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alt.freeweights.qwen / Qwen3.5-35B-A3B-MTP-GGUF
From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.qwenSubject:

Qwen3.5-35B-A3B-MTP-GGUF

Date: 2026-11-15Commit: 63af837Signed: 2DC28CC7B26A283B ✓

Image text to text model. Gguf weights, 531 GB in all. Published by unsloth under Apache-2.0.

qwen531GBQwen3.5-35B-A3B-MTP-GGUFggufpermissive
FactsParamsnot statedSize531 GBFormatsggufTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get unsloth/Qwen3.5-35B-A3B-MTP-GGUF
# manifest sha256 a38feebe40b7… · 531 GB · 29 files
$ fw verify ./Qwen3.5-35B-A3B-MTP-GGUF unsloth/Qwen3.5-35B-A3B-MTP-GGUF
From sourceHosted (signed)unsloth/Qwen3.5-35B-A3B-MTP-GGUFLast check: 2026-11-15 04:45 UTC
Run at homeGet help

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

Quantized of Qwen3.5-35B-A3B.

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ggml
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Overview

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

Original release from the publisher.

Base model: Qwen/Qwen3.5-35B-A3B

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 04:45 UTCpassed

Model card

From the publisher at commit 63af837. 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/Qwen3.5-35B-A3B-MTP-GGUF"
./llama.cpp/llama-server \
    -hf unsloth/Qwen3.5-35B-A3B-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 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.

Image: horiztonal plot

For Qwen3.5-35B-A3B, we primarily reduced the maximum KLD:

Image: image

Feb 27 Update: GGUFs Refreshed + Tool-calling fixes + Benchmarks#

  • Qwen3.5 is now updated with improved tool-calling & coding performance! See improvements via Claude Code, Codex.
  • We also benchmarked GGUFs & removed MXFP4 layers from 3 quants. Read analysis here.

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

Fine-tuning and RL Qwen3.5#

Qwen3.5-35B-A3B#

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.

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-Flash is the hosted version corresponding to Qwen3.5-35B-A3B with more production features, e.g., 1M context length by default and official built-in tools. 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: 35B in total and 3B activated
    • Hidden Dimension: 2048
    • Token Embedding: 248320 (Padded)
    • Number of Layers: 40
    • Hidden Layout: 10 × (3 × (Gated DeltaNet → MoE) → 1 × (Gated Attention → MoE))
    • Gated DeltaNet:
      • Number of Linear Attention Heads: 32 for V and 16 for QK
      • Head Dimension: 128
    • Gated Attention:
      • Number of Attention Heads: 16 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: 512
    • 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

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

Files

29 files · 531 GBWeights 11.4 GB (smallest of 22 GGUF variants). Sizes and sha256 from the signed manifest.
Show all 29 files with their sha256
FileSizesha256
.gitattributes3.24 KB8b864d4012f41d5d17c8da8e73f82edc8ded70670315148cc63fdc3a76cc9350
BF16/Qwen3.5-35B-A3B-BF16-00001-of-00002.gguf49.8 GBd6583422debde8e2eebf98793be6f83052811d8507317c92bcfcf90ac2bb63e0
BF16/Qwen3.5-35B-A3B-BF16-00002-of-00002.gguf21.2 GB3be58d82ac5979f1f4d1d74f8831158998c53ef5d3ce03ac80a0ee3cbee29e41
Qwen3.5-35B-A3B-MXFP4_MOE.gguf22.2 GB08110e4c77e4060402701b69b7708994c7eb4ea16f59664b7fcb5328b9fd10ad
Qwen3.5-35B-A3B-Q8_0.gguf37.8 GBb08e017b6ffcd1f57343a603a9e6d9e498373ef09da55537a2788af70ceb1d0d
Qwen3.5-35B-A3B-UD-IQ1_M.gguf11.4 GBb17fbbc54b803d6dae9adcbdc7ac0bf6f6fbe8c5fd7fd807ce6444a85c908e70
Qwen3.5-35B-A3B-UD-IQ2_M.gguf11.9 GB03684c5838c2a94d3d3b10a485fa61233c9ed440ce4970e0dc6506611b6645da
Qwen3.5-35B-A3B-UD-IQ2_XXS.gguf11.8 GB3914ae7b550f0b3279ee925875ca9fa6b7dc927a7554a25ae59b10675c4448b9
Qwen3.5-35B-A3B-UD-IQ3_S.gguf15.3 GB491cca884600cf6a4673d1642f071447f99f8613a8bde7f48ca5f3eef9a2a34a
Qwen3.5-35B-A3B-UD-IQ3_XXS.gguf14.1 GB68d21976070d4746a64bc3c5394d1947306406610b823a31ebbe7de6401d5d4f
Qwen3.5-35B-A3B-UD-IQ4_NL.gguf18.5 GB393a950d55316f264936a830299dda12003dc464ec10c70b6f8e1b568b038680
Qwen3.5-35B-A3B-UD-IQ4_XS.gguf18.2 GB7cc6ef70e7127c068518df70fb3332fe22bbf09f14361a4558d97324d15cfe05
Qwen3.5-35B-A3B-UD-Q2_K_XL.gguf12.6 GB2e2a7d69a4b3c806786e8e063bd732e17bd02334c09363e1121e424ee189aae8
Qwen3.5-35B-A3B-UD-Q3_K_M.gguf17.1 GBc2d46cfa78edb540830651c5ee937b1518c1bf3ed08e9ff14f1e0fcaa3bbbbc6
Qwen3.5-35B-A3B-UD-Q3_K_XL.gguf17.2 GBf27b8b6f8412db5b936dbb100d6bd29457bf1f9656a04ebbc4170cde2f8e619c
Qwen3.5-35B-A3B-UD-Q4_K_M.gguf22.6 GBa20c5a9f0cee94d24af3553e7828df1337c7d24213789911c74e1f4936894936
Qwen3.5-35B-A3B-UD-Q4_K_S.gguf21.3 GB30d199399f89b1cb3cde0a29dae2174562761c3e8329abcc1ae6f202690823ab
Qwen3.5-35B-A3B-UD-Q4_K_XL.gguf22.9 GB26445264c88d8bd4bf465763962b222cc8f816f9abe3717421f916ec4661fa9e
Qwen3.5-35B-A3B-UD-Q5_K_M.gguf27.0 GBd06c5abc4979bfb9f08c4c54fa27d6c66b1c2805417b90d1395fcf67725f623d
Qwen3.5-35B-A3B-UD-Q5_K_S.gguf25.5 GBe23bc50abf70aa82eeebe4b56f22a70f318b5857cdec50af3988d4a1364adde6
Qwen3.5-35B-A3B-UD-Q5_K_XL.gguf27.2 GB0e074df0e327a660b36db061f95272650233d8d6e330659dd209684fa38652f2
Qwen3.5-35B-A3B-UD-Q6_K.gguf29.9 GB6045dc5526a6d45078bae0cac5448d8a080ab5d03618adf1988c1981aa02e3c1
Qwen3.5-35B-A3B-UD-Q6_K_XL.gguf32.6 GB660bd6c2db363dbbd946bef7a8820b1abdc286038482d07bd9ba0f0b46b76151
Qwen3.5-35B-A3B-UD-Q8_K_XL.gguf39.1 GB2560b1078bc211f718d58a50e3d4801a664ed5dc94580642b85189dd2d0b8a8a
README.md97.5 KB5f4ab1a96990cc83a438ec9cd418c21eef29f75cc3cb4bf67722685e26b309ca
imatrix_unsloth.gguf_file192 MB37011c51f1a40ab7fd34712271d68cc71b5737e11b926a257c571f032d050ed2
mmproj-BF16.gguf903 MB2909c7f82becd3fa8730004cf34915ca7ed1cfbc51541a7dcaaccbfa8a0c27c1
mmproj-F16.gguf899 MBa7ade071fb7bf589befc486621d12e517598c301d63664a123e5f4a4f7235704
mmproj-F32.gguf1.79 GBc059c426902c354f01e72017182b3c9ecff7d6315b88ff97ce8b5c8b4ab1eb3d
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
63af837389 pinned now2026-11-15 04:45 UTC29 files · 531 GB

Use it

$ fw get unsloth/Qwen3.5-35B-A3B-MTP-GGUF
# manifest sha256 a38feebe40b7… · 531 GB · 29 files
$ fw verify ./Qwen3.5-35B-A3B-MTP-GGUF unsloth/Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B-MTP-GGUF.json --dir ./Qwen3.5-35B-A3B-MTP-GGUF $ python3 fw.py verify ./Qwen3.5-35B-A3B-MTP-GGUF ./unsloth__Qwen3.5-35B-A3B-MTP-GGUF.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Qwen3.5-35B-A3B-MTP-GGUF --revision 63af8373893a7a73c6dfcb84cb63d815981da5e0 --local-dir ./Qwen3.5-35B-A3B-MTP-GGUF
llama.cppDownload one GGUF variant (Qwen3.5-35B-A3B-UD-IQ1_M, 11.4 GB), then serve it.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Qwen3.5-35B-A3B-MTP-GGUF --include "Qwen3.5-35B-A3B-UD-IQ1_M*" --local-dir ./Qwen3.5-35B-A3B-MTP-GGUF $ llama-server -m ./Qwen3.5-35B-A3B-MTP-GGUF/Qwen3.5-35B-A3B-UD-IQ1_M.gguf
OllamaMake an Ollama model from the downloaded file.
$ printf 'FROM ./Qwen3.5-35B-A3B-MTP-GGUF/Qwen3.5-35B-A3B-UD-IQ1_M.gguf\n' > Modelfile $ ollama create qwen3.5-35b-a3b-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
5f4ab1a96990cc83a438ec9cd418c21eef29f75cc3cb4bf67722685e26b309ca
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: Qwen3.5-35B-A3B-MTP-GGUFDate: 2026-11-15Commit: 63af837Signed: 2DC28CC7B26A283B ✓
Upstream
unsloth/Qwen3.5-35B-A3B-MTP-GGUF
Pinned commit
63af8373893a7a73c6dfcb84cb63d815981da5e0
Manifest
Qwen3.5-35B-A3B-MTP-GGUF.json · .minisig
Manifest sha256
a38feebe40b7c5876992907c59d2ca4b29f580b627d0373978a14248c985da9f
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:45 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-35B-A3B-MTP-GGUF.json $ curl -O https://freeweights.allternit.com/manifests/unsloth/Qwen3.5-35B-A3B-MTP-GGUF.json.minisig $ minisign -Vm Qwen3.5-35B-A3B-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/Qwen3.5-35B-A3B · 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/Qwen3.5-35B-A3B-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. Qwen3.5-35B-A3B-MTP-GGUF.json. Provenance. Takedown history: none.
Source
huggingface.co/unsloth/Qwen3.5-35B-A3B-MTP-GGUF

Related models

All of alt.freeweights.qwen →
From: BAAI <huggingface.co/BAAI>Subject: Aquila-VL-2B-llava-qwen
From: Perciqa <huggingface.co/Perciqa>Subject: Aurora-Code-1
From: BAAI <huggingface.co/BAAI>Subject: bge-reasoner-embed-qwen3-8b-0923
From: amazon <huggingface.co/amazon>Subject: BMOJOF-primed-HQwen3-8B-Instruct
From: nbeerbower <huggingface.co/nbeerbower>Subject: Bubba-Qwen3.6-27B-LoRA
From: bartowski <huggingface.co/bartowski>Subject: bytkim_Qwen3.8-27B-pi-GGUF