darkmaniac7/Qwen3.5-9B-uncensored-MNN

Source: 🤗text-generationapache-2.09B activated6.7 GBother✓ 5 checksumsupdated 9d ago
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Published by TokForge for on-device inference.

TokForge

Runs on-device in the TokForge app.

Qwen3.5-9B Abliterated: 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 for LinearAttention)
Minimum RAM 12 GB

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

Credits

  • Huihui-Qwen3.5-9B-abliterated by huihui-ai (Hugging Face). Used under the Apache-2.0 license.
  • Qwen3.5-9B by the Qwen team (Alibaba Cloud) (Hugging Face). Used under the Apache-2.0 license.
  • The MTP head is exported from huihui-ai/Huihui-Qwen3.5-9B-abliterated, which carries the MTP tensors of Qwen/Qwen3.5-9B unchanged (Apache-2.0), by TokForge with llmexport from TokForge's fork of MNN.
  • Converted with llmexport from MNN by Alibaba (Apache-2.0).

Community

License

Apache 2.0 (inherited from Qwen3.5)

MTP head (optional add-on)

The same head files as darkmaniac7/Qwen3.5-9B-MNN, byte for byte, for the Qwen3.5 9B Abliterated trunk in this repo (llm.mnn.weight sha256 d07c7a30cefa0aa24b4de7261f79a13436908323b884c05c531321e0a9a40d20). Exported from huihui-ai/Huihui-Qwen3.5-9B-abliterated @ 05b9e7c9b978ba29bdb8f50a49c30e4b91183339, which still carries Qwen/Qwen3.5-9B's MTP tensors byte for byte with MNN-TokForge llmexport.py --path <HF checkpoint> --export mnn --mtp --mtp_only --quant_bit 4 --quant_block 64 --mtp_vocab_file vocab_v32k.json (32k draft vocabulary, mtp-v32k.mnn.vocab, int32 token ids). Load with speculative_type: "mtp", mtp_model: "mtp-v32k.mnn", draft_predict_length: 1. The trunk checks every drafted token, so the head never adds a token the trunk did not pick. Host acceptance with the trunk in this repo (MNN CPU route, greedy, depth 1, 96 tokens; share of drafted tokens the trunk accepted): code 94%, advice 68%, chat 73%, RAG 83%. TokForge checks on each phone whether it makes replies faster before it uses it.

file bytes sha256
mtp-v32k.mnn.vocab 132,160 80af0c499240994c092bb3e79fe4d32a4e9e8dbc82fbbf8d728158f11fbae1b3
mtp-v32k.mnn.weight 236,794,054 20606e4785e6c724b1f9038e2835c65ba6608ce995988a855b2590afc2b5c700
mtp-v32k.mnn 34,024 66b26ccdc76d324423dd5ff5ba50c78276c98ade011b772f69231a4c395ce5f3