CatalogWantedMirrorsLedgerOperating StatementDocsStudio statusCard system
alt.freeweights.qwen / Qwen3.5-27B-FP8
From: Qwen <huggingface.co/Qwen>Newsgroups: alt.freeweights.qwenSubject:

Qwen3.5-27B-FP8

Date: 2026-11-15Commit: 97f5941Signed: 2DC28CC7B26A283B ✓

Image text to text model with 27.8B parameters. Safetensors weights, 30.9 GB in all. Published by Qwen under Apache-2.0.

27.8B params30.9GBQwen3.5-27B-FP8safetensorspermissive
FactsParams27.8BSize30.9 GBFormatssafetensorsTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get Qwen/Qwen3.5-27B-FP8
# manifest sha256 b27c6ee21caf… · 30.9 GB · 25 files
$ fw verify ./Qwen3.5-27B-FP8 Qwen/Qwen3.5-27B-FP8
From sourceHosted (signed)Qwen/Qwen3.5-27B-FP8Last check: 2026-11-15 04:35 UTC
Run at homeGet help

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

Quantized of Qwen3.5-27B.

Works with

Run23Fine-tune29Convert15Evaluate13Scan13Apps62
OverviewModel cardFilesUse itLicenseProvenanceModel tree

Overview

Size
30.9 GB
Weights
30.9 GB (safetensors)
Parameters
27.8B
Formats
safetensors
Task
image text to text
License
Apache-2.0 (permissive)
Files
25, each with a sha256
Publisher
Qwen
Added
2026-11-15
Section
alt.freeweights.qwen
Lineage

Original release from the publisher.

Base model: Qwen/Qwen3.5-27B

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
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 04:35 UTCpassed

Model card

From the publisher at commit 97f5941. Shown as plain text with links; images open at the publisher.

Qwen3.5-27B-FP8#

Image: image

Qwen Chat

Note. This repository contains FP8-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.

The quantization method is fine-grained fp8 quantization with block size of 128, and its performance metrics are nearly identical to those of the original model.

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: 27B
    • Hidden Dimension: 5120
    • Token Embedding: 248320 (Padded)
    • Number of Layers: 64
    • Hidden Layout: 16 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
    • Gated DeltaNet:
      • Number of Linear Attention Heads: 48 for V and 16 for QK
      • Head Dimension: 128
    • Gated Attention:
      • Number of Attention Heads: 24 for Q and 4 for KV
      • Head Dimension: 256
      • Rotary Position Embedding Dimension: 64
    • Feed Forward Network:
      • Intermediate Dimension: 17408
    • 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

| 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

| VideoMME(w/o sub.) | 78.9 | 75.3 | 79.0 | 83.9

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

Files

25 files · 30.9 GBWeights 30.9 GB (safetensors). Sizes and sha256 from the signed manifest.
Show all 25 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
LICENSE11.3 KB50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32
README.md92.7 KBc0d0fdb510cbf32ca9043d391fcf33d4c3ff2215e4235128823c9722ab3dece4
chat_template.jinja7.76 KBa4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715
config.json19.7 KB53f7e06eebbfe64e70eb0b0979f3da31de9aff6870594e9cf731ba3961501adc
configuration.json51 B2d4464e2ead06bc9bc718c781309ad1e7baded626d66e8dcdc8b469ba185faf0
generation_config.json244 B303aba891d66ab63908a7b3cc9163bcb835fdf8b9f6301c73216f3f1eb3992dd
merges.txt3.35 MBa9d356d7bdf1ef4949e3e748e95b8e10ad9d4e2e838eddc38a0a7b6b94d1db8d
model.safetensors-00001-of-00011.safetensors5.17 GB7701198b29eef4e7af4507beeb9d9a005810fc96c1163cc9d978612a7fd08af4
model.safetensors-00002-of-00011.safetensors2.67 GB2f43f66745253bb757636ca95d9c0f3e9fc79d23e8426d269e1264304b890583
model.safetensors-00003-of-00011.safetensors2.67 GB7aa87a91b2565305dc26f4d839f4449918f9dc63ca4a71819bd652cdd2183b1f
model.safetensors-00004-of-00011.safetensors2.67 GBb3acefabc2da65dfda0291d815bdf68d7ba104c4d8584ddca220929e93bcbe47
model.safetensors-00005-of-00011.safetensors2.67 GB5c3ac92c53fb83e767defdbfa1b42d0085c2f5c5eb514f5f23c4ca550a733d90
model.safetensors-00006-of-00011.safetensors2.67 GBa8d7c096d2963440a63e33afc6f503e4fd6ada0fff9b42d2dc5ae0bb07df5847
model.safetensors-00007-of-00011.safetensors2.67 GB6ab1b1bb6a505b9813a2ad1e5f0efe30aa78aed0abc1dc01564442c64c83501e
model.safetensors-00008-of-00011.safetensors2.74 GBd76a94ba758a8e26057274a2e6ca28aa9026c0deac34a282bf9bbaf97fbf2e65
model.safetensors-00009-of-00011.safetensors2.67 GBea67474a59d4aeed2daaf1b30620e34be78981171f0d058ab6244ee1bd20a3c0
model.safetensors-00010-of-00011.safetensors2.67 GB812b25e59727647a4af8f1d5f2c6d95cd7b1c1674f7e8610461ad5e73861779f
model.safetensors-00011-of-00011.safetensors1.56 GB7b64e359bd951e90c8dcb609df16e1334cc68a8501f242b7fd483a4398f4b64a
model.safetensors.index.json182 KB1f48d8a182a2b5dad8e7e3ab4aa661bb5ab4cfc5f54b2c7fdc02d5e00eec9171
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
97f5941bf6 pinned now2026-11-15 04:35 UTC25 files · 30.9 GB

Use it

$ fw get Qwen/Qwen3.5-27B-FP8
# manifest sha256 b27c6ee21caf… · 30.9 GB · 25 files
$ fw verify ./Qwen3.5-27B-FP8 Qwen/Qwen3.5-27B-FP8
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-27B-FP8.json --dir ./Qwen3.5-27B-FP8 $ python3 fw.py verify ./Qwen3.5-27B-FP8 ./Qwen__Qwen3.5-27B-FP8.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download Qwen/Qwen3.5-27B-FP8 --revision 97f5941bf617e31c5e237364a8602ce3f03a551a --local-dir ./Qwen3.5-27B-FP8
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/Qwen3.5-27B-FP8", revision="97f5941bf617e31c5e237364a8602ce3f03a551a") model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.5-27B-FP8", revision="97f5941bf617e31c5e237364a8602ce3f03a551a", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve Qwen/Qwen3.5-27B-FP8 --revision 97f5941bf617e31c5e237364a8602ce3f03a551a
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
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: Qwen3.5-27B-FP8Date: 2026-11-15Commit: 97f5941Signed: 2DC28CC7B26A283B ✓
Upstream
Qwen/Qwen3.5-27B-FP8
Pinned commit
97f5941bf617e31c5e237364a8602ce3f03a551a
Manifest
Qwen3.5-27B-FP8.json · .minisig
Manifest sha256
b27c6ee21cafc04d1cf74b1d7a09302894e407bf845c72c30978127359f25538
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/Qwen3.5-27B-FP8.json $ curl -O https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-27B-FP8.json.minisig $ minisign -Vm Qwen3.5-27B-FP8.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/Qwen3.5-27B · 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/Qwen3.5-27B-FP8
Open in Freeweights Studio

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

Signed manifest
minisign, key 2DC28CC7B26A283B. Qwen3.5-27B-FP8.json. Provenance. Takedown history: none.
Source
huggingface.co/Qwen/Qwen3.5-27B-FP8

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