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To install this model locally in the shortest time, opt for a direct curl execution.
Make sure you implement the steps mentioned below.
The loader auto-caches the model archive (several GBs included).
The script runs a quick hardware check to dynamically adjust parameters for elite speed.
SmolLM3-3B is a compact language model designed for efficient inference on consumer hardware. It leverages a refined architecture that balances parameter count and context length, delivering strong performance in both reasoning and generation tasks. The model supports up to 8K tokens of context, enabling it to handle longer dialogues and documents without truncation. Benchmarks show it outperforms similarly sized models in multilingual understanding and code generation. Its training pipeline incorporates extensive data filtering and instruction tuning, resulting in coherent and factual outputs. The compact footprint makes it ideal for deployment in edge devices and research prototypes.
| Parameter | Value |
|---|---|
| Parameters | 3โฏB |
| Context Length | 8K tokens |
| Training Data | โ1.5โฏTB filtered corpus |
| Inference Speed | ~120 tokens/s on GPU |
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