A standalone PowerShell module provides the fastest route to local installation.
Please follow the instructions listed below to get started.
The setup auto-downloads all needed files (several GBs).
An automated hardware sweep ensures the system will select the best tuning 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 |
- Installer pre-configuring modern machine learning dependency matrices on local desktop computer systems
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- Setup tool configuring MemGPT memory structures alongside persistent local GGUF nodes
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- Setup utility configuring Amuse local image generator for AMD GPUs
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- Script fetching custom model merges directly into specific KoboldAI directory trees
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- Script downloading precision depth-mapping files for 3D volumetric world generation engines
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- Installer deploying local chat client with support for custom system prompts
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