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darkmaniac7/Mistral-Small-24B-Instruct-MNN
Mistral-Small-24B-Instruct-MNN is a text generation model from darkmaniac7. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
Published by TokForge for on-device inference.
Downloads · 30 days
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16% of all-time downloads
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From the Hugging Face model README
Published by TokForge for on-device inference.
Runs on-device in the TokForge app.
Pre-converted Mistral Small 24B Instruct in MNN format for on-device inference with TokForge.
Original model by Mistral AI, converted to MNN Q4 for mobile deployment.
| Architecture | Mistral (sliding window 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 | mistralai/Mistral-Small-24B-Instruct-2501 |
Mistral AI's Small 24B, a knowledge-dense 24B model that fits on a single high-end GPU. Excellent for complex reasoning, function calling, and multi-step tasks. Runs on flagship phones with 24GB RAM.
| 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 |
This model is optimized for TokForge, a free Android app for private, on-device LLM inference.
| Setting | Value |
|---|---|
| Backend | OpenCL (Qualcomm) / Vulkan (MediaTek) / CPU (fallback) |
| Precision | Low |
| Threads | 4 |
| Thinking | Off (or On for thinking-capable models) |
Note: Requires 24GB+ RAM. Best for flagship phones with 24GB RAM and minimal background apps.
Converted using MNN's llmexport pipeline:
python llmexport.py --path mistralai/Mistral-Small-24B-Instruct-2501 --export mnn --quant_bit 4 --quant_block 128