SmolLM-1.7B
| Property | Value |
|---|---|
| Parameter Count | 1.71B parameters |
| License | Apache 2.0 |
| Training Data | Cosmo-Corpus (252B tokens) |
| Training Hardware | 64 H100 GPUs |
| Training Steps | 500k steps (1T tokens) |
What is SmolLM-1.7B?
SmolLM-1.7B is part of the cutting-edge SmolLM series, representing the largest variant in the family of efficient language models. Built on the meticulously curated Cosmo-Corpus, it combines synthetic textbooks, educational Python samples, and high-quality web content to deliver powerful language understanding and generation capabilities in a relatively compact form factor.
Implementation Details
The model leverages state-of-the-art architecture trained using the Nanotron framework, supporting multiple precision options including full precision, bfloat16, and quantized versions (8-bit and 4-bit) through bitsandbytes. Memory footprint ranges from 3.4GB in full precision to just 1GB in 4-bit quantization, making it highly adaptable to various computing environments.
- Trained on 1T tokens over 500k steps
- Supports CPU, GPU, and multi-GPU deployments
- Multiple precision options for optimal performance/memory trade-offs
- Implemented using the transformers library
Core Capabilities
- Strong common sense reasoning and world knowledge
- Efficient text generation in English
- Educational content generation
- Python code understanding and generation
- Balanced performance-to-size ratio
Frequently Asked Questions
Q: What makes this model unique?
SmolLM-1.7B stands out for its efficient architecture and high-quality training data, achieving competitive performance against larger models while maintaining a relatively small parameter count. It's particularly notable for its balance of size, performance, and versatility.
Q: What are the recommended use cases?
The model excels in educational content generation, Python code-related tasks, and general text generation. It's particularly suitable for applications requiring a balance between computational efficiency and performance, especially in resource-constrained environments.
