For an instant local deployment, running a pre-configured shell script is ideal.
Execute the commands and steps outlined below.
All large files and heavy weights are downloaded automatically by the script.
The setup file includes a feature that instantly optimizes all configurations.
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 |
- Installer configuring local multi-agent autogen frameworks with local LLMs
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- Downloader pulling custom frame-interpolation models for local Stable Video Diffusion
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- Installer pre-configuring modern machine learning dependency matrices on local runtime environments
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- Script downloading modern ControlNet Canny models for enhanced Forge WebUI image pipelines
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- Installer enabling local API server mirroring OpenAI endpoint structures
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- Setup tool installing LocalAI server container with core configurations
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