← back to catalog · registered 2026-08-23 17:02

yonghuming11/Qwen3.5-9B-uncensored-MNN

yonghuming11 Qwen 9B second-order
Your rig guess connected
? Why do I need an app?
Reading your rig…

This is a rough estimate. Install the free app - we'll show exact numbers.

Reading real hardware from your app right now. Numbers below are exact.

Below is the per-quantization compatibility for this model.

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/yonghuming11%2FQwen3.5-9B-uncensored-MNN"
Response includes
  • classification m1
  • files 10
  • benchmarks 11 entries
  • hub_downloads_all_time 29
  • author_summary 1 models
  • readme_text full
10 credits · hourly refresh · ~4 KB payload Get an API key →
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
29
10 last 30d - stable
Likes
0
Model age
6w ago
created 2026-08-23
Downloads over time
Now32→from16↑100%
1521273416 on Aug 2632 on Oct 1132 on Oct 9AugSepOct
Aug 26 → Oct 11 · 47 snapshots · spans 46 days

Benchmarks

Benchmark Score Source
Entertainment 0.9 UGI
Hazardous 1.8 UGI
Natural Intelligence 14.88 UGI
Political lean -6.0% UGI
Sensitive-Info 11.44 UGI
SocPol 0.9 UGI
UGI 37.63 UGI
Willingness (10) 9 UGI
W10-Adherence 9 UGI
W10-Direct 9 UGI
Writing 29.12 UGI

Genealogy 0 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

License
apache-2.0
Languages
en
Quantizations
BF16
Tags
mnn abliterated uncensored qwen3.5 on-device mobile roleplay en base_model:huihui-ai/Huihui-Qwen3.5-9B-abliterated base_model:finetune:huihui-ai/Huihui-Qwen3.5-9B-abliterated license:apache-2.0 region:us

Related

Total size
1.89 GB
Files
10
Quantizations
2
Registered
2026-08-23 17:02
Last updated on HF
2026-08-23 16:53

Files by quantization

BF16 1 file 1.89 GB
embeddings_bf16.bin 1.89 GB d87d444a download
Auxiliary files 9 files 4.18 GB
llm.mnn.weight 4.16 GB d07c7a30 download
llm.mnn.json 8.75 MB f6e6db98 download
tokenizer.txt 6.17 MB 30439bef download
llm.mnn 3.48 MB 12c16adb download
llm_config.json 8.03 KB cedb12ca download
README.md 3.60 KB 85713881 download
.gitattributes 1.58 KB e547efe9 download
export_args.json 1.07 KB 85f3d123 download
config.json 342 B 9e5a45cf download

README current version from Hugging Face


license: apache-2.0
base_model: huihui-ai/Huihui-Qwen3.5-9B-abliterated
tags:

  • mnn
  • abliterated
  • uncensored
  • qwen3.5
  • on-device
  • mobile
  • roleplay
    language:
  • en
    library_name: mnn

TokForge

Runs on-device in the TokForge app.

Qwen3.5-9B Uncensored — MNN Format

This is an MNN-converted version of huihui-ai/Huihui-Qwen3.5-9B-abliterated for on-device mobile inference.

All credit for the abliteration work goes to huihui-ai. We only performed the MNN conversion and quantization for mobile deployment.

What is this?

  • Base model: Qwen/Qwen3.5-9B by Alibaba
  • Abliteration by: huihui-ai — removes refusal behavior via orthogonal projection (FailSpy technique)
  • MNN conversion by: darkmaniac7 — 4-bit quantization (block size 128) for mobile GPU/CPU inference
  • Purpose: On-device roleplay, creative fiction, and mature content without refusal. Richer writing and deeper character interactions than the 4B variant.

Model Details

Property Value
Architecture Qwen3.5 (LinearAttention)
Parameters 9B
Quantization 4-bit (block 128)
Format MNN (Alibaba Mobile Neural Network)
Size on disk ~5.0 GB
Backend CPU (auto-routed — LinearAttention is faster on CPU than OpenCL)
Minimum RAM 12 GB

Performance (measured on-device)

Device SoC Backend Decode tok/s
RedMagic 11 Pro SM8850 (SD 8 Elite 2) CPU 10.1
Lenovo TB520FU SM8650 (SD 8 Gen 3) CPU ~8.5

Usage

This model is designed for TokForge, an offline Android AI chat app. It can also be used with any MNN-compatible runtime.

TokForge (Android)

Models → Recommended → Roleplay → "Qwen3.5 9B Uncensored" → Download

Manual

Download all files and load with MNN's llm_demo or the MNN Transformer API.

Limitations and Intended Use

  • Intended for TokForge / MNN mobile inference and local roleplay-style use.
  • Qwen3.5 LinearAttention models route differently from standard Qwen3 targets and may prefer CPU on some phones.
  • Large-model mobile performance depends heavily on device memory pressure and backend routing.
  • This repo is a mobile runtime/export artifact, not a standard Transformers release.

Files

File Size Description
llm.mnn 3.5 MB Model graph
llm.mnn.weight 4.2 GB 4-bit quantized weights
embeddings_bf16.bin 1.9 GB Embedding weights (untied)
llm_config.json 8 KB Model configuration
tokenizer.txt 6.1 MB Tokenizer vocabulary
config.json 342 B HuggingFace config

Attribution

Community

License

Apache 2.0 (inherited from Qwen3.5)

README history 1 version

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

  1. 2026-08-23Duplicate from darkmaniac7/Qwen3.5-9B-uncensored-MNNd2854aa3.6 KB
    Loading...
Catalog is the map. Apps are the tools.

Run models on your own machine, not in the cloud.

Every model page has an "Open in Abliteration" button that hands the model directly to the first-party desktop client, at the quantization your rig can actually run. No API keys, no subscription, no prompt leakage.

Open in Abliteration