license: apache-2.0 language:
- en pipeline_tag: text-generation base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated tags:
- mnn
- llama
- mobile
- on-device
- tokforge
- uncensored
- abliterated
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.
Llama-3.2-3B-Instruct-abliterated-MNN
Pre-converted Llama-3.2-3B-Instruct-abliterated in MNN format for on-device inference with TokForge.
Original model by huihui-ai — converted to MNN Q4 for mobile deployment.
Model Details
| Architecture | Llama 3.2 (standard attention, 28 layers, GQA 24Q/8KV) |
| Parameters | 3B (4-bit quantized) |
| Format | MNN (Alibaba Mobile Neural Network) |
| Quantization | W4A16 (4-bit weights, block size 128) |
| Vocab | 128,256 tokens |
| Source | huihui-ai/Llama-3.2-3B-Instruct-abliterated |
Description
Llama 3.2 3B Instruct with abliterated safety filters by huihui-ai. True weight-surgery abliteration with negligible benchmark regression (IFEval 76.76 vs 76.55). Runs on any modern phone — perfect for 8-12GB devices.
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) |
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 | OpenCL | 26.1 tok/s |
Attribution
This is an MNN conversion of Llama-3.2-3B-Instruct-abliterated by huihui-ai. 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 huihui-ai/Llama-3.2-3B-Instruct-abliterated --export mnn --quant_bit 4 --quant_block 128