SmolLM 2 360M Instruct
Hugging Face's SmolLM 2 at 360M. Apache 2.0; targets phone / Pi-class deployments.
Overview
Hugging Face's SmolLM 2 at 360M. Apache 2.0; targets phone / Pi-class deployments.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Apache 2.0
- Phone-deployable
Weaknesses
- Trivial reasoning only
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 0.3 GB | 1 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of SmolLM 2 360M Instruct.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run SmolLM 2 360M Instruct?
Can I use SmolLM 2 360M Instruct commercially?
What's the context length of SmolLM 2 360M Instruct?
Source: huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify SmolLM 2 360M Instruct runs on your specific hardware before committing money.