": 0.8475,
+      "request_errors": 0,
+      "truncations": 1,
+      "thinking_tokens_mean": 2644.7,
+      "thinking_tokens_median": 1014.5,
+      "thinking_tokens_total": 2618242,
+      "config_sha256": "bd3e52fdb98770eb7545cfb64ed5e37703a9fdbd0479fc7fb3ec79066217a840"
+    }
+  }
+}

chat_template.jinja

@@ -0,0 +1,170 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- set reasoning_instructions = '' %}
+{%- if enable_thinking is undefined or enable_thinking is true %}
+    {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}
+    {%- if resolved_reasoning_effort not in ('xhigh', 'medium', 'low') %}
+        {{- raise_exception('Unexpected reasoning effort ' ~ reasoning_effort ~ '. Supported types are xhigh (default), medium, and low.') }}
+    {%- endif %}
+    {%- if resolved_reasoning_effort == 'xhigh' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.' %}
+    {%- elif resolved_reasoning_effort == 'low' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.' %}
+    {%- endif %}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {%- if reasoning_instructions %}
+        {{- reasoning_instructions + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '<|im_start|>system\n' + (reasoning_instructions + '\n\n' if reasoning_instructions else '')  + content + '<|im_end|>\n' }}
+        {%- elif reasoning_instructions %}
+            {{- '<|im_start|>system\n' + reasoning_instructions + '<|im_end|>\n' }}
+        {%- endif %}
+    {%- elif reasoning_instructions %}
+        {{- '<|im_start|>system\n' + reasoning_instructions + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if preserve_thinking is undefined or preserve_thinking is true or loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined and tool_call.arguments != '' %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,154 @@
+{
+  "architectures": [
+    "Qwen4ExpForConditionalGeneration"
+  ],
+  "image_token_id": 248056,
+  "language_model_only": false,
+  "model_type": "qwen4_exp",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 248044,
+    "dtype": "bfloat16",
+    "eos_token_id": 248044,
+    "full_attention_interval": 4,
+    "hc_count": 4,
+    "hc_lowrank": 320,
+    "head_dim": 256,
+    "heads_per_ngram": 8,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "indexer_budget": 2048,
+    "indexer_compress_ratio": 4,
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+    "num_experts_per_tok": 10,
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+    "output_router_logits": false,
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+  "transformers_version": "5.8.0.dev0",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 27,
+    "hidden_act": "gelu_pytorch_tanh",
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+    "in_channels": 3,
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+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+    "bos_token_id": 248044,
+    "do_sample": true,
+    "eos_token_id": [
+        248046,
+        248044
+    ],
+    "pad_token_id": 248044,
+    "temperature": 1.0,
+    "top_k": 20,
+    "top_p": 0.95
+}

kept_experts.json

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+[transformers] `causal_conv1d_fn` is falling back to its reference PyTorch implementation because `causal_conv1d` is not installed. This is correct but much slower; install `causal_conv1d` for the optimized kernel.
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+[transformers] `causal_conv1d_fn` is falling back to its reference PyTorch implementation because `causal_conv1d` is not installed. This is correct but much slower; install `causal_conv1d` for the optimized kernel.
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+[transformers] `causal_conv1d_fn` is falling back to its reference PyTorch implementation because `causal_conv1d` is not installed. This is correct but much slower; install `causal_conv1d` for the optimized kernel.
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+{"stopped": "SIGTERM", "step": 296, "tokens": 3578973, "best_val_kl": 0.19060634383155653}

training/heal75.log

@@ -0,0 +1,87 @@
+
+{"trainable_tensors": 280, "trainable_params": 48346624, "routers": 48}
+[transformers] `causal_conv1d_fn` is falling back to its reference PyTorch implementation because `causal_conv1d` is not installed. This is correct but much slower; install `causal_conv1d` for the optimized kernel.
+[transformers] `chunk_gated_delta_rule` is falling back to its reference PyTorch implementation because `flash-linear-attention` is not installed. This is correct but much slower; install `flash-linear-attention` for the optimized kernel.
+{"eval": "start", "step": 0, "tokens": 0, "val_kl": 0.13787, "logic": 0.7, "logic_by_family": {"all": 0.7, "sillogismi": 0.9, "proposizionale": 0.75, "ordinamento": 0.65, "cavalieri_furfanti": 0.35, "catene_di_regole": 0.8, "griglia": 0.75}, "best_val_kl": 0.13787, "evals_without_gain": 0, "eval_sec": 360, "quiz_n": 120, "skipped_long": 0, "epoch": 0.0, "gpu_peak_gb": [4.6, 80.2, 78.8, 25.8]}
+[W1002 04:23:35.740489666 CUDACachingAllocator.cpp:3933] memory allocation failed with OOM on device 2 while trying to allocate 304087040 bytes (free: 70844416, total: 101975851008).
+[W1002 04:23:35.265208949 CUDACachingAllocator.cpp:3933] memory allocation failed with OOM on device 2 while trying to allocate 633339904 bytes (free: 320405504, total: 101975851008).
+[W1002 04:23:35.265447038 CUDACachingAllocator.cpp:3933] memory allocation failed with OOM on device 2 while trying to allocate 633339904 bytes (free: 320405504, total: 101975851008).
+Traceback (most recent call last):
+  File "/root/b/heal_swift.py", line 319, in <module>
+    main()
+  File "/root/b/heal_swift.py", line 285, in main
+    (loss / args.accum).backward()
+  File "/usr/local/lib/python3.12/dist-packages/torch/_tensor.py", line 623, in backward
+    torch.autograd.backward(
+  File "/usr/local/lib/python3.12/dist-packages/torch/autograd/__init__.py", line 395, in backward
+    _engine_run_backward(
+  File "/usr/local/lib/python3.12/dist-packages/torch/autograd/graph.py", line 979, in _engine_run_backward
+    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 1183, in unpack_hook
+    _run_fn_with_dynamo_disabled(frame.recompute_fn, *args)
+  File "/usr/local/lib/python3.12/dist-packages/torch/_compile.py", line 54, in inner
+    return disable_fn(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/_dynamo/eval_frame.py", line 1446, in _fn
+    return fn(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 1150, in _run_fn_with_dynamo_disabled
+    return fn(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/utils/checkpoint.py", line 1688, in recompute_fn
+    fn(*args, **kwargs)
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/accelerate/hooks.py", line 192, in new_forward
+    output = module._old_forward(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/root/tenv/lib/python3.12/site-packages/transformers/models/qwen4_exp/modeling_qwen4_exp.py", line 1306, in forward
+    hidden_states = self.mlp(hidden_states)
+                    ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/accelerate/hooks.py", line 192, in new_forward
+    output = module._old_forward(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/root/tenv/lib/python3.12/site-packages/transformers/models/qwen4_exp/modeling_qwen4_exp.py", line 994, in forward
+    expert_output = self.experts(hidden_states_reshaped, selected_experts, routing_weights)
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
+    return self._call_impl(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1789, in _call_impl
+    return forward_call(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/usr/local/lib/python3.12/dist-packages/accelerate/hooks.py", line 192, in new_forward
+    output = module._old_forward(*args, **kwargs)
+             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/root/tenv/lib/python3.12/site-packages/transformers/integrations/moe.py", line 573, in forward
+    return experts_forward(self, *args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/root/tenv/lib/python3.12/site-packages/transformers/integrations/moe.py", line 394, in grouped_mm_experts_forward
+    selected_hidden_states_g = hidden_states[perm // num_top_k]
+                               ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
+torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 604.00 MiB. GPU 2 has a total capacity of 94.97 GiB of which 305.56 MiB is free. Including non-PyTorch memory, this process has 94.66 GiB memory in use. Of the allocated memory 91.53 GiB is allocated by PyTorch, and 2.45 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf)
+
+{"trainable_tensors": 280, "trainable_params": 48346624, "routers": 48}
+{"resumed": 0, "tokens": 0}
+[transformers] `causal_conv1d_fn` is falling back to its reference PyTorch implementation because `causal_conv1d` is not installed. This is correct but much slower; install `causal_conv1d` for the optimized kernel.
+[transformers] `chunk_gated_delta_rule` is falling back to its reference PyTorch implementation because `flash-linear-attention` is not installed. This is correct but much slower; install `flash-linear-attention` for the optimized kernel.
+{"eval": "start", "step": 0, "tokens": 0, "val_kl": 0.13787, "logic": 0.7, "logic_by_family": {"all": 0.7, "sillogismi": 0.9, "proposizionale": 0.75, "ordinamento": 0.65, "cavalieri_furfanti": 0.35, "catene_di_regole": 0.8, "griglia": 0.75}, "best_val_kl": 0.13787, "evals_without_gain": 1, "eval_sec": 349, "quiz_n": 120, "skipped_long": 0, "epoch": 0.0, "gpu_peak_gb": [4.6, 80.2, 78.8, 25.8]}
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+{"step": 5, "tokens": 71910, "kl_ema": 0.23852, "lr_f": 0.491, "tok_s": 48, "min": 25.2}
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+{"step": 10, "tokens": 126860, "kl_ema": 0.1678, "lr_f": 0.914, "tok_s": 46, "min": 46.0}
+
+{"trainable_tensors": 280, "trainable_params": 48346624, "routers": 48}
+{"resumed": 11, "tokens": 145683}
+[transformers] `causal_conv1d_fn` is falling back to its reference PyTorch implementation because `causal_conv1d` is not installed. This is correct but much slower; install `causal_conv1d` for the optimized kernel.

training/heal75/best.pt

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training/heal75/train.log

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training/logic_val_swift.json

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+ "synth/catene_di_regole": 0.84,
+ "synth/catene_di_regole/L1": 0.944,
+ "synth/catene_di_regole/L2": 1.0,
+ "synth/catene_di_regole/L3": 0.789,
+ "synth/catene_di_regole/L4": 0.625,
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+ "synth/cavalieri_furfanti/L2": 0.5,
+ "synth/griglia": 0.64,
+ "synth/griglia/L10": 0.5,
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+ "synth/proposizionale/L2": 0.762,
+ "synth/proposizionale/L3": 0.867,
+ "synth/sillogismi": 0.9,
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+}

training/mask_maximin_0.25.pt

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training/mask_maximin_0.75.pt

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training/stages.log

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+=== start 03:59
+=== swift logic quiz 04:02 {"all":0.747,"synth/catene_di_regole":0.84,"synth/catene_di_
+=== pruned 75 04:04 79G free
+=== e2/heal75 OOM at seq 16384, retrying with a smaller window 04:23

training/stages2.log

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+=== 50: SIGTERM at 3559971 tokens 07:46
+=== stage 50: quiz_all=83.3 logic=0.7 07:54
+=== stage 50 NOT promoted (thresholds quiz>=85.0 logic>=0.72) 07:54
+=== descent ends at 50 (not promoted) 07:54
+=== descent done, publishing stage none 07:54
+tail: cannot open 'publish.log' for reading: No such file or directory
+=== all done:  07:54

video_preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 25165824,
+        "shortest_edge": 4096
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "video_processor_type": "Qwen3VLVideoProcessor"
+}