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alt.freeweights.qwen / Qwen3.5-397B-A17B-GGUF
From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.qwenSubject:

Qwen3.5-397B-A17B-GGUF

Date: 2026-11-15Commit: da33c16Signed: 2DC28CC7B26A283B ✓

Image text to text model. Gguf weights, 7.40 TB in all. Published by unsloth under Apache-2.0.

qwen7.40TBQwen3.5-397B-A17B-GGUFggufpermissive
FactsParamsnot statedSize7.40 TBFormatsggufTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get unsloth/Qwen3.5-397B-A17B-GGUF
# manifest sha256 fbfe39c92f99… · 7.40 TB · 198 files
$ fw verify ./Qwen3.5-397B-A17B-GGUF unsloth/Qwen3.5-397B-A17B-GGUF
From sourceHosted (signed)unsloth/Qwen3.5-397B-A17B-GGUFLast check: 2026-11-15 04:40 UTC
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Overview

Size
7.40 TB
Weights
107 GB (smallest of 29 GGUF variants)
Parameters
not stated
Formats
gguf
Task
image text to text
License
Apache-2.0 (permissive)
Files
198, each with a sha256
Publisher
unsloth
Added
2026-11-15
Section
alt.freeweights.qwen
Lineage

Original release from the publisher.

Base model: Qwen/Qwen3.5-397B-A17B

Where these bytes come from
Publisher at the pinned commit→You

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Model card

From the publisher at commit da33c16. Shown as plain text with links; images open at the publisher.

To run Qwen3.5 locally - Read our Guide!#

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

https://github.com/unslothai/unsloth/ https://discord.gg/unsloth https://docs.unsloth.ai/models/qwen3.5

Disable thinking via --chat-template-kwargs '{"enable_thinking":false}'. Read our guide.

Mar 6 Update:#

  • 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.

Qwen3.5-397B-A17B#

Image: image

Qwen Chat

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, etc.

Tip. For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Alibaba Cloud Model Studio.

In particular, Qwen3.5-Plus is the hosted version corresponding to Qwen3.5-397B-A17B with more production features, e.g., 1M context length by default, official built-in tools, and adaptive tool use. For more information, please refer to the User Guide.

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.

Qwen3.5 Highlights#

Qwen3.5 features the following enhancement:

  • 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.
  • Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
  • Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.
  • Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.
  • Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.

Image: Benchmark Results

For more details, please refer to our blog post Qwen3.5.

Model Overview#

  • Type: Causal Language Model with Vision Encoder
  • Training Stage: Pre-training & Post-training
  • Language Model
    • Number of Parameters: 397B in total and 17B activated
    • Hidden Dimension: 4096
    • Token Embedding: 248320 (Padded)
    • Number of Layers: 60
      • Hidden Layout: 15 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE))
    • Gated DeltaNet:
      • Number of Linear Attention Heads: 64 for V and 16 for QK
      • Head Dimension: 128
    • Gated Attention:
      • Number of Attention Heads: 32 for Q and 2 for KV
      • Head Dimension: 256
      • Rotary Position Embedding Dimension: 64
    • Mixture Of Experts
      • Number of Experts: 512
      • Number of Activated Experts: 10 Routed + 1 Shared
      • Expert Intermediate Dimension: 1024
    • LM Output: 248320 (Padded)
    • MTP: trained with multi-steps
  • Context Length: 262,144 natively and extensible up to 1,010,000 tokens.

Benchmark Results#

Language#

| | GPT5.2 | Claude 4.5 Opus | Gemini-3 Pro | Qwen3-Max-Thinking | K2.5-1T-A32B | Qwen3.5-397B-A17B

| Knowledge

| MMLU-Pro | 87.4 | 89.5 | 89.8 | 85.7 | 87.1 | 87.8

| MMLU-Redux | 95.0 | 95.6 | 95.9 | 92.8 | 94.5 | 94.9

| SuperGPQA | 67.9 | 70.6 | 74.0 | 67.3 | 69.2 | 70.4

| C-Eval | 90.5 | 92.2 | 93.4 | 93.7 | 94.0 | 93.0

| Instruction Following

| IFEval | 94.8 | 90.9 | 93.5 | 93.4 | 93.9 | 92.6

| IFBench | 75.4 | 58.0 | 70.4 | 70.9 | 70.2 | 76.5

| MultiChallenge | 57.9 | 54.2 | 64.2 | 63.3 | 62.7 | 67.6

| Long Context

| AA-LCR | 72.7 | 74.0 | 70.7 | 68.7 | 70.0 | 68.7

| LongBench v2 | 54.5 | 64.4 | 68.2 | 60.6 | 61.0 | 63.2

| STEM

| GPQA | 92.4 | 87.0 | 91.9 | 87.4 | 87.6 | 88.4

| HLE | 35.5 | 30.8 | 37.5 | 30.2 | 30.1 | 28.7

| HLE-Verified¹ | 43.3 | 38.8 | 48 | 37.6 | -- | 37.6

| Reasoning

| LiveCodeBench v6 | 87.7 | 84.8 | 90.7 | 85.9 | 85.0 | 83.6

| HMMT Feb 25 | 99.4 | 92.9 | 97.3 | 98.0 | 95.4 | 94.8

| HMMT Nov 25 | 100 | 93.3 | 93.3 | 94.7 | 91.1 | 92.7

| IMOAnswerBench | 86.3 | 84.0 | 83.3 | 83.9 | 81.8 | 80.9

| AIME26 | 96.7 | 93.3 | 90.6 | 93.3 | 93.3 | 91.3

| General Agent

| BFCL-V4 | 63.1 | 77.5 | 72.5 | 67.7 | 68.3 | 72.9

| TAU2-Bench | 87.1 | 91.6 | 85.4 | 84.6 | 77.0 | 86.7

| VITA-Bench | 38.2 | 56.3 | 51.6 | 40.9 | 41.9 | 49.7

| DeepPlanning | 44.6 | 33.9 | 23.3 | 28.7 | 14.5 | 34.3

| Tool Decathlon | 43.8 | 43.5 | 36.4 | 18.8 | 27.8 | 38.3

| MCP-Mark | 57.5 | 42.3 | 53.9 | 33.5 | 29.5 | 46.1

| Search Agent³

| HLE w/ tool | 45.5 | 43.4 | 45.8 | 49.8 | 50.2 | 48.3

| BrowseComp | 65.8 | 67.8 | 59.2 | 53.9 | --/74.9 | 69.0/78.6

| BrowseComp-zh | 76.1 | 62.4 | 66.8 | 60.9 | -- | 70.3

| WideSearch | 76.8 | 76.4 | 68.0 | 57.9 | 72.7 | 74.0

| Seal-0 | 45.0 | 47.7 | 45.5 | 46.9 | 57.4 | 46.9

| Multilingualism

| MMMLU | 89.5 | 90.1 | 90.6 | 84.4 | 86.0 | 88.5

| MMLU-ProX | 83.7 | 85.7 | 87.7 | 78.5 | 82.3 | 84.7

| NOVA-63 | 54.6 | 56.7 | 56.7 | 54.2 | 56.0 | 59.1

| INCLUDE | 87.5 | 86.2 | 90.5 | 82.3 | 83.3 | 85.6

| Global PIQA | 90.9 | 91.6 | 93.2 | 86.0 | 89.3 | 89.8

| PolyMATH | 62.5 | 79.0 | 81.6 | 64.7 | 43.1 | 73.3

| WMT24++ | 78.8 | 79.7 | 80.7 | 77.6 | 77.6 | 78.9

| MAXIFE | 88.4 | 79.2 | 87.5 | 84.0 | 72.8 | 88.2

| Coding Agent

| SWE-bench Verified | 80.0 | 80.9 | 76.2 | 75.3 | 76.8 | 76.4

| SWE-bench Multilingual | 72.0 | 77.5 | 65.0 | 66.7 | 73.0 | 69.3

| SecCodeBench | 68.7 | 68.6 | 62.4 | 57.5 | 61.3 | 68.3

| Terminal Bench 2 | 54.0 | 59.3 | 54.2 | 22.5 | 50.8 | 52.5

  • HLE-Verified: a verified and revised version of Humanity’s Last Exam (HLE), accompanied by a transparent, component-wise verification protocol and a fine-grained error taxonomy. We open-source the dataset at https://huggingface.co/datasets/skylenage/HLE-Verified.
  • TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.
  • MCPMark: GitHub MCP server uses v0.30.3 from api.githubcopilot.com; Playwright tool responses are truncated at 32k tokens.
  • Search Agent: most search agents built on our model adopt a simple context-folding strategy(256k): once the cumulative Tool Response length reaches a preset threshold, earlier Tool Responses are pruned from the history to keep the context within limits.
  • BrowseComp: we tested two strategies, simple context-folding achieved a score of 69.0, while using the same discard-all strategy as DeepSeek-V3.2 and Kimi K2.5 achieved 78.6.
  • WideSearch: we use a 256k context window without any context management.
  • MMLU-ProX: we report the averaged accuracy on 29 languages.
  • WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.
  • MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).
  • Empty cells (--) indicate scores not yet available or not applicable.
Vision Language#

| | GPT5.2 | Claude 4.5 Opus | Gemini-3 Pro | Qwen3-VL-235B-A22B | K2.5-1T-A32B | Qwen3.5-397B-A17B

| STEM and Puzzle

| MMMU | 86.7 | 80.7 | 87.2 | 80.6 | 84.3 | 85.0

| MMMU-Pro | 79.5 | 70.6 | 81.0 | 69.3 | 78.5 | 79.0

| MathVision | 83.0 | 74.3 | 86.6 | 74.6 | 84.2 | 88.6

| Mathvista(mini) | 83.1 | 80.0 | 87.9 | 85.8 | 90.1 | 90.3

| We-Math | 79.0 | 70.0 | 86.9 | 74.8 | 84.7 | 87.9

| DynaMath | 86.8 | 79.7 | 85.1 | 82.8 | 84.4 | 86.3

| ZEROBench | 9 | 3 | 10 | 4 | 9 | 12

| ZEROBench_sub | 33.2 | 28.4 | 39.0 | 28.4 | 33.5 | 41.0

| BabyVision | 34.4 | 14.2 | 49.7 | 22.2 | 36.5 | 52.3/43.3

| General VQA

| RealWorldQA | 83.3 | 77.0 | 83.3 | 81.3 | 81.0 | 83.9

| MMStar | 77.1 | 73.2 | 83.1 | 78.7 | 80.5 | 83.8

| HallusionBench | 65.2 | 64.1 | 68.6 | 66.7 | 69.8 | 71.4

| MMBenchEN-DEV-v1.1 | 88.2 | 89.2 | 93.7 | 89.7 | 94.2 | 93.7

| SimpleVQA | 55.8 | 65.7 | 73.2 | 61.3 | 71.2 | 67.1

| Text Recognition and Document Understanding

| OmniDocBench1.5 | 85.7 | 87.7 | 88.5 | 84.5 | 88.8 | 90.8

| CharXiv(RQ) | 82.1 | 68.5 | 81.4 | 66.1 | 77.5 | 80.8

| MMLongBench-Doc | -- | 61.9 | 60.5 | 56.2 | 58.5 | 61.5

| CC-OCR | 70.3 | 76.9 | 79.0 | 81.5 | 79.7 | 82.0

| AI2D_TEST | 92.2 | 87.7 | 94.1 | 89.2 | 90.8 | 93.9

| OCRBench | 80.7 | 85.8 | 90.4 | 87.5 | 92.3 | 93.1

| Spatial Intelligence

| ERQA | 59.8 | 46.8 | 70.5 | 52.5 | -- | 67.5

| CountBench | 91.9 | 90.6 | 97.3 | 93.7 | 94.1 | 97.2

| RefCOCO(avg) | -- | -- | 84.1 | 91.1 | 87.8 | 92.3

| ODInW13 | -- | -- | 46.3 | 43.2 | -- | 47.0

| EmbSpatialBench | 81.3 | 75.7 | 61.2 | 84.3 | 77.4

The publisher's README is longer; it is cut here. Read the rest at the source.

Files

198 files · 7.40 TBWeights 107 GB (smallest of 29 GGUF variants). Sizes and sha256 from the signed manifest.
Show all 198 files with their sha256
FileSizesha256
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UD-Q5_K_M/Qwen3.5-397B-A17B-UD-Q5_K_M-00005-of-00008.gguf48.6 GBf46a18407d0b17ed48758af4cd765ea2d5a6931a6384dc5b1417a2ca293c07a6
UD-Q5_K_M/Qwen3.5-397B-A17B-UD-Q5_K_M-00006-of-00008.gguf48.6 GBdd7b94ac3fd1e7a7de01abad02fd3767062d41f3ec7c08ad67c3f1d61c9c7262
UD-Q5_K_M/Qwen3.5-397B-A17B-UD-Q5_K_M-00007-of-00008.gguf48.6 GB29c6d01ef67357fc085a8b812a5730f2d0a046b295752501ea1bc44cb419ef33
UD-Q5_K_M/Qwen3.5-397B-A17B-UD-Q5_K_M-00008-of-00008.gguf1.48 GB4bbade915bb05436822b36a088710a90d9da5a7b0f2a5621a978b79a366be35f
UD-Q5_K_S/Qwen3.5-397B-A17B-UD-Q5_K_S-00001-of-00007.gguf10.9 MB27c8b892a53507665e08d101e65cece62546dcb4babd851aae8d18aa3a369417
UD-Q5_K_S/Qwen3.5-397B-A17B-UD-Q5_K_S-00002-of-00007.gguf49.5 GB68e26bae50ff9594199f513cde6a66271809898daed20f0108a5f440f703035b
UD-Q5_K_S/Qwen3.5-397B-A17B-UD-Q5_K_S-00003-of-00007.gguf48.8 GB2a24f08ec3f1b2061e61a7341b92481fb97d03fed83eb394b848e33914a018b6
UD-Q5_K_S/Qwen3.5-397B-A17B-UD-Q5_K_S-00004-of-00007.gguf48.7 GB22b4c44135277d3ec4789a1c1e490f7d520f1a9b626af32581803099e355c1c9
UD-Q5_K_S/Qwen3.5-397B-A17B-UD-Q5_K_S-00005-of-00007.gguf48.8 GB4a003a82803dde00244ea551617a08e86892786861f1887d774ff4ddb928ca79
UD-Q5_K_S/Qwen3.5-397B-A17B-UD-Q5_K_S-00006-of-00007.gguf48.8 GB2df9d263069942da687792445583621425b05a47d35b53c7478fb3c802fd3305
UD-Q5_K_S/Qwen3.5-397B-A17B-UD-Q5_K_S-00007-of-00007.gguf31.9 GB0b67c6c06cbf0227ffe9ab8d3453808fe5ecb17676bb2a6e48a9a156e48da357
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00001-of-00008.gguf10.9 MBc82146df8eca3ee9611f9ed1925a19ab7ec32f69c4695ed66e1ed87233cd6a79
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00002-of-00008.gguf49.2 GBed30d3bf537c22dd834f7eeeebc8d6214404c3d56445616c55fdd66e0a21a988
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00003-of-00008.gguf48.6 GB612ed2bc62562b096ffec5ba25a51c46a575043c01a33597064b438a38a935a1
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00004-of-00008.gguf49.8 GBc5444e6177855f5297d4e25f94e465ffcc2d8aa01d355230723e5e66a35b91b1
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00005-of-00008.gguf48.6 GBf46a18407d0b17ed48758af4cd765ea2d5a6931a6384dc5b1417a2ca293c07a6
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00006-of-00008.gguf48.6 GBdd7b94ac3fd1e7a7de01abad02fd3767062d41f3ec7c08ad67c3f1d61c9c7262
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00007-of-00008.gguf48.6 GB29c6d01ef67357fc085a8b812a5730f2d0a046b295752501ea1bc44cb419ef33
UD-Q5_K_XL/Qwen3.5-397B-A17B-UD-Q5_K_XL-00008-of-00008.gguf1.48 GB4bbade915bb05436822b36a088710a90d9da5a7b0f2a5621a978b79a366be35f
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00001-of-00008.gguf10.9 MB0a409159a8b206ef81bdd5736ae6bcc300445ed1adcb7e3faaf9167a8fb79f07
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00002-of-00008.gguf49.0 GB63aa2ef1048a36faaba2ebb7d320c6bab88eaf9635e20c30964d2b15315cfc09
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00003-of-00008.gguf48.7 GBc874919cd6ba194d3b259e173faad5e41f989168492477d783c3594d3994f7ba
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00004-of-00008.gguf48.7 GB8a21d496e0b16e85eb74ea679053055a4579a658ffc3e6c00f7875e76b88c4ce
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00005-of-00008.gguf48.7 GBe327fdc4663890a488858d142af6c477f1c060bbbeb4268c8db6e49983cd41d4
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00006-of-00008.gguf48.6 GBef68b262459a4304d371bd503016de9cf3277eb971e18a073203c4fe10345a45
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00007-of-00008.gguf48.7 GB4f28a8907d17a21aa670c0a78403d24a3e52a5d5ee5a65987e54a6742b63bbc0
UD-Q6_K/Qwen3.5-397B-A17B-UD-Q6_K-00008-of-00008.gguf34.2 GB2092241676533c799862ec78d4035af4402907df5822fac13f4109584336a214
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00001-of-00009.gguf10.9 MB7a43f55accd11f00458ae299266fe71467800ff44e8f6737f88730d247d6f688
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00002-of-00009.gguf49.8 GB6fac2ecdccb2f5d33a8e4e685f779f33ef5c511f821d63e957f9692ed1e99504
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00003-of-00009.gguf49.9 GB1377c722f67b31071065a79b1b4974c0495bf9c4b680077cf59e64e582105260
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00004-of-00009.gguf49.4 GBdf6e595342804c2d2a3209bc25d342e7a325c407d93f1a390a1fc8d52ec522a8
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00005-of-00009.gguf49.0 GB0cbee928a3760c44d584fe1c88e9310ed132e3b52e13fdeaec2d2e3c3b764ad9
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00006-of-00009.gguf49.4 GB5750dea38ee359d0b465c6c1c59a2137c7e0e42d5d0a713074ea8da4fc56c059
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00007-of-00009.gguf49.5 GBff566b82c86a356284cd6863bb28617656454a86901ce675de4438dd9f18bf2e
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00008-of-00009.gguf49.9 GBaa81fa77ef35532c556d5ea00397ed943505c993b87b7846d2d4586bc1246e0f
UD-Q6_K_XL/Qwen3.5-397B-A17B-UD-Q6_K_XL-00009-of-00009.gguf15.5 GBd3bda566119958dafead0c385a5499d4af8da0a4080a4a9887c830b71f475530
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00001-of-00010.gguf10.9 MBba984516e0fb4000dc2d097bed53add6ecf615930ac4a3da89e351fc23abf17a
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00002-of-00010.gguf48.8 GBdf4b2afb37fa08b1b02bf73d07a249348c3f22f46b9adab84c35cab9501c4233
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00003-of-00010.gguf48.9 GB73aadfcaeec39742911ed32db1e03b0134bf6ddb3e9694d9492f6ce99b01c8a6
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00004-of-00010.gguf48.9 GBedd92efb11a60eb76c4b856811064576f4631a37b8b0359c5f38d12a5f02791b
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00005-of-00010.gguf48.8 GB28fc8da8fae64ee5e78859aa208922ed141c4093711cb6a3f43227753b28ca91
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00006-of-00010.gguf48.2 GB920ddb1a4fa37a5338f77885a11c3a4d1b7a970052c6c43d323d4fd246ef1ff0
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00007-of-00010.gguf48.9 GB6cc49fd77ef7fa88e26820eca0c1e6c5b46ece6068784365853606512764210d
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00008-of-00010.gguf49.0 GB7655b605b2ca5bd6f8c32274f48abcdce830789170eff80c48ac00113f7ad13a
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00009-of-00010.gguf48.9 GBeb606a18985361adb9f7fa3e54d87be01d53525a34414edc41353595d8e24f97
UD-Q8_K_XL/Qwen3.5-397B-A17B-UD-Q8_K_XL-00010-of-00010.gguf37.3 GBb4ecbbec353c6b6bba6d04d18bac218e832937d9cb833c1c06faa20a09649ef9
config.json3.74 KBe8fb6d966535b12cd42deba495219724f16584f201d3946987eff0279a2d7389
imatrix_unsloth.gguf_file1.14 GBa7bd06bbf0e97592a336f9dd91d857e7bea1b8eb8ed657fa18a33c477cbb6ae6
mmproj-BF16.gguf922 MBb3624272d7b9b49ffe6c6d0c592980bed6b026ce59cde11708bb230395c2a227
mmproj-F16.gguf918 MB906de10b2bec29942a8e2003397fa2348cbb5c47b24efc4f6e224529a2d96404
mmproj-F32.gguf1.82 GBe47df150363dd9d53b4ddf01e5477a6803f7fc2d2e0341064dcf39511ad5f110
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
da33c16fa4 pinned now2026-11-15 04:40 UTC198 files · 7.40 TB

Use it

$ fw get unsloth/Qwen3.5-397B-A17B-GGUF
# manifest sha256 fbfe39c92f99… · 7.40 TB · 198 files
$ fw verify ./Qwen3.5-397B-A17B-GGUF unsloth/Qwen3.5-397B-A17B-GGUF
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-397B-A17B-GGUF.json --dir ./Qwen3.5-397B-A17B-GGUF $ python3 fw.py verify ./Qwen3.5-397B-A17B-GGUF ./unsloth__Qwen3.5-397B-A17B-GGUF.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Qwen3.5-397B-A17B-GGUF --revision da33c16fa4440f831149fcf53b98a22bc07785e5 --local-dir ./Qwen3.5-397B-A17B-GGUF
llama.cppDownload one GGUF variant (UD-IQ1_M, 107 GB), then serve it.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Qwen3.5-397B-A17B-GGUF --include "UD-IQ1_M/*" --local-dir ./Qwen3.5-397B-A17B-GGUF $ llama-server -m ./Qwen3.5-397B-A17B-GGUF/UD-IQ1_M/Qwen3.5-397B-A17B-UD-IQ1_M-00001-of-00004.gguf
OllamaMake an Ollama model from the downloaded file.
$ printf 'FROM ./Qwen3.5-397B-A17B-GGUF/UD-IQ1_M/Qwen3.5-397B-A17B-UD-IQ1_M-00001-of-00004.gguf\n' > Modelfile $ ollama create qwen3.5-397b-a17b-gguf -f Modelfile

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
9bff92dac4457e083e87c738e125f288a8c848816d13aac3c65a7ab5189af617
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.qwenSubject: Qwen3.5-397B-A17B-GGUFDate: 2026-11-15Commit: da33c16Signed: 2DC28CC7B26A283B ✓
Upstream
unsloth/Qwen3.5-397B-A17B-GGUF
Pinned commit
da33c16fa4440f831149fcf53b98a22bc07785e5
Manifest
Qwen3.5-397B-A17B-GGUF.json · .minisig
Manifest sha256
fbfe39c92f994ac74ba25a2bf295897b5a4954edac1b09cd08665593a829c676
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:40 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-397B-A17B-GGUF.json $ curl -O https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-397B-A17B-GGUF.json.minisig $ minisign -Vm Qwen3.5-397B-A17B-GGUF.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

Qwen/Qwen3.5-397B-A17B · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Qwen3.5-397B-A17B-GGUF
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. Qwen3.5-397B-A17B-GGUF.json. Provenance. Takedown history: none.
Source
huggingface.co/unsloth/Qwen3.5-397B-A17B-GGUF

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