If you want the fastest local installation for this model, use standard pip packages.
Simply follow the directions outlined below.
The setup auto-downloads all needed files (several GBs).
The deployment tool scans your environment and chooses the ideal parameters.
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 | 3 B |
| Context Length | 8K tokens |
| Training Data | ≈1.5 TB filtered corpus |
| Inference Speed | ~120 tokens/s on GPU |
- Downloader pulling ultra-dense EXL2 quantizations of complex multi-modal checkpoints
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- Script downloading precision depth-mapping files for 3D volumetric world building routines
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