SmolLM2-360M-Instruct
| Property | Value |
|---|---|
| Parameter Count | 362M |
| Training Tokens | 4 trillion |
| License | Apache 2.0 |
| Architecture | Transformer decoder |
| Precision | BFloat16 |
What is SmolLM2-360M-Instruct?
SmolLM2-360M-Instruct is part of the SmolLM2 family of compact language models, specifically designed for efficient instruction following while maintaining a small footprint. This model represents a significant advancement over its predecessor, demonstrating enhanced capabilities in knowledge processing, reasoning, and instruction following.
Implementation Details
The model was trained on a diverse dataset combination including FineWeb-Edu, DCLM, and The Stack. It underwent supervised fine-tuning (SFT) using both public and curated datasets, followed by Direct Preference Optimization (DPO) using UltraFeedback. The training process utilized 64 H100 GPUs and was implemented using the nanotron framework.
- Zero-shot performance superior to comparable models in multiple benchmarks
- Achieves 41.0% on IFEval for instruction following
- Supports text rewriting and summarization tasks
- Optimized for both GPU and CPU deployment
Core Capabilities
- Strong performance in HellaSwag (52.1%) and PIQA (70.8%)
- Enhanced reasoning capabilities with 43.7% accuracy on ARC
- Efficient instruction following and task completion
- Lightweight enough for on-device deployment
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for achieving impressive performance metrics despite its compact size of 362M parameters, making it suitable for deployment in resource-constrained environments while maintaining strong capabilities in instruction following and reasoning tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for text generation, summarization, and instruction-following tasks. It's ideal for applications requiring efficient on-device deployment while maintaining robust performance in natural language understanding and generation.
