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vanch007/Qwen3.5-122B-A10B-abliterated-4bit-vlm-mlx-cs2764-pass2

vanch007 Qwen 122B MoE multimodal
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Response includes
  • classification m1
  • files 28
  • benchmarks 11 entries
  • hub_downloads_all_time 1,905
  • author_summary 27 models
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Abliteration classifier · v1.0.0
M1
Primary method

Direct removal

No other method signals detected in this model.
Confidence
MEDIUM
Why this label 3 signals
Method inferred from partial signals - repository name, related files, or tag patterns. Producer identity not confirmed; label may sharpen or shift as we gather more evidence.
  • 'abliterated' in name/tags
  • is_gguf=0 (base model)
  • no specific method indicators - defaulting to M1 (most common)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
MEDIUM
Why we say so
primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
Downloads · lifetime
2K
71 last 30d - cooling
Likes
0
Model age
7mo ago
created 2026-03-07
Downloads over time
Now1.9K→from0↑0%
07041.4K2.1K0 on Mar 41.9K on Oct 111.9K on Oct 6MarAprMayJunJulAugSepOct
Mar 4 → Oct 11 · 71 snapshots · spans 221 days

Benchmarks

Portrait before abliteration
Benchmarks of the base model as it stood before the refusal-removal operation. Compare with the numbers above to see what the operation cost.
Benchmark Score Source
Entertainment 1.4 UGI
Hazardous 1.8 UGI
Natural Intelligence 31.08 UGI
Political lean -21.9% UGI
Sensitive-Info 17.81 UGI
SocPol 2.3 UGI
UGI 17.71 UGI
Willingness (10) 1.8 UGI
W10-Adherence 1.5 UGI
W10-Direct 2 UGI
Writing 39.54 UGI

Genealogy 0 direct forks

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Metadata

License
apache-2.0
Languages
en zh
Tags
mlx-vlm safetensors qwen3_5_moe mlx qwen qwen3 multimodal vision-language-model 4-bit quantized abliteration refusal-removal

Related

Total size
65.1 GB
Files
28
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-03-07 15:17

Files by quantization

Auxiliary files 28 files 65.1 GB
model-00008-of-00014.safetensors 4.86 GB 3185d212 download
model-00011-of-00014.safetensors 4.86 GB b8e48bbe download
model-00012-of-00014.safetensors 4.86 GB 749d99c0 download
model-00009-of-00014.safetensors 4.86 GB 0b68699b download
model-00006-of-00014.safetensors 4.86 GB 864cae48 download
model-00005-of-00014.safetensors 4.86 GB 4679ce74 download
model-00003-of-00014.safetensors 4.86 GB 59cacb14 download
model-00002-of-00014.safetensors 4.86 GB dba1ed16 download
model-00013-of-00014.safetensors 4.81 GB fb22217c download
model-00004-of-00014.safetensors 4.81 GB 91296359 download
model-00010-of-00014.safetensors 4.81 GB 09bee136 download
model-00007-of-00014.safetensors 4.81 GB 0d7ab5ab download
model-00001-of-00014.safetensors 4.78 GB 5900c4ec download
model-00014-of-00014.safetensors 2.14 GB fdec9351 download
tokenizer.json 19.1 MB 87a7830d download
vocab.json 6.41 MB 0aa0ce06 download
model.safetensors.index.json 247 KB fbe15d15 download
chat_template.jinja 7.57 KB ae8ae364 download
README.md 4.39 KB 7727f789 download
config.json 3.97 KB 37a388af download
.gitattributes 1.53 KB 52373fe2 download
processor_config.json 1.27 KB 7ad6acdf download
tokenizer_config.json 1.15 KB 040f6e4b download
abliteration_log.json 1.14 KB 34e58126 download
ablation_meta.json 410 B 2781b5e7 download
preprocessor_config.json 390 B 2ea84a43 download
video_preprocessor_config.json 385 B 3ba673a5 download
generation_config.json 244 B 85b45ab4 download

README current version from Hugging Face


language:

  • en
  • zh
    license: apache-2.0
    library_name: mlx-vlm
    pipeline_tag: image-text-to-text
    tags:
  • mlx
  • mlx-vlm
  • qwen
  • qwen3
  • multimodal
  • vision-language-model
  • 4-bit
  • quantized
  • abliteration
  • refusal-removal
  • uncensored
    base_model:
  • Qwen/Qwen3.5-122B-A10B

Qwen3.5-122B-A10B-abliterated-4bit-vlm-mlx-cs2764-pass2

4-bit MLX VLM release of a Qwen3.5-122B-A10B abliterated checkpoint, with an additional broad cs2764/mlx-abliteration pass applied on the MLX model.

A newer, more speed-balanced variant is available here:

Summary

  • Architecture: qwen3_5_moe / Qwen3_5MoeForConditionalGeneration
  • Modality: vision-language model
  • Quantization: 4-bit MLX (group_size=64, mode=affine)
  • Model size on disk: about 65.1 GB
  • Weight shards: 14
  • Toolkit used for the extra ablation pass: cs2764/mlx-abliteration

When To Use This Repo vs The Newer final Repo

Aspect This pass2 repo Newer final repo
Extra ablation scope broader pass over layers 0..47 targeted hot-path pass on layers 36..47
Goal maximize refusal reduction better speed / behavior tradeoff
Local behavior spot checks stronger refusal weakening milder than pass2, but still weaker refusal than direct 4-bit base
Local short generation speed about 12.6 tok/s about 24.0 tok/s
Recommendation:
  • Use this pass2 repo if you want the more aggressive refusal-removal behavior.
  • Use the newer final repo if you want a faster model with a narrower targeted extra pass.

Lineage

The release chain for this repository is:

  1. Foundation model: Qwen/Qwen3.5-122B-A10B
  2. Input checkpoint: a user-supplied abliterated Qwen3.5-122B-A10B VLM checkpoint
  3. MLX VLM conversion: quantized to 4-bit MLX while preserving vision_config, preprocessor_config.json, processor_config.json, and video_preprocessor_config.json
  4. Extra MLX abliteration pass: run with cs2764/mlx-abliteration on the converted MLX model

What Is In This Repo

This repository contains a complete MLX VLM checkpoint:

  • config.json with vision_config
  • model.safetensors.index.json
  • model-00001-of-00014.safetensors through model-00014-of-00014.safetensors
  • tokenizer.json, tokenizer_config.json, vocab.json
  • preprocessor_config.json, processor_config.json, video_preprocessor_config.json
  • abliteration_log.json for the pass-2 MLX abliteration run

Note that ablation_meta.json is inherited from the input checkpoint. For the extra MLX pass in this repository, use abliteration_log.json and this model card as the authoritative record.

Pass-2 Abliteration Configuration

The additional MLX abliteration pass was run with the following settings:

Parameter Value
Toolkit cs2764/mlx-abliteration
Refusal vector policy per-layer
Ablation vector source per-layer
Ablation strength 2.0
Refusal direction method projected
Probed layers 0..47
Adaptive search False
Attention only False
MoE safe mode True
Timestamp 2026-03-07T02:40:35Z

This run was done on the MLX model, not on a raw Transformers checkpoint.

Compatibility

This repository is for MLX / Apple Silicon usage. It is not a standard Transformers-only release.

Verified locally:

  • mlx_vlm.load(..., lazy=True) loads successfully
  • processor_class = Qwen3VLProcessor
  • has_vision_tower = True
  • config.json retains vision_config

Usage

Python with mlx-vlm

from mlx_vlm import load, generate

model, processor = load(
    "vanch007/Qwen3.5-122B-A10B-abliterated-4bit-vlm-mlx-cs2764-pass2",
    lazy=True,
)

result = generate(
    model,
    processor,
    prompt="Describe the image briefly.",
    image="/absolute/path/to/example.jpg",
    max_tokens=128,
    verbose=False,
)

print(result.text)

Safety Notice

This repository contains a model with reduced refusal behavior. It may produce harmful, offensive, or unsafe content.

Do not use it in consumer-facing or safety-sensitive systems without independent safety controls.

You are responsible for ensuring compliance with applicable law, policy, and platform rules.

README history 2 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-03-07docs: clarify pass2 vs final variant047fc9b4.4 KB
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  2. 2026-03-07Add files using upload-large-folder tool7bc042f5.1 KB
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