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alt.freeweights.qwen / Qwen3.5-35B-A3B-GPTQ-Int4
From: Qwen <huggingface.co/Qwen>Newsgroups: alt.freeweights.qwenSubject:

Qwen3.5-35B-A3B-GPTQ-Int4

Date: 2026-11-15Commit: 3af5ca2Signed: 2DC28CC7B26A283B ✓

Image text to text model with 36B parameters. Safetensors weights, 24.5 GB in all. Published by Qwen under Apache-2.0.

36B params24.5GBQwen3.5-35B-A3B-GPTQ-Int4safetensorspermissive
FactsParams36BSize24.5 GBFormatssafetensorsTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get Qwen/Qwen3.5-35B-A3B-GPTQ-Int4
# manifest sha256 0020ea793f74… · 24.5 GB · 28 files
$ fw verify ./Qwen3.5-35B-A3B-GPTQ-Int4 Qwen/Qwen3.5-35B-A3B-GPTQ-Int4
From sourceHosted (signed)Qwen/Qwen3.5-35B-A3B-GPTQ-Int4Last check: 2026-11-15 04:35 UTC
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Quantized of Qwen3.5-35B-A3B.

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Overview

Size
24.5 GB
Weights
24.4 GB (safetensors)
Parameters
36B
Formats
safetensors
Task
image text to text
License
Apache-2.0 (permissive)
Files
28, each with a sha256
Publisher
Qwen
Added
2026-11-15
Section
alt.freeweights.qwen
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Original release from the publisher.

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

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Model card

From the publisher at commit 3af5ca2. Shown as plain text with links; images open at the publisher.

Qwen3.5-35B-A3B-GPTQ-Int4#

Image: image

Qwen Chat

Note. This repository contains int4-quantized 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.
  • Quantization: GPTQ 4-bit

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

| Hypersim | -- | -- | 11.0 | 12.7 | 13.0 | 13.1

| SUNRGBD | -- | -- | 34.9 | 36.2 | 35.4 | 33.4

| Nuscene | -- | -- | 13.9 | 15.4 | 15.2 | 14.6

| Video Understanding

| VideoMME(w sub.) | 83.5 | 81.1 | 83.8 | 87.3 | 87.0 | 86.6

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

Files

28 files · 24.5 GBWeights 24.4 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 28 files with their sha256
FileSizesha256
.gitattributes1.64 KBd1a8f4a1d2e3787c5956393d9306365ebefe1912cb76828870682b1ab16f5c27
LICENSE11.3 KB50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32
README.md92.8 KB544cae9538cdd6d0c7f33ec98073c6d72081f3a5657fcfa55e163391649e2ca3
chat_template.jinja7.76 KBa4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715
config.json4.08 KB13dd6a453236a33f16873dced7558fe7c9751e09f6c86ccc0cab66e3f23d4154
configuration.json51 B2d4464e2ead06bc9bc718c781309ad1e7baded626d66e8dcdc8b469ba185faf0
generation_config.json244 B4f25002776b741773666203dcea8f54619f177ace3ae483d311102092a4658e0
merges.txt3.35 MBa9d356d7bdf1ef4949e3e748e95b8e10ad9d4e2e838eddc38a0a7b6b94d1db8d
model.safetensors-00001-of-00014.safetensors1.42 GB44a50009ea9a08b6936e714406cc92db62112cadc5aa0891ae494974a015ba65
model.safetensors-00002-of-00014.safetensors1.42 GBa1230837600823407695239994c8c9ec4456d82a96bf584c7cbf8ebaa8df6f7a
model.safetensors-00003-of-00014.safetensors1.42 GB1eee7ac9d38d756abd7d2d139033b4a2ea9b235e11c605a07227eeaa24bf73cd
model.safetensors-00004-of-00014.safetensors1.42 GB81d8a9dfd8207576185263592f07be98238612f01e9fa2329db4614d995c4442
model.safetensors-00005-of-00014.safetensors1.42 GBfe9ae8c7b7f9fd77a5fe4eb64b38f220273948a634f46cdc2329ad4a8e46bd87
model.safetensors-00006-of-00014.safetensors1.42 GB87adc7dfcaa118dbd67945c49bb3262a048fb766e964c8d0c05f7820c731c80c
model.safetensors-00007-of-00014.safetensors1.42 GBcc4347439c89fe66ea758cdb98a4de61f07f7332817ca049660dcb8db9d7da15
model.safetensors-00008-of-00014.safetensors1.42 GB9d194d148038413796c17ae9e21991bd2e23bea3245d307a8cbe5a08197aefc1
model.safetensors-00009-of-00014.safetensors2.87 GB4ed6f8fb9ae9db5a701adeeaef7ee93d76b1baf5d8f2c546cc67b019619b54cf
model.safetensors-00010-of-00014.safetensors1.40 GB916e282db9cd96ccbd8b59a58c6cd5839a4b18eb9f52214b5837c7964e9d2499
model.safetensors-00011-of-00014.safetensors1.40 GB55aa0fac76b655c4925b8f67a0779cbc0d33e626fb41454358a90e5c625003c4
model.safetensors-00012-of-00014.safetensors1.40 GB66a71ff1d59e676fe3c8a84180edaed3d512c497e9db11c92f8ed3a585fb98a0
model.safetensors-00013-of-00014.safetensors3.78 GBe3fca27370edeba637703c248a9bc0db1892c6e798e718e25f01d484e04f9752
model.safetensors-00014-of-00014.safetensors2.22 GBd5e08a7dd670d7ef8da38e7af29cb8cdf42ecded5dfb2d9fcaf06cae79dfaa41
model.safetensors.index.json15.1 MB2cf82a50740d7a18b4ba1dbb0e35f2b76c1c7ca683a04156ad062346d5c7bc1f
preprocessor_config.json390 B27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516
tokenizer.json12.8 MB5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42
tokenizer_config.json16.7 KB316230d6a809701f4db5ea8f8fc862bc3a6f3229c937c174e674ff3ca0a64ac8
video_preprocessor_config.json385 B7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13
vocab.json6.72 MBce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
3af5ca2972 pinned now2026-11-15 04:35 UTC28 files · 24.5 GB

Use it

$ fw get Qwen/Qwen3.5-35B-A3B-GPTQ-Int4
# manifest sha256 0020ea793f74… · 24.5 GB · 28 files
$ fw verify ./Qwen3.5-35B-A3B-GPTQ-Int4 Qwen/Qwen3.5-35B-A3B-GPTQ-Int4
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-35B-A3B-GPTQ-Int4.json --dir ./Qwen3.5-35B-A3B-GPTQ-Int4 $ python3 fw.py verify ./Qwen3.5-35B-A3B-GPTQ-Int4 ./Qwen__Qwen3.5-35B-A3B-GPTQ-Int4.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download Qwen/Qwen3.5-35B-A3B-GPTQ-Int4 --revision 3af5ca2972faf6de1fd6f4efc4d8d319ca751e8b --local-dir ./Qwen3.5-35B-A3B-GPTQ-Int4
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForImageTextToText, AutoProcessor processor = AutoProcessor.from_pretrained("Qwen/Qwen3.5-35B-A3B-GPTQ-Int4", revision="3af5ca2972faf6de1fd6f4efc4d8d319ca751e8b") model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.5-35B-A3B-GPTQ-Int4", revision="3af5ca2972faf6de1fd6f4efc4d8d319ca751e8b", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve Qwen/Qwen3.5-35B-A3B-GPTQ-Int4 --revision 3af5ca2972faf6de1fd6f4efc4d8d319ca751e8b
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
LICENSE
Text sha256
50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32
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No extra terms beyond the license.

Read the license text

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Provenance

From: Qwen <huggingface.co/Qwen>Newsgroups: alt.freeweights.qwenSubject: Qwen3.5-35B-A3B-GPTQ-Int4Date: 2026-11-15Commit: 3af5ca2Signed: 2DC28CC7B26A283B ✓
Upstream
Qwen/Qwen3.5-35B-A3B-GPTQ-Int4
Pinned commit
3af5ca2972faf6de1fd6f4efc4d8d319ca751e8b
Manifest
Qwen3.5-35B-A3B-GPTQ-Int4.json · .minisig
Manifest sha256
0020ea793f74ccb6482e94d30c7a1c8ba09ecf19c3f8b3a2e62ff1060257d537
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:35 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-35B-A3B-GPTQ-Int4.json $ curl -O https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-35B-A3B-GPTQ-Int4.json.minisig $ minisign -Vm Qwen3.5-35B-A3B-GPTQ-Int4.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

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Parent

Qwen/Qwen3.5-35B-A3B · this model is a quantization of it

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Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download Qwen/Qwen3.5-35B-A3B-GPTQ-Int4
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-GPTQ-Int4.json. Provenance. Takedown history: none.
Source
huggingface.co/Qwen/Qwen3.5-35B-A3B-GPTQ-Int4

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