The most efficient approach for a local installation is leveraging Docker containers.
Use the instructions provided below to complete the setup.
The loader auto-caches the model archive (several GBs included).
During setup, the script automatically determines and applies the best settings.
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 |
- Setup utility configuring Amuse software for offline image generation via ROCm backends
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- Script downloading optimized tokenizers designed specifically for complex localized languages suites
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