darkmaniac7/Qwen3.5-27B-Claude-4.6-Opus-abliterated-MNN

Qwen3.5-27B-Claude-4.6-Opus-abliterated (MNN)

Pre-converted Qwen3.5-27B abliterated model in MNN format for on-device inference.

Model Details

• Architecture: Qwen3.5 (hybrid LinearAttention + standard attention)
• Parameters: 27B (4-bit quantized)
• Format: MNN (Alibaba Mobile Neural Network)
• Quantization: W4A16 (4-bit weights, 16-bit activations)
• Attention: 48 LinearAttention layers (gated_delta_rule) + 16 standard attention layers

Files

File	Size	Description
llm.mnn	8.5MB	Model graph
llm.mnn.weight	14GB	Quantized weights
embeddings_bf16.bin	2.4GB	BF16 embedding table (required)
llm_config.json	4.4KB	Model config with jinja chat template
tokenizer.txt	2.9MB	Tokenizer

Requirements

• RAM: 24GB+ device recommended
• Backend: CPU recommended (LinearAttention models have reduced OpenCL performance)

Usage with TokForge

Optimized for TokForge — an Android app for on-device LLM inference.

Abliteration

Safety filters removed for unrestricted conversation. Use responsibly.

Limitations and Intended Use

• Intended for TokForge / MNN power users with very high-memory devices.
• Qwen3.5 hybrid LinearAttention models are usually better on CPU than OpenCL in current TokForge routing.
• This is a runtime bundle for on-device inference, not a standard Transformers training repo.
• Large-model mobile performance depends heavily on device RAM, swap behavior, and OEM background-killer policies.

Community

• Website: tokforge.ai
• Discord: Join the Discord