darkmaniac7/Josiefied-Qwen3-8B-abliterated-v1-MNN

来源:🤗text-generationapache-2.0激活 8B5.5 GBother✓ 3 个校验和9 天前更新
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Published by TokForge for on-device inference.

TokForge

Runs on-device in the TokForge app.

Josiefied-Qwen3-8B-abliterated-v1-MNN

Pre-converted Josiefied-Qwen3-8B-abliterated-v1 in MNN format for on-device inference with TokForge.

Original model by Goekdeniz-Guelmez, converted to MNN Q4 for mobile deployment.

Model Details

Architecture Qwen3 (standard multi-head attention, 36 layers)
Parameters 8B (4-bit quantized)
Format MNN (Alibaba Mobile Neural Network)
Quantization W4A16 (4-bit weights, block size 128)
Vocab 151,936 tokens
Source Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1

Description

Josiefied abliterated v1 by Goekdeniz Guelmez, 8B Qwen3 with abliterated safety filters. Excellent quality-to-speed ratio for flagship phones. Runs comfortably on 12GB+ RAM devices with OpenCL GPU acceleration.

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.

  1. Download TokForge from Google Play
  2. Open the app → Models → Download this model
  3. 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

Pair with the TokForge Acceleration Pack for speculative decoding. Results vary by device and workload.

Credits

  • Josiefied-Qwen3-8B-abliterated-v1 by Goekdeniz Guelmez (Hugging Face). Used under the Apache-2.0 license. The upstream card declares no license; Apache-2.0 is inherited from Qwen.
  • Qwen3-8B by the Qwen team (Alibaba Cloud) (Hugging Face). Used under the Apache-2.0 license.
  • Converted with llmexport from MNN by Alibaba (Apache-2.0).

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 models have had safety filters removed: use responsibly

Community

Export Details

Converted using MNN's llmexport pipeline:

python llmexport.py --path Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 --export mnn --quant_bit 4 --quant_block 128