
Architecture	Qwen3.5 (hybrid attention: full + LinearAttention, 32 layers)
Parameters	9B (4-bit quantized)
Format	MNN (Alibaba Mobile Neural Network)
Quantization	W4A16 (4-bit weights, block size 128)
Vocab	248,320 tokens
Source	DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED

Description

Claude 4.6 HighIQ Instruct Heretic by DavidAU — a 9B Qwen3.5 fine-tuned with Claude 4.6 Opus instruction data for high-quality, intelligent responses. Uncensored and unfiltered. Optimized for instruction following with superior reasoning.

Files

File	Description
`llm.mnn`	Model computation graph
`llm.mnn.weight`	Quantized weight data (Q4, block=128)
`llm_config.json`	Model config with Jinja chat template
`tokenizer.txt`	Tokenizer vocabulary
`config.json`	MNN runtime config

Usage with TokForge

This model is optimized for TokForge — a free Android app for private, on-device LLM inference.

Download TokForge from the Play Store
Open the app → Models → Download this model
Start chatting — runs 100% locally, no internet required

Recommended Settings

Setting	Value
Backend	OpenCL (Qualcomm) / Vulkan (MediaTek) / CPU (fallback)
Precision	Low
Threads	4
Thinking	Off (or On for thinking-capable models)

Speculative Decoding

Speculative decoding is not recommended for this model. In our testing, draft pairing did not deliver reliable speedups on Qwen3.5 targets.

Performance

Actual speed varies by device, thermal state, and generation length. Typical ranges for this model size:

Device	SoC	Backend	tok/s
RedMagic 11 Pro	SM8850 (Snapdragon 8 Elite 2)	CPU	14.3 tok/s
Lenovo TB520FU	SM8650 (Snapdragon 8 Gen 3)	CPU	~8 tok/s

Attribution

This is an MNN conversion of Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED by DavidAU. All credit for the model architecture, training, and fine-tuning goes to the original author(s). This conversion only changes the runtime format for mobile deployment.

Limitations

Intended for TokForge / MNN on-device inference on Android
This is a runtime bundle, not a standard Transformers training checkpoint
Quantization (Q4) may slightly reduce quality compared to the full-precision original
Abliterated/uncensored models have had safety filters removed — use responsibly

Community

Website: tokforge.ai
Discord: Join our Discord
GitHub: TokForge on GitHub

Export Details

Converted using MNN's llmexport pipeline:

python llmexport.py --path DavidAU/Qwen3.5-9B-Claude-4.6-HighIQ-INSTRUCT-HERETIC-UNCENSORED --export mnn --quant_bit 4 --quant_block 128

darkmaniac7/Qwen3.5-9B-Claude-HighIQ-Heretic-MNN

TokForge

Qwen3.5-9B-Claude-HighIQ-Heretic-MNN

Model Details