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alt.freeweights.qwen / Qwen3.5-397B-A17B-GPTQ-Int4
From: Qwen <huggingface.co/Qwen>Newsgroups: alt.freeweights.qwenSubject:

Qwen3.5-397B-A17B-GPTQ-Int4

Date: 2026-11-15Commit: df333deSigned: 2DC28CC7B26A283B ✓

Image text to text model with 403.4B parameters. Safetensors weights, 236 GB in all. Published by Qwen under Apache-2.0.

403.4B params236GBQwen3.5-397B-A17B-GPTQ-Int4safetensorspermissive
FactsParams403.4BSize236 GBFormatssafetensorsTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get Qwen/Qwen3.5-397B-A17B-GPTQ-Int4
# manifest sha256 693d40fb6846… · 236 GB · 108 files
$ fw verify ./Qwen3.5-397B-A17B-GPTQ-Int4 Qwen/Qwen3.5-397B-A17B-GPTQ-Int4
From sourceHosted (signed)Qwen/Qwen3.5-397B-A17B-GPTQ-Int4Last check: 2026-11-15 04:51 UTC
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Overview

Size
236 GB
Weights
236 GB (safetensors)
Parameters
403.4B
Formats
safetensors
Task
image text to text
License
Apache-2.0 (permissive)
Files
108, each with a sha256
Publisher
Qwen
Added
2026-11-15
Section
alt.freeweights.qwen
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Base model: Qwen/Qwen3.5-397B-A17B

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Qwen3.5-397B-A17B-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-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.
  • Quantization: GPTQ 4-bit

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 | 84.5

| RefSpatialBench | -- | -- | 65.5 | 69.9 | -- | 73.6

| LingoQA | 68.8 | 78.8 | 72.8 | 66.8

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

Files

108 files · 236 GBWeights 236 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 108 files with their sha256
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LICENSE11.3 KB50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32
README.md94.5 KBcac618bf44d636ed51cfe32d2bb8992774cbb08e352afdd8f379a1d685c3bc86
chat_template.jinja7.76 KBa4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715
config.json4.71 KB7f369d64a5919ef4dc70c3f33002f11d067bddc87a47781d949f4c530e4bbf08
configuration.json51 B2d4464e2ead06bc9bc718c781309ad1e7baded626d66e8dcdc8b469ba185faf0
generation_config.json244 B303aba891d66ab63908a7b3cc9163bcb835fdf8b9f6301c73216f3f1eb3992dd
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VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
df333de344 pinned now2026-11-15 04:51 UTC108 files · 236 GB

Use it

$ fw get Qwen/Qwen3.5-397B-A17B-GPTQ-Int4
# manifest sha256 693d40fb6846… · 236 GB · 108 files
$ fw verify ./Qwen3.5-397B-A17B-GPTQ-Int4 Qwen/Qwen3.5-397B-A17B-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/Qwen3.5-397B-A17B-GPTQ-Int4.json --dir ./Qwen3.5-397B-A17B-GPTQ-Int4 $ python3 fw.py verify ./Qwen3.5-397B-A17B-GPTQ-Int4 ./Qwen__Qwen3.5-397B-A17B-GPTQ-Int4.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download Qwen/Qwen3.5-397B-A17B-GPTQ-Int4 --revision df333de344e06b492063d1ca04e2d4ee878f7595 --local-dir ./Qwen3.5-397B-A17B-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/Qwen3.5-397B-A17B-GPTQ-Int4", revision="df333de344e06b492063d1ca04e2d4ee878f7595") model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.5-397B-A17B-GPTQ-Int4", revision="df333de344e06b492063d1ca04e2d4ee878f7595", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve Qwen/Qwen3.5-397B-A17B-GPTQ-Int4 --revision df333de344e06b492063d1ca04e2d4ee878f7595

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
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: Qwen <huggingface.co/Qwen>Newsgroups: alt.freeweights.qwenSubject: Qwen3.5-397B-A17B-GPTQ-Int4Date: 2026-11-15Commit: df333deSigned: 2DC28CC7B26A283B ✓
Upstream
Qwen/Qwen3.5-397B-A17B-GPTQ-Int4
Pinned commit
df333de344e06b492063d1ca04e2d4ee878f7595
Manifest
Qwen3.5-397B-A17B-GPTQ-Int4.json · .minisig
Manifest sha256
693d40fb6846128cf6ee0f1920e4e39074470b8f05c06d1710c6694185969fd7
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:51 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-397B-A17B-GPTQ-Int4.json $ curl -O https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-397B-A17B-GPTQ-Int4.json.minisig $ minisign -Vm Qwen3.5-397B-A17B-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

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 Qwen/Qwen3.5-397B-A17B-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. Qwen3.5-397B-A17B-GPTQ-Int4.json. Provenance. Takedown history: none.
Source
huggingface.co/Qwen/Qwen3.5-397B-A17B-GPTQ-Int4

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