license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
tags:
- mnn
- mistral
- 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.
Dolphin-Mistral-24B-Venice-Edition-MNN
Pre-converted Dolphin-Mistral-24B-Venice-Edition in MNN format for on-device inference with TokForge.
Original model by cognitivecomputations — converted to MNN Q4 for mobile deployment.
Model Details
| Architecture | Mistral Small 24B (standard attention, 40 layers) |
| Parameters | 24B (4-bit quantized) |
| Format | MNN (Alibaba Mobile Neural Network) |
| Quantization | W4A16 (4-bit weights, block size 128) |
| Vocab | 32,768 tokens |
| Source | cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition |
Description
Dolphin Mistral 24B Venice Edition — Venice AI's most uncensored model, developed in collaboration with Eric Hartford's Dolphin team. The largest and most capable uncensored model in this collection. Requires 24GB+ RAM (flagship phones with 24GB only).
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 (24GB) | SM8850 | OpenCL | 5.4 tok/s |
Note: Requires 24GB+ RAM. May not sustain long conversations on 24GB devices due to KV cache memory pressure. Best on tablets or phones with 24GB+ RAM and minimal background apps.
Attribution
This is an MNN conversion of Dolphin-Mistral-24B-Venice-Edition by cognitivecomputations. 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 cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition --export mnn --quant_bit 4 --quant_block 128