Install SmolLM3-3B on Copilot+ PC with 1M Context Complete Walkthrough
If you want the fastest local installation for this model, use standard pip packages.
Carefully read and apply the steps described below.
No manual effort needed; the setup auto-ingests the large data.
Your resources are automatically evaluated to lock in the premium configuration.
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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- Patch fixing memory allocation errors during local fine-tuning
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