Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. - You can now also fine-tune the model locally with Unsloth. - Read our Qwen3.5 fine-tuning guide here. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. > > In light of its parameter scale, the intended use cases are prototyping, task-specific fine-tuning, and other research or development purposes.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. NEW: MTP speculative decoding for ~1.5-2x faster generation - build llama.cpp from the MTP PR branch - has been merged 16th May 2026! > **Note:** llama.cpp renamed `--spec-type mtp` to `--spec-type draft-mtp` on 2026-05-13
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
分類準備中
入力 未確認→ 出力 未確認
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Disable thinking via `--chat-template-kwargs '{"enable_thinking":false}'`. Read our guide. - All GGUFs now use our new imatrix data. See some improvements in chat, coding, long context, and tool-calling use-cases. - GGUFs now updated with an improved quantization algorithm. - Rest of variants like Q8_0, Q4_K_M, BF16 are now uploaded. - See our new benchmarks for 122B-A10B here.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
NEW: MTP speculative decoding for ~1.5-2x faster generation - build llama.cpp from the MTP PR branch - has been merged 16th May 2026! Developer Role Support so Qwen3.5 can work in Codex, OpenCode and more! Tool calling improvements: Makes parsing nested objects to make tool calling succeed more. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Disable thinking via `--chat-template-kwargs '{"enable_thinking":false}'`. Read our guide. - All GGUFs now use our new imatrix data. See some improvements in chat, coding, long context, and tool-calling use-cases. - GGUFs now updated with an improved quantization algorithm. - See our new benchmarks for Qwen3.5 here. ---
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 テキスト · 画像→ 出力 テキスト
> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
> [!Note] > This repository contains model weights and configuration files for the pre-trained only model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. > > The intended use cases are fine-tuning, in-context learning experiments, and other research or development purposes, not direct interaction. > However, the control tokens, e.g., ` ` and ` ` were trained to allow efficient LoRA-style PEFT with the official chat template, mitigating the need to finetune embeddings, a significant optimization given Qwen3.5's larger vocabulary. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. - You can now also fine-tune the model locally with Unsloth. - Read our Qwen3.5 fine-tuning guide here. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. NEW: MTP speculative decoding for ~1.5-2x faster generation - build llama.cpp from the MTP PR branch - has been merged 16th May 2026! > **Note:** llama.cpp renamed `--spec-type mtp` to `--spec-type draft-mtp` on 2026-05-13
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト · 画像→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. - You can now also fine-tune the model locally with Unsloth. - Read our Qwen3.5 fine-tuning guide here. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
翻訳待ち・原文を表示
入力 テキスト→ 出力 テキスト
Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. NEW: MTP speculative decoding for ~1.5-2x faster generation - build llama.cpp from the MTP PR branch - has been merged 16th May 2026! > **Note:** llama.cpp renamed `--spec-type mtp` to `--spec-type draft-mtp` on 2026-05-13
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 テキスト · 画像→ 出力 テキスト
Developer Role Support so Qwen3.6 can work in Codex, OpenCode and more! Qwen3.6 can now be run and fine-tuned in Unsloth Studio . Read our guide . Tool calling improvements: Makes parsing nested objects to make tool calling succeed more. Example of Qwen3.6 35B-A3B (4-bit GGUF) running in Unsloth Studio with tool-calling: > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience.
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 テキスト · 画像→ 出力 テキスト
MTP enables ~1.5-2x faster inference with no accuracy loss. You can now run Qwen3.6 MTP GGUFs in Unsloth Studio Unsloth Studio auto sets the ideal MTP settings for your hardware (Mac, CPU, GPU): Set `-DGGML_CUDA=OFF` for CPU/Metal. `-np > 1` and `--mmproj` are not yet supported with MTP. --- - Developer Role Support so Qwen3.6 can work in Codex , OpenCode and more! - Qwen3.6 can now be run and fine-tuned in Unsloth Studio . Read our guide . - Tool calling improvements: Makes parsing nested objects to make tool calling succeed more. ---
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
* **2.5x faster** throughput than other NVFP4 quants. * This is an Unsloth NVFP4 quantized checkpoint calibrated on a mixture of our Unsloth dataset + UltraChat dataset. * Works on a 24GB VRAM GPU. Benchmarks on 1xB200 128 concurrency. For accuracy benchmarks, we conducted MMLU-Pro, AIME 2025, GPQA for FP8, BF16, NVIDIA's NVFP4 and our NVFP4s - we show our faster quants do similarly on all: Read all benchmarks in our NVFP4 blog
翻訳待ち・原文を表示
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
入力 テキスト · 画像→ 出力 テキスト
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
入力 テキスト · 画像→ 出力 テキスト
