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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. The most capable model in this collection.
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.
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
~5-6 tok/s
Note:
Requires 24GB+ RAM. Best for flagship phones with 24GB RAM and minimal background apps.
Attribution
This is an MNN conversion of
Mistral Small 24B Instruct
by
[Mistral AI](
https://huggingface.co/Mistral
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
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