alt.freeweights.qwen / Qwen3.5-122B-A10B-FP8

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Qwen3.5-122B-A10B-FP8

Date: 2026-11-15Commit: a099deeSigned: 2DC28CC7B26A283B ✓

Image text to text model with 125.1B parameters. Safetensors weights, 127 GB in all. Published by Qwen under Apache-2.0.

125.1B params

127GBQwen3.5-122B-A10B-FP8safetensorspermissive

FactsParams125.1BSize127 GBFormatssafetensorsTaskimage text to textLicenseApache-2.0From sourcepermissive

$ fw get Qwen/Qwen3.5-122B-A10B-FP8
# manifest sha256 cab36345f15b… · 127 GB · 53 files
$ fw verify ./Qwen3.5-122B-A10B-FP8 Qwen/Qwen3.5-122B-A10B-FP8

From sourceHosted (signed)Qwen/Qwen3.5-122B-A10B-FP8Last check: 2026-11-15 04:36 UTC

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Quantized of Qwen3.5-122B-A10B.

Works with

Run23 Fine-tune29 Convert15 Evaluate13 Scan13 Apps62

Runs with

Ranked by fit to this model (safetensors), then by GitHub stars.

vLLM exo Text Generation WebUI

SGLang MLC LLM KTransformers LMCache Xinference LMDeploy mistral.rs Mooncake FlashInfer

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AIBrix fastllm ExLlamaV3

Transformers LocalAI tinygrad Triton Inference Server BentoML CTranslate2 ExLlamaV2 LitServe

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mergekit TensorRT Model Optimizer

LLM Compressor Neural Compressor AutoRound AQLM GPTQModel ONNX Core ML Tools ExecuTorch

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Evaluate with

Opik DeepEval Ragas lm-evaluation-harness OpenCompass SWE-bench

simple-evals TruLens HELM

LightEval

Evaluate Weave

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Autoevals

Scan with

promptfoo garak Guardrails NeMo Guardrails Giskard DeepTeam AI Fairness 360 Fairlearn Detoxify MCP Scanner AgentDojo ModelScan

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picklescan

Overview Model card Files Use it License Provenance Model tree

Overview

Size: 127 GB
Weights: 127 GB (safetensors)
Parameters: 125.1B
Formats: safetensors
Task: image text to text
License: Apache-2.0 (permissive)
Files: 53, each with a sha256
Publisher: Qwen
Added: 2026-11-15
Section: alt.freeweights.qwen

Lineage

Original release from the publisher.

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

Where these bytes come from

Publisher at the pinned commit→You

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Sources

Badge	Host	Last check	Result
From source	huggingface.co	2026-11-15 04:36 UTC	passed

Model card

From the publisher at commit a099dee. Shown as plain text with links; images open at the publisher.

Qwen3.5-122B-A10B-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: 122B in total and 10B activated
- Hidden Dimension: 3072
- Token Embedding: 248320 (Padded)
- Number of Layers: 48
- Hidden Layout: 12 × (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#

| 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#

| 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

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

Files

53 files · 127 GBWeights 127 GB (safetensors). Sizes and sha256 from the signed manifest.

Show all 53 files with their sha256

File	Size	sha256
.gitattributes	1.57 KB	34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
LICENSE	11.3 KB	50cbab8a892c5f2993b8c7351a99182507472def3b1374558308605d99b86b32
README.md	92.9 KB	bca4fee86199cb783e95c20f8fc7542ac5517d3f143b4dbc05b26e97e410fd4d
chat_template.jinja	7.76 KB	a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715
config.json	23.0 KB	008eaf274520a2bacb88cecce50988685f4887643c8bc760514dac2c54f4cca9
configuration.json	48 B	a78221ae93dd8977b26fcd1d106fa5ad7e7f1fd3a7d93bfd9d6fba5ed00ec4d2
generation_config.json	244 B	303aba891d66ab63908a7b3cc9163bcb835fdf8b9f6301c73216f3f1eb3992dd
merges.txt	3.35 MB	a9d356d7bdf1ef4949e3e748e95b8e10ad9d4e2e838eddc38a0a7b6b94d1db8d
model.safetensors-00001-of-00039.safetensors	3.22 GB	03570a0776e147765bf83445e307e1b9a7fdedf6b9735b0d0f4b042480e38083
model.safetensors-00002-of-00039.safetensors	3.22 GB	a90419c5f032476270b569d6a1fca1b71138e3cdacb627fbd8f6112bacb51b3b
model.safetensors-00003-of-00039.safetensors	3.22 GB	bf09da6d8b7db1839b3fa79121897e6cdbcec4668fb1e48cda9b77b52969a4e0
model.safetensors-00004-of-00039.safetensors	3.22 GB	b01caa1e89a0d6979991ec9bd4ea96f81bf92adbfaa820caa704d900339c6b74
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model.safetensors-00007-of-00039.safetensors	3.22 GB	825e4dafc67f7ceab8aa87be4b2af21207c7950d4e57084fb363d5b3d53844a6
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model.safetensors-00009-of-00039.safetensors	3.22 GB	d548121a402aba816dd985b1cb4003ebfc4da839045257e2fab7e06b0ad2a2a4
model.safetensors-00010-of-00039.safetensors	3.22 GB	15c8e524b8ce0bf4772444230ff5f5233fed0c00cd58feba48dd1dbf36940881
model.safetensors-00011-of-00039.safetensors	3.22 GB	69183f4aae8a93f85795a1f0328d1aeb2bbffba669a755f27ede3fc62a949ecd
model.safetensors-00012-of-00039.safetensors	3.22 GB	97abce01afe17e979177ea6a80b90a6be4778bdee9ba61d0fbb6b4b745753c91
model.safetensors-00013-of-00039.safetensors	3.22 GB	a183abc66109cc8954fb9d5d0de34fa0d6444d61e2fec4734f5a7763690b031d
model.safetensors-00014-of-00039.safetensors	3.22 GB	8a3890952b285a945760f470156693ab19bb59834eac3a21aea3de9a8d9f719e
model.safetensors-00015-of-00039.safetensors	3.22 GB	de3b6a245b29effe536368021c897c5d5b971b566186e6a7c8dbf044769bd2f3
model.safetensors-00016-of-00039.safetensors	3.22 GB	a3cde2ce2f9464521fd2c05d2a40aff494762c01d13ea4fb952e199df6a845a3
model.safetensors-00017-of-00039.safetensors	3.22 GB	ac646f42d733e686db7e628d6be05436183b0a3b7a01fdbaa2e5da7fdd304802
model.safetensors-00018-of-00039.safetensors	3.22 GB	ebea05736faafc1c53b22f7f408cf51a82ae0186295db976d2dd24910bdeb4ee
model.safetensors-00019-of-00039.safetensors	3.22 GB	5afd209ebd65755f32bdd150624594586c9aeff20b32b7f5d359d0e58b58adf5
model.safetensors-00020-of-00039.safetensors	3.22 GB	89bc8e26f6d0ffa43db7075165f2cfa960139521665609036b227bc32e84e770
model.safetensors-00021-of-00039.safetensors	3.22 GB	5346dd81bada99e31fd9991c607ebe6a5d78e4506e5be29e983767d1041656b3
model.safetensors-00022-of-00039.safetensors	3.22 GB	dd61546aa76a394287d2a06ea77663108e631121b0c936e0b2873f2bd4d75735
model.safetensors-00023-of-00039.safetensors	3.22 GB	70a486c4a29dd06f653b5424fb152066f9b762a0ce73b76829ac0d67d1e2517a
model.safetensors-00024-of-00039.safetensors	3.22 GB	2834454d361034789da6417b4afb482a90f6277208cf8a6466aeb6433817951d
model.safetensors-00025-of-00039.safetensors	3.22 GB	e56c4604d5d1048bd6bfac26496fbb5f9b3feff4a8bffd5a5ff896c5cc8779ca
model.safetensors-00026-of-00039.safetensors	3.22 GB	9c2a569fc52d7f447c9e2314e7287cf312d4aec93956f58d5058de191a1ee7ef
model.safetensors-00027-of-00039.safetensors	3.22 GB	b20b4245e8ef752003e75213a57b568118c963e76e56fcf6e0e43d3e4cbc3be7
model.safetensors-00028-of-00039.safetensors	3.22 GB	79cbd97216da5dc718bd2c230321987e22e81ed961db2d4d9fe1ef9fa9d3490a
model.safetensors-00029-of-00039.safetensors	3.22 GB	5cd452476b9f3c67063da823c73a916bc6264d91baac4eb947d06edd90f81035
model.safetensors-00030-of-00039.safetensors	3.22 GB	059a4b8e73313f9e2866d52dc3fabe4fe7bf533143c1c1aa4816759a5303ec03
model.safetensors-00031-of-00039.safetensors	3.22 GB	417b8aaf0425678024669a4f4b0e604703164db716ca5219ecfca5b61cc3eb81
model.safetensors-00032-of-00039.safetensors	3.22 GB	d4e989134a41cba665eba9c1fabeda662c70ea3531adc4ddb4856f641972193e
model.safetensors-00033-of-00039.safetensors	3.22 GB	e6484a95e4b99ea257bfc11e8452286c87b23710c56005eb9545b48032b18539
model.safetensors-00034-of-00039.safetensors	3.22 GB	744c593b7d47c24bb275d6a5a9355267a531ac10028eed39db871b394906fb0e
model.safetensors-00035-of-00039.safetensors	3.22 GB	92bb405529e5a3da4327d535b22978218449dfa3b4e11ba9c44f7cd8cccf8c34
model.safetensors-00036-of-00039.safetensors	3.22 GB	ba5650043c6b4527bcab66dda0f38e1526fcdf8652ffa39f5dc878cfd6a1433d
model.safetensors-00037-of-00039.safetensors	4.73 GB	861c78ce0ccd71ac7951bd450328b3433deba263eb7742184854646ba41ab6c1
model.safetensors-00038-of-00039.safetensors	3.65 GB	fcae161d35811013db94d49674d58713e27cc802468d82e681531c3df287b2f2
model.safetensors-00039-of-00039.safetensors	2.80 GB	bc4f8cae2cc67401ba74e04f3d0ba76d57de4dc147ae2f36cea619b285f746f3
model.safetensors.index.json	9.67 MB	bd24760f446be23cbae265215375ce0f08cec6c2be00149931631b3981f59d23
preprocessor_config.json	390 B	27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516
tokenizer.json	12.8 MB	5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42
tokenizer_config.json	16.7 KB	316230d6a809701f4db5ea8f8fc862bc3a6f3229c937c174e674ff3ca0a64ac8
video_preprocessor_config.json	385 B	7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13
vocab.json	6.72 MB	ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003

VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.

Upstream commit	Date	Contents
a099dee70c pinned now	2026-11-15 04:36 UTC	53 files · 127 GB

Use it

$ fw get Qwen/Qwen3.5-122B-A10B-FP8
# manifest sha256 cab36345f15b… · 127 GB · 53 files
$ fw verify ./Qwen3.5-122B-A10B-FP8 Qwen/Qwen3.5-122B-A10B-FP8

fw.pyChecks the signature and every sha256, tries each source in order, resumes.

$ python3 fw.py get https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-122B-A10B-FP8.json --dir ./Qwen3.5-122B-A10B-FP8
$ python3 fw.py verify ./Qwen3.5-122B-A10B-FP8 ./Qwen__Qwen3.5-122B-A10B-FP8.json

hfThe Hugging Face CLI with one setting. Same repo id.

$ HF_ENDPOINT=https://freeweights.allternit.com hf download Qwen/Qwen3.5-122B-A10B-FP8 --revision a099dee70ccfcd8d5dda56aaa0b60cb8ecadabc9 --local-dir ./Qwen3.5-122B-A10B-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/Qwen3.5-122B-A10B-FP8", revision="a099dee70ccfcd8d5dda56aaa0b60cb8ecadabc9")
model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.5-122B-A10B-FP8", revision="a099dee70ccfcd8d5dda56aaa0b60cb8ecadabc9", device_map="auto")

vLLMServe an OpenAI-compatible API.

$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve Qwen/Qwen3.5-122B-A10B-FP8 --revision a099dee70ccfcd8d5dda56aaa0b60cb8ecadabc9

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.

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Provenance

From:

Qwen <huggingface.co/Qwen>Newsgroups: alt.freeweights.qwenSubject: Qwen3.5-122B-A10B-FP8Date: 2026-11-15Commit: a099deeSigned: 2DC28CC7B26A283B ✓

Upstream: Qwen/Qwen3.5-122B-A10B-FP8
Pinned commit: a099dee70ccfcd8d5dda56aaa0b60cb8ecadabc9
Manifest: Qwen3.5-122B-A10B-FP8.json · .minisig
Manifest sha256: cab36345f15b5fe9572c33073b6b1be3893bade96cc2b4ca44c62b22726e2a17
Signature: minisign, Ed25519, key 2DC28CC7B26A283B
Created: 2026-11-15 04:36 UTC
Generator: freeweights-manifest/0.1.0
Takedowns: None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-122B-A10B-FP8.json
$ curl -O https://freeweights.allternit.com/manifests/Qwen/Qwen3.5-122B-A10B-FP8.json.minisig
$ minisign -Vm Qwen3.5-122B-A10B-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/Qwen3.5-122B-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 Qwen/Qwen3.5-122B-A10B-FP8

Open in Freeweights Studio

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

Signed manifest

minisign, key 2DC28CC7B26A283B. Qwen3.5-122B-A10B-FP8.json. Provenance. Takedown history: none.

Source

huggingface.co/Qwen/Qwen3.5-122B-A10B-FP8

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Qwen3.5-122B-A10B-FP8

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Overview

Model card

Qwen3.5-122B-A10B-FP8#

Qwen3.5 Highlights#

Model Overview#

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Language#

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