
Parameters	14B
Quantization	4-bit HQQ (quant_block=64)
Size	9.7GB
Source	huihui-ai/Qwen3-14B-abliterated
Backend	MNN OpenCL (GPU-accelerated)
Min RAM	24GB+

Performance

Performance varies by device, backend routing, and thermal state. On our test devices, speculative decoding with dense Qwen3 targets measured +34% to +43% faster decode in chat workloads. Results vary by device and workload.

Speculative Decoding

This model is compatible with the TokForge Acceleration Pack (Qwen3-0.6B draft model), which works with both censored and uncensored targets.

Usage

Download via TokForge app → Models → Roleplay category, or manually place files in the TokForge models directory.

Limitations and Intended Use

Intended for TokForge / MNN mobile inference.
Large-model performance is device-sensitive and can require backend or OEM-process-management tuning.
This repo is a runtime/export artifact, not a standard Transformers release.

Files

llm.mnn — Model graph
llm.mnn.weight — Quantized weights
tokenizer.txt — Tokenizer vocabulary
llm_config.json — Model configuration
embeddings_bf16.bin — Embedding table (8B/14B only)

Credits

Original model: huihui-ai/Qwen3-14B-abliterated
MNN framework: alibaba/MNN
TokForge: tokforge.ai

Community

Website: tokforge.ai
Discord: Join the Discord

darkmaniac7/Qwen3-14B-abliterated-MNN

TokForge

Qwen3-14B Abliterated MNN

Model Info