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alt.freeweights.qwen / Qwen3.5-122B-A10B-MTP-GGUF
From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.qwenSubject:

Qwen3.5-122B-A10B-MTP-GGUF

Date: 2026-11-15Commit: 907becbSigned: 2DC28CC7B26A283B ✓

Image text to text model. Gguf weights, 1.82 TB in all. Published by unsloth under Apache-2.0.

qwen1.82TBQwen3.5-122B-A10B-MTP-GGUFggufpermissive
FactsParamsnot statedSize1.82 TBFormatsggufTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get unsloth/Qwen3.5-122B-A10B-MTP-GGUF
# manifest sha256 f691bb29440f… · 1.82 TB · 68 files
$ fw verify ./Qwen3.5-122B-A10B-MTP-GGUF unsloth/Qwen3.5-122B-A10B-MTP-GGUF
From sourceHosted (signed)unsloth/Qwen3.5-122B-A10B-MTP-GGUFLast check: 2026-11-15 04:43 UTC
Run at homeGet help

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Quantized of Qwen3.5-122B-A10B.

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Overview

Size
1.82 TB
Weights
38.7 GB (smallest of 22 GGUF variants)
Parameters
not stated
Formats
gguf
Task
image text to text
License
Apache-2.0 (permissive)
Files
68, each with a sha256
Publisher
unsloth
Added
2026-11-15
Section
alt.freeweights.qwen
Lineage

Original release from the publisher.

Base model: Qwen/Qwen3.5-122B-A10B

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

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
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From sourcehuggingface.co2026-11-15 04:43 UTCpassed

Model card

From the publisher at commit 907becb. Shown as plain text with links; images open at the publisher.

Read our How to Run Qwen3.5 MTP Guide!#

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://unsloth.ai/docs/models/qwen3.5

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

apt-get update
apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build \
    -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
cp llama.cpp/build/bin/llama-* llama.cpp
export LLAMA_CACHE="unsloth/Qwen3.5-122B-A10B-MTP-GGUF"
./llama.cpp/llama-server \
    -hf unsloth/Qwen3.5-122B-A10B-MTP-GGUF:UD-Q4_K_XL \
    -ngl 99 -c 8192 -fa on -np 1 \
    --spec-type draft-mtp --spec-draft-n-max 6

Set -DGGML_CUDA=OFF for CPU/Metal. -np > 1 and --mmproj are not yet supported with MTP.

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.

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

Mar 5 'Final' Update:#

  • All GGUFs now use our new imatrix data. See some improvements in chat, coding, long context, and tool-calling use-cases.
  • GGUFs updated with an improved quantization algorithm.
  • Updated with fixed chat template for improved tool-calling & coding performance!
  • See our new benchmarks for 122B-A10B here.

Image: 122b final

Please follow the correct instructions / settings in our guide here.

Fine-tuning and RL Qwen3.5#

Qwen3.5-122B-A10B#

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, 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.

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: 122B in total and 10B activated
    • Hidden Dimension: 3072
    • Token Embedding: 248320 (Padded)
    • Number of Layers: 48
    • Hidden Layout: 16 × (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: 256
      • Number of Activated Experts: 8 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#

| | GPT-5-mini 2025-08-07 | GPT-OSS-120B | Qwen3-235B-A22B | Qwen3.5-122B-A10B | Qwen3.5-27B | Qwen3.5-35B-A3B

| Knowledge

| MMLU-Pro | 83.7 | 80.8 | 84.4 | 86.7 | 86.1 | 85.3

| MMLU-Redux | 93.7 | 91.0 | 93.8 | 94.0 | 93.2 | 93.3

| C-Eval | 82.2 | 76.2 | 92.1 | 91.9 | 90.5 | 90.2

| SuperGPQA | 58.6 | 54.6 | 64.9 | 67.1 | 65.6 | 63.4

| Instruction Following

| IFEval | 93.9 | 88.9 | 87.8 | 93.4 | 95.0 | 91.9

| IFBench | 75.4 | 69.0 | 51.7 | 76.1 | 76.5 | 70.2

| MultiChallenge | 59.0 | 45.3 | 50.2 | 61.5 | 60.8 | 60.0

| Long Context

| AA-LCR | 68.0 | 50.7 | 60.0 | 66.9 | 66.1 | 58.5

| LongBench v2 | 56.8 | 48.2 | 54.8 | 60.2 | 60.6 | 59.0

| STEM & Reasoning

| HLE w/ CoT | 19.4 | 14.9 | 18.2 | 25.3 | 24.3 | 22.4

| GPQA Diamond | 82.8 | 80.1 | 81.1 | 86.6 | 85.5 | 84.2

| HMMT Feb 25 | 89.2 | 90.0 | 85.1 | 91.4 | 92.0 | 89.0

| HMMT Nov 25 | 84.2 | 90.0 | 89.5 | 90.3 | 89.8 | 89.2

| Coding

| SWE-bench Verified | 72.0 | 62.0 | -- | 72.0 | 72.4 | 69.2

| Terminal Bench 2 | 31.9 | 18.7 | -- | 49.4 | 41.6 | 40.5

| LiveCodeBench v6 | 80.5 | 82.7 | 75.1 | 78.9 | 80.7 | 74.6

| CodeForces | 2160 | 2157 | 2146 | 2100 | 1899 | 2028

| OJBench | 40.4 | 41.5 | 32.7 | 39.5 | 40.1 | 36.0

| FullStackBench en | 30.6 | 58.9 | 61.1 | 62.6 | 60.1 | 58.1

| FullStackBench zh | 35.2 | 60.4 | 63.1 | 58.7 | 57.4 | 55.0

| General Agent

| BFCL-V4 | 55.5 | -- | 54.8 | 72.2 | 68.5 | 67.3

| TAU2-Bench | 69.8 | -- | 58.5 | 79.5 | 79.0 | 81.2

| VITA-Bench | 13.9 | -- | 31.6 | 33.6 | 41.9 | 31.9

| DeepPlanning | 17.9 | -- | 17.1 | 24.1 | 22.6 | 22.8

| Search Agent

| HLE w/ tool | 35.8 | 19.0 | -- | 47.5 | 48.5 | 47.4

| Browsecomp | 48.1 | 41.1 | -- | 63.8 | 61.0 | 61.0

| Browsecomp-zh | 49.5 | 42.9 | -- | 69.9 | 62.1 | 69.5

| WideSearch | 47.2 | 40.4 | -- | 60.5 | 61.1 | 57.1

| Seal-0 | 34.2 | 45.1 | -- | 44.1 | 47.2 | 41.4

| Multilingualism

| MMMLU | 86.2 | 78.2 | 83.4 | 86.7 | 85.9 | 85.2

| MMLU-ProX | 78.5 | 74.5 | 77.9 | 82.2 | 82.2 | 81.0

| NOVA-63 | 51.9 | 51.1 | 55.4 | 58.6 | 58.1 | 57.1

| INCLUDE | 81.8 | 74.0 | 81.0 | 82.8 | 81.6 | 79.7

| Global PIQA | 88.5 | 84.1 | 85.7 | 88.4 | 87.5 | 86.6

| PolyMATH | 67.3 | 54.0 | 60.1 | 68.9 | 71.2 | 64.4

| WMT24++ | 80.7 | 74.4 | 75.8 | 78.3 | 77.6 | 76.3

| MAXIFE | 85.3 | 83.7 | 83.2 | 87.9 | 88.0 | 86.6

  • CodeForces: evaluated on our own query set.
  • 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.
  • 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.
  • 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#

| | GPT-5-mini 2025-08-07 | Claude-Sonnet-4.5 | Qwen3-VL-235B-A22B | Qwen3.5-122B-A10B | Qwen3.5-27B | Qwen3.5-35B-A3B

| STEM and Puzzle

| MMMU | 79.0 | 79.6 | 80.6 | 83.9 | 82.3 | 81.4

| MMMU-Pro | 67.3 | 68.4 | 69.3 | 76.9 | 75.0 | 75.1

| MathVision | 71.9 | 71.1 | 74.6 | 86.2 | 86.0 | 83.9

| Mathvista(mini) | 79.1 | 79.8 | 85.8 | 87.4 | 87.8 | 86.2

| DynaMath | 81.4 | 78.8 | 82.8 | 85.9 | 87.7 | 85.0

| ZEROBench | 3 | 4 | 4 | 9 | 10 | 8

| ZEROBench_sub | 27.3 | 26.3 | 28.4 | 36.2 | 36.2 | 34.1

| VlmsAreBlind | 75.8 | 85.5 | 79.5 | 96.7 | 96.9 | 97.0

| BabyVision | 20.9 | 18.6 | 22.2 | 40.2 / 34.5 | 44.6 / 34.8 | 38.4 / 29.6

| General VQA

| RealWorldQA | 79.0 | 70.3 | 81.3 | 85.1 | 83.7 | 84.1

| MMStar | 74.1 | 73.8 | 78.7 | 82.9 | 81.0 | 81.9

| MMBenchEN-DEV-v1.1 | 86.8 | 88.3 | 89.7 | 92.8 | 92.6 | 91.5

| SimpleVQA | 56.8 | 57.6 | 61.3 | 61.7 | 56.0 | 58.3

| HallusionBench | 63.2 | 59.9 | 66.7 | 67.6 | 70.0 | 67.9

| Text Recognition and Document Understanding

| OmniDocBench1.5 | 77.0 | 85.8 | 84.5 | 89.8 | 88.9 | 89.3

| CharXiv(RQ) | 68.6 | 67.2 | 66.1 | 77.2 | 79.5 | 77.5

| MMLongBench-Doc | 50.3 | -- | 56.2 | 59.0 | 60.2 | 59.5

| CC-OCR | 70.8 | 68.1 | 81.5 | 81.8 | 81.0 | 80.7

| AI2D_TEST | 88.2 | 87.0 | 89.2 | 93.3 | 92.9 | 92.6

| OCRBench | 82.1 | 76.6 | 87.5 | 92.1 | 89.4 | 91.0

| Spatial Intelligence

| ERQA | 54.0 | 45.0 | 52.5 | 62.0 | 60.5 | 64.8

| CountBench | 91.0 | 90.0 | 93.7 | 97.0 | 97.8 | 97.8

| RefCOCO(avg) | -- | -- | 91.1 | 91.3 | 90.9 | 89.2

| ODInW13 | -- | -- | 43.2 | 44.5 | 41.1 | 42.6

| EmbSpatialBench | 80.7 | 71.8 | 84.3 | 83.9 | 84.5 | 83.1

| RefSpatialBench | 9.0 | 2.2 | 69.9 | 69.3 | 67.7 | 63.5

| LingoQA | 62.4 | 12.8 | 66.8 | 80.8 | 82.0 | 79.2

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

Files

68 files · 1.82 TBWeights 38.7 GB (smallest of 22 GGUF variants). Sizes and sha256 from the signed manifest.
Show all 68 files with their sha256
FileSizesha256
.gitattributes11.5 KB18f9f2d675615aec64fab8908f32a11bd14cdb5b59dfbb5d922e0c4e28d64e6d
BF16/Qwen3.5-122B-A10B-BF16-00001-of-00005.gguf49.9 GBbfe3d06aa7acee9d47a1b9ecbfa532878b6b93c0a1aa018062f0fda31a3f5db1
BF16/Qwen3.5-122B-A10B-BF16-00002-of-00005.gguf49.9 GB34ed397f681064f00ac58bf2ec6164456ea4659f657c8b21b9ab761e13b05515
BF16/Qwen3.5-122B-A10B-BF16-00003-of-00005.gguf49.9 GB08fd9af85a827d82dd434eb624defd15665763ffd3a603b866c71d32952e2058
BF16/Qwen3.5-122B-A10B-BF16-00004-of-00005.gguf49.9 GB1271c5d1f1565f6484fc7bd785e79c2b05fb25f0e1afde8065bf252fa6962e6c
BF16/Qwen3.5-122B-A10B-BF16-00005-of-00005.gguf49.6 GB3b09eefebc5c8f52acced86944dc16b5948cd78df34b6393bd68f008db9647d9
MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf10.9 MBefb412774b244f5ca568b858f6a803fa992dc592048b821e0f479d78dc114106
MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00002-of-00003.gguf49.7 GB96fb38db6c9904cd72f7775667c999c630b4e5b80fe45dda36a3de7222a125d3
MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00003-of-00003.gguf26.6 GBc75eeacf1938c061ad5bce3e5a2ddb0ef9baa4a761c2c233e127cbfe9ccbb6bf
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Q8_0/Qwen3.5-122B-A10B-Q8_0-00003-of-00004.gguf49.8 GB21fda0065cdba3f7592bbeb045175501a0ce3f32b6cbc9a48ff632f531e5f285
Q8_0/Qwen3.5-122B-A10B-Q8_0-00004-of-00004.gguf33.0 GB75ffd668c43b72bc14a3a076e09776b1f93b69aee9969480ed6bf1a44107ae40
Qwen3.5-122B-A10B-UD-IQ1_M.gguf38.7 GB1eb02d5ded051849dab202aa8fe6b0d76aca5c31879b023885c5f7f6d490bf60
Qwen3.5-122B-A10B-UD-IQ2_M.gguf40.4 GB744dd2373bb4616def73cdd97eb3401dc7c64027762a3f178b1de00d42f2e9cd
Qwen3.5-122B-A10B-UD-IQ2_XXS.gguf40.3 GBb784fb2ec9fadd88387eb3797689198e656643b98612d0fd3ecce528a9f91b04
Qwen3.5-122B-A10B-UD-IQ3_XXS.gguf47.8 GB998a8924ff6a12661b60b898553f1771ed1c22c8bfe75e5c8a6f47d01776becc
Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf42.8 GB64a27623aeba350feb3894cfbc05e5b5aae2cf7a0fd44d356b221a6f29ea42e7
README.md96.5 KB18338c975d7162864a8828978546c8f492339a104cd4f28e95274b831695447c
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UD-IQ3_S/Qwen3.5-122B-A10B-UD-IQ3_S-00002-of-00003.gguf50.0 GB83a989e82ad8e7ec88f8bc57d065f86acdada578e446a64b5264ef318ba2d2a0
UD-IQ3_S/Qwen3.5-122B-A10B-UD-IQ3_S-00003-of-00003.gguf2.03 GB6e7610515907b87e7916f368ffa1b7cf1a00ddd6cd615c514b87783454445308
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UD-IQ4_NL/Qwen3.5-122B-A10B-UD-IQ4_NL-00003-of-00003.gguf13.3 GBf4091fcb3d3c779c744496404f61dd65e9e7b5f9a2c6853f154676100a8c9bb9
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UD-IQ4_XS/Qwen3.5-122B-A10B-UD-IQ4_XS-00002-of-00003.gguf49.8 GB5d414ef4dd2b8c73780bafa6b47deb5f5d70ecfa804a785f3b1d98a9084310de
UD-IQ4_XS/Qwen3.5-122B-A10B-UD-IQ4_XS-00003-of-00003.gguf12.2 GBd9ac8af93d3818980762fdfb18d4ff30415d37233acba156995bfb816819bcf9
UD-Q3_K_M/Qwen3.5-122B-A10B-UD-Q3_K_M-00001-of-00003.gguf10.9 MB80703f44b21f454e76ad7544c7a3ce1cdfeaedbec3f6944859d2174a65a41742
UD-Q3_K_M/Qwen3.5-122B-A10B-UD-Q3_K_M-00002-of-00003.gguf49.6 GB5c217241d1fbfba79f1d6829959e5f645ef93a3fa9ee8d6f0ae96d72bf375140
UD-Q3_K_M/Qwen3.5-122B-A10B-UD-Q3_K_M-00003-of-00003.gguf8.56 GB1495eb6c6c3715a8f90686348003f734a660532d3a5839a43e1801b342cdad35
UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf10.9 MB80703f44b21f454e76ad7544c7a3ce1cdfeaedbec3f6944859d2174a65a41742
UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00002-of-00003.gguf49.8 GB09b9d51c66a33c2c5881fb148a962cb4ea5ba0293265e5934f5400a1fc9da0b0
UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00003-of-00003.gguf8.56 GB1495eb6c6c3715a8f90686348003f734a660532d3a5839a43e1801b342cdad35
UD-Q4_K_M/Qwen3.5-122B-A10B-UD-Q4_K_M-00001-of-00003.gguf10.9 MB2faef922e80dcb9558c5a358510244a7c0bb96868b974a359dac87ce52a84316
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UD-Q4_K_M/Qwen3.5-122B-A10B-UD-Q4_K_M-00003-of-00003.gguf28.3 GB985f36efe05d1bd8ed50bfb17f1294b6957b8f343e14fed0d7ec7c81b739d32a
UD-Q4_K_S/Qwen3.5-122B-A10B-UD-Q4_K_S-00001-of-00003.gguf10.9 MB1bad1b03e500604f730a9cac343b97a72dec1ff4928338244d128ac0e5435ae2
UD-Q4_K_S/Qwen3.5-122B-A10B-UD-Q4_K_S-00002-of-00003.gguf49.8 GB1f1f513f13231899392aea4e6db67b2444368e56bf4c7e2c223c8941f4f9ee12
UD-Q4_K_S/Qwen3.5-122B-A10B-UD-Q4_K_S-00003-of-00003.gguf23.6 GBd20044f024f4ee8f48e5a8c95c690980245b66cf1747e9d04045c7ff7a8fea8b
UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf10.9 MB2faef922e80dcb9558c5a358510244a7c0bb96868b974a359dac87ce52a84316
UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00002-of-00003.gguf49.7 GB874d464b62303257a152c15a3c813f65c56957eb6cf19f8ed596668235a53164
UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00003-of-00003.gguf29.0 GB1e37527ef0e3edbbbe605956752e1be4f4bdbe4ae104701146bb33afed14244a
UD-Q5_K_M/Qwen3.5-122B-A10B-UD-Q5_K_M-00001-of-00003.gguf10.9 MBadf65c504c36890c609fc2faac272c55fef3c7f8d02e4af63c84e2f66e8d57fd
UD-Q5_K_M/Qwen3.5-122B-A10B-UD-Q5_K_M-00002-of-00003.gguf49.8 GB24ee12f15c0e467bd38fb7ee03b16c05f32663177435caafbe59d88c8909b628
UD-Q5_K_M/Qwen3.5-122B-A10B-UD-Q5_K_M-00003-of-00003.gguf43.9 GB87f2f4e59bc127ee6dab3a1943cb974ac958bf2b23bf9554dd81b2ab307cf78f
UD-Q5_K_S/Qwen3.5-122B-A10B-UD-Q5_K_S-00001-of-00003.gguf10.9 MBea08ef11402edf53a98cb90329961ee3dbe191dcf75ff7c99f009264cf8a99b3
UD-Q5_K_S/Qwen3.5-122B-A10B-UD-Q5_K_S-00002-of-00003.gguf50.0 GB6d4910dc603ab82570d2c8a1a979414e9d921af68273113063e7679424d53fa7
UD-Q5_K_S/Qwen3.5-122B-A10B-UD-Q5_K_S-00003-of-00003.gguf38.3 GBe0f018ae1f3016fab56005ba4b25e708720eaa2413232bd65ba07c7c6d964938
UD-Q5_K_XL/Qwen3.5-122B-A10B-UD-Q5_K_XL-00001-of-00003.gguf10.9 MBadf65c504c36890c609fc2faac272c55fef3c7f8d02e4af63c84e2f66e8d57fd
UD-Q5_K_XL/Qwen3.5-122B-A10B-UD-Q5_K_XL-00002-of-00003.gguf49.8 GB24ee12f15c0e467bd38fb7ee03b16c05f32663177435caafbe59d88c8909b628
UD-Q5_K_XL/Qwen3.5-122B-A10B-UD-Q5_K_XL-00003-of-00003.gguf44.1 GBe36ab233e5166696b070aecb1d594f6d3920e7a57e465194509afe2473f71c90
UD-Q6_K/Qwen3.5-122B-A10B-UD-Q6_K-00001-of-00004.gguf10.9 MBf0b1118816801970848476c4ddb1e65dd4b9be6f0a45aa7b2161d5c6b00a0ba5
UD-Q6_K/Qwen3.5-122B-A10B-UD-Q6_K-00002-of-00004.gguf49.7 GB92cd1fa645b374352e9cd363a9406a7cb63d5f454b95b5460496b9a99e400ee4
UD-Q6_K/Qwen3.5-122B-A10B-UD-Q6_K-00003-of-00004.gguf49.5 GB5d281ebbf36dc2c8fa6351e05a0a22da8bd3199706765170502b3062b34d069c
UD-Q6_K/Qwen3.5-122B-A10B-UD-Q6_K-00004-of-00004.gguf4.87 GB83aa312b4727c0a8cfcbfd27521f5459ec3ac8338885e83aebc9f1f7da0baed9
UD-Q6_K_XL/Qwen3.5-122B-A10B-UD-Q6_K_XL-00001-of-00004.gguf10.9 MBf0b1118816801970848476c4ddb1e65dd4b9be6f0a45aa7b2161d5c6b00a0ba5
UD-Q6_K_XL/Qwen3.5-122B-A10B-UD-Q6_K_XL-00002-of-00004.gguf49.6 GBf0e4ff6eb44ccf4280ea5152905f333934cc3e53e1226709e8c1ae31adfc7f62
UD-Q6_K_XL/Qwen3.5-122B-A10B-UD-Q6_K_XL-00003-of-00004.gguf49.5 GB861ae94cdaab7f23c0414df49a9d3ea3684239e38f4bba28553b96ae98e493c8
UD-Q6_K_XL/Qwen3.5-122B-A10B-UD-Q6_K_XL-00004-of-00004.gguf14.8 GB01db80891542e8379140780f2d815a59d3d044dce708df2e7d7f3c487292b396
UD-Q8_K_XL/Qwen3.5-122B-A10B-UD-Q8_K_XL-00001-of-00004.gguf10.9 MBbe207f374df3e927ae402c0870d4473c720f44499398d61f54b3ddcdaf345154
UD-Q8_K_XL/Qwen3.5-122B-A10B-UD-Q8_K_XL-00002-of-00004.gguf49.8 GB28a9de9da1372aac899482666ff3e95756fa7d09cf50eee22e967b0b7728e88b
UD-Q8_K_XL/Qwen3.5-122B-A10B-UD-Q8_K_XL-00003-of-00004.gguf49.8 GB0f160640bc3397af3348caaf128bd7b2b4a284fe65097ecdad971906de07e878
UD-Q8_K_XL/Qwen3.5-122B-A10B-UD-Q8_K_XL-00004-of-00004.gguf35.3 GBda7a6b0396acc734d0aa98ce7f5da6bcb7530f19a4d045087b66e3aea437d1a9
imatrix_unsloth.gguf_file359 MB5dea467b51cba3d759a555909abc48e9350cd022846c2b4171494df170aa530e
mmproj-BF16.gguf912 MB8f2442758a6f5a2bbfbbddb4b160b08ff4e29c3a76cad7627a0fe2beca2325e6
mmproj-F16.gguf909 MBde4405930dc6f288546e274ee41945f651fa3673b2ad9044c25fcffc5bb1c56d
mmproj-F32.gguf1.81 GB20ec92bc68b75d0496309a58a7a028763d4cd7c4bda95152a8c73584964cc205
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
907becb331 pinned now2026-11-15 04:43 UTC68 files · 1.82 TB

Use it

$ fw get unsloth/Qwen3.5-122B-A10B-MTP-GGUF
# manifest sha256 f691bb29440f… · 1.82 TB · 68 files
$ fw verify ./Qwen3.5-122B-A10B-MTP-GGUF unsloth/Qwen3.5-122B-A10B-MTP-GGUF
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-122B-A10B-MTP-GGUF.json --dir ./Qwen3.5-122B-A10B-MTP-GGUF $ python3 fw.py verify ./Qwen3.5-122B-A10B-MTP-GGUF ./unsloth__Qwen3.5-122B-A10B-MTP-GGUF.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Qwen3.5-122B-A10B-MTP-GGUF --revision 907becb33103ab66b23da06a2dc74a5fca0583bd --local-dir ./Qwen3.5-122B-A10B-MTP-GGUF
llama.cppDownload one GGUF variant (Qwen3.5-122B-A10B-UD-IQ1_M, 38.7 GB), then serve it.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Qwen3.5-122B-A10B-MTP-GGUF --include "Qwen3.5-122B-A10B-UD-IQ1_M*" --local-dir ./Qwen3.5-122B-A10B-MTP-GGUF $ llama-server -m ./Qwen3.5-122B-A10B-MTP-GGUF/Qwen3.5-122B-A10B-UD-IQ1_M.gguf
OllamaMake an Ollama model from the downloaded file.
$ printf 'FROM ./Qwen3.5-122B-A10B-MTP-GGUF/Qwen3.5-122B-A10B-UD-IQ1_M.gguf\n' > Modelfile $ ollama create qwen3.5-122b-a10b-mtp-gguf -f Modelfile
Run at home

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
18338c975d7162864a8828978546c8f492339a104cd4f28e95274b831695447c
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: Qwen3.5-122B-A10B-MTP-GGUFDate: 2026-11-15Commit: 907becbSigned: 2DC28CC7B26A283B ✓
Upstream
unsloth/Qwen3.5-122B-A10B-MTP-GGUF
Pinned commit
907becb33103ab66b23da06a2dc74a5fca0583bd
Manifest
Qwen3.5-122B-A10B-MTP-GGUF.json · .minisig
Manifest sha256
f691bb29440fa39183e4719ca17acdf155a3460ff3fca479f91ad95abc435051
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:43 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-122B-A10B-MTP-GGUF.json $ curl -O https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-122B-A10B-MTP-GGUF.json.minisig $ minisign -Vm Qwen3.5-122B-A10B-MTP-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/Qwen3.5-122B-A10B · 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/Qwen3.5-122B-A10B-MTP-GGUF
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. Qwen3.5-122B-A10B-MTP-GGUF.json. Provenance. Takedown history: none.
Source
huggingface.co/unsloth/Qwen3.5-122B-A10B-MTP-GGUF

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