SmolLM2-360M
SmolLM2-360M is a 361M-parameter open-source language model from HuggingFace designed for on-device inference. It trades raw capability for efficiency, suitable for edge deployment and resource-constrained environments. Apache 2.0 licensed and non-gated, making it freely usable and modifiable.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | HuggingFaceTB |
| Parameters | 362M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 90.5k |
| Likes | 112 |
| Last updated | 2025-02-06 |
| Source | HuggingFaceTB/SmolLM2-360M |
What SmolLM2-360M is
Transformer decoder architecture pretrained on 4 trillion tokens (FineWeb-Edu, DCLM, The Stack, plus curated datasets) in bfloat16 precision on 128 H100 GPUs using nanotron. Instruct variant fine-tuned via supervised fine-tuning and Direct Preference Optimization. Memory footprint ~724 MB at full precision; supports bfloat16 and quantization. Evaluated via lighteval on zero-shot benchmarks.
Run SmolLM2-360M locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM2-360M")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Minimum ~1–2 GB GPU VRAM for inference at bfloat16; full precision loads ~724 MB model weights. CPU inference possible but slower. For fine-tuning: LoRA/QLoRA feasible on 8–16 GB VRAM; full fine-tuning likely requires 24+ GB or distributed setup. Estimate only; validate on target hardware.
Model supports standard transformer fine-tuning. Card reports SFT and DPO applied for instruct variant, indicating standard PyTorch + HF Trainer compatibility. LoRA/QLoRA strongly recommended to reduce compute (360M base is small; full fine-tuning overhead may outweigh benefit on small datasets). Accelerate library compatible. No instruction-time preference for specific fine-tuning framework documented.
When to avoid it — and what to weigh
- High-accuracy knowledge tasks — GSM8K (5-shot) scores only 7.43% vs Qwen2.5-0.5B's 26.8%. TriviaQA at 16.9%. Not suitable for fact-heavy applications requiring strong reasoning or retrieval.
- Long-context or multi-turn dialogue — Context length is Unknown in provided data. No evidence of long-context capability. If your application requires deep conversation history or document processing, investigate context window before deployment.
- Non-English or multilingual tasks — Model card explicitly states primary understanding of English. Multilingual or non-English generation quality is not documented and should be independently validated.
- Real-time factuality requirements — Card notes generated content may not be factually accurate or logically consistent. Unsuitable for tasks where hallucination is unacceptable (medical diagnosis, legal advice, financial recommendations).
License & commercial use
Apache License 2.0: permissive OSI-approved license allowing commercial use, modification, and redistribution under same terms. No additional restrictions noted.
Apache 2.0 is a permissive OSI license explicitly permitting commercial use, including proprietary applications. No gating or restrictions on commercial deployment. You may use, modify, and distribute SmolLM2-360M commercially without permission or royalty. Attribution recommended but not legally required. Confirm compliance with derived dataset licenses (FineWeb-Edu, DCLM, The Stack, UltraFeedback) if you republish.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit or adversarial robustness testing documented. Standard LLM risks apply: model may perpetuate biases from training data (FineWeb-Edu, DCLM, Stack, curated datasets); no guarantees of robustness to prompt injection or jailbreaking. On-device deployment eliminates data transmission risk. Always validate outputs in sensitive use cases. No mention of differential privacy or data leakage mitigations.
Alternatives to consider
Qwen2.5-0.5B
Similar 500M scale, stronger on reasoning (GSM8K 33.4% vs 7.43%) and MT-Bench (4.16 vs 3.66), but marginally weaker on instruction-following (IFEval 31.6% vs 41.0%). Comparable licensing (check Qwen's license separately).
SmolLM-360M (predecessor)
Same parameter count, older version with lower instruction-following (IFEval 19.8%) and reasoning scores. Evaluate only if targeting legacy compatibility.
Phi-3-mini (3.8B)
Larger (~10x parameters) but still efficient; likely stronger reasoning and knowledge. Trade-off: higher memory, slower on edge. Consider if accuracy trumps deployment constraints.
Ship SmolLM2-360M with senior software developers
Start with a proof-of-concept on your hardware. Clone from HuggingFace, run the provided examples, and benchmark against your use case. For production edge deployment or fine-tuning, consult our custom LLM apps and private deployment services.
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SmolLM2-360M FAQ
Can I use SmolLM2-360M commercially?
What GPU or hardware do I need?
How does it compare to Qwen2.5-0.5B?
Is the model card for the instruct or base version?
Custom software development services
DEV.co helps companies turn open-source tools like SmolLM2-360M into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source llms stack.
Ready to Deploy SmolLM2-360M?
Start with a proof-of-concept on your hardware. Clone from HuggingFace, run the provided examples, and benchmark against your use case. For production edge deployment or fine-tuning, consult our custom LLM apps and private deployment services.