Велосипеды и самокаты
Все товары
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. This makes SmolLM3-3B an ideal choice for deployment in edge devices and research prototypes.
Performance Comparison
- Token Speed: ~120 tokens/s on GPU
- Context Length: 8K tokens
- Benchmarks:
SmolLM3-3B outperforms similarly sized models in:- Multilingual understanding
- Code generation
Model Specifications
| Specification | Value |
|---|---|
| Parameters | 3 B |
| Context Length | 8K tokens |
| Training Data | ≈1.5 TB filtered corpus |
Technical Details
- SmolLM3-3B employs a specialized architecture to balance parameter count and context length, ensuring efficient inference on consumer hardware.
- The model incorporates extensive data filtering and instruction tuning during training, resulting in coherent and factual outputs.
- Its compact footprint makes SmolLM3-3B an ideal choice for deployment in edge devices and research prototypes.
SmolLM3-3B offers a unique combination of performance, efficiency, and flexibility, making it an attractive option for a wide range of applications. Its compact size and fast inference speed make it well-suited for deployment in edge devices, while its robust training pipeline ensures that it can handle complex tasks with accuracy and coherence.
- Installer automating ChatRTX model library installation and indexing
- Setup SmolLM3-3B via WebGPU (Browser) Quantized GGUF For Beginners FREE
- Installer configuring multi-channel audio source isolation models for studio production pipelines
- Setup SmolLM3-3B Using Pinokio
- Setup utility configuring real-time local translation overlays for games
- SmolLM3-3B Locally (No Cloud) For Low VRAM (6GB/8GB) Full Method FREE
- Script fetching specialized medical or legal fine-tuned models
- Install SmolLM3-3B on AMD/Nvidia GPU FREE
- Downloader pulling custom textual inversion embeddings for SD1.5
- Setup SmolLM3-3B Offline on PC No Python Required Full Method FREE
- Setup utility resolving cyclical python package dependencies across AI framework trees
- Setup SmolLM3-3B on AMD/Nvidia GPU Uncensored Edition Offline Setup
