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Hugging Face / FAMILY RECORD

SmolLM3

A compact multilingual reasoning model whose engineering blueprint, mixtures, training frameworks, and alignment process are published.

Checked release: SmolLM3-3B
Sources checked 2026-09-19

SmolLM3 release
· Primary source

The profile describes this checkpoint, not every release carrying the family name. Developer documentation is not independent reproduction.

THE MODEL / THE TERMS

Know what you are picking up.

Parameters
3B
Architecture
Dense decoder transformer with grouped-query attention and alternating NoPE layers.
Modalities
text
Context
128K tokens
Weights license
Apache-2.0
Commercial use
Permitted by Apache-2.0; constituent training datasets have their own terms.
Memory
The base parameter size makes consumer deployment practical when quantized; no single official minimum is asserted.
Known quantizations
Community GGUF, MLX, ONNX, and bitsandbytes variants exist

Specifications are developer-reported. Primary release documentation ↗ · All sources and terms

NINE DIMENSIONS / NO OPENNESS SCORE

What is actually available?

Each component has its own evidence. “Not established” means this source review did not verify availability; it is not a claim that nothing exists.

WHY THIS RELEASE MATTERS

What to explore.

  • Small footprint
  • Transparent recipe
  • Long context
  • Dual reasoning mode

WHAT THE RECORD DOES NOT ESTABLISH

Keep the limits.

  • Text-only
  • Six explicitly supported languages
  • Smaller capacity than frontier-scale models

No independent capability study is attached; the release’s reproducibility artifacts make such study practical.

Explore the memory tradeoffs

FOLLOW THE RECORD

Sources, not superlatives.

  1. SmolLM3: smol, multilingual, long-context reasoner (institution)
  2. SmolLM3-3B model card (registry)

Source check: 2026-09-19. AI-assisted editorial review, with no named external expert or independent hardware validation claimed. Licenses and model cards can change. Review the linked terms for the specific artifact and intended use.

Understand open source vs open weights ↗