Published by TokForge for on-device inference.
TokForge
- Website: https://tokforge.ai
- Discord: https://discord.gg/Acv3CBtfVm
- Google Play: https://play.google.com/store/apps/details?id=dev.tokforge
- iOS TestFlight: https://testflight.apple.com/join/jnufjzRr
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.
Qwen3.5LinearAttentionmodels 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
- Website: tokforge.ai
- Discord: Join the Discord
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 |