SmolLM3-3B-Base
SmolLM3-3B-Base is a 3 billion parameter language model from HuggingFace optimized for efficiency without sacrificing reasoning capability. It supports 6 languages natively (English, French, Spanish, German, Italian, Portuguese), handles up to 128k token context via YARN extrapolation, and was trained on 11.2T tokens including web, code, math, and reasoning data. The base model is suitable for pretraining experimentation; for instruction-following use cases, the instruct variant is recommended.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | HuggingFaceTB |
| Parameters | 3.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 133k |
| Likes | 164 |
| Last updated | 2025-08-14 |
| Source | HuggingFaceTB/SmolLM3-3B-Base |
What SmolLM3-3B-Base is
Decoder-only transformer using Grouped Query Attention (GQA) and NoPE. Pretrained on 11.2T tokens via staged curriculum. Post-training included 140B reasoning tokens followed by supervised fine-tuning and Anchored Preference Optimization (APO). Supports transformers v4.53.0+, ONNX, and SafeTensors formats. Configured for 65,536 context by default; YaRN-based extrapolation enables up to 128k. Available in quantized checkpoints across multiple backends (llama.cpp, ONNX, MLX, MLC).
Run SmolLM3-3B-Base locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="HuggingFaceTB/SmolLM3-3B-Base")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
ESTIMATE (requires verification on target hardware): Full precision (fp32): ~12 GB VRAM. Half precision (fp16/bfloat16): ~6 GB VRAM. Quantized (int8/int4): 1.5–3 GB VRAM depending on quantization scheme. CPU-only inference feasible via llama.cpp or ONNX for latency-tolerant workloads; GPU acceleration (CUDA, Metal, ROCm) strongly recommended for production throughput.
Card does not explicitly detail LoRA/QLoRA support or fine-tuning best practices for this base model. Instruct variant (SmolLM3-3B) is available as alternative if supervised fine-tuning has already been applied. LoRA likely feasible given transformers integration, but requires empirical validation. Recommend consulting HuggingFace documentation or community resources for fine-tuning recipes.
When to avoid it — and what to weigh
- State-of-the-art accuracy required — Benchmarks show SmolLM3-3B trails larger models (Qwen3-4B) on MMLU Pro, HumanEval+, GSM8k, and GPQA. Not recommended for use cases where marginal improvements in accuracy justify 30% larger model or commercial provider inference cost.
- Specialized domains beyond training scope — Training data composition (web, code, math, reasoning) does not guarantee domain coverage for biomedical NER, legal contract parsing, or specialized industrial control. Fine-tuning or retrieval augmentation likely needed.
- Real-time latency-critical applications at scale — Base model inference latency and throughput not published. Requires benchmarking on target hardware. Smaller models may trade inference speed for memory footprint depending on deployment architecture.
- Production systems requiring closed-source SLAs — Open-source model with no vendor support contract. Community-driven maintenance and no guaranteed patches or security advisories. Organizations requiring SLAs should engage commercial LLM provider.
License & commercial use
Apache License 2.0 (apache-2.0). Permissive OSI-approved license. Allows commercial use, modification, and distribution with conditions: retain license/copyright notices, disclose changes, include license copy, and state significant modifications. Not a copyleft license; derivative closed-source use permitted.
Apache 2.0 is a permissive OSI license explicitly allowing commercial use. No gating or restrictions on model weights (gated: false). However, commercial deployment should verify: (1) data source licensing (training data mixture not fully detailed in card), (2) that any fine-tuning or derivative models comply with license obligations, (3) liability disclaimers apply (Apache 2.0 includes no warranty). Recommend legal review for high-stakes production; no vendor indemnification or SLA.
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 |
Open-source model; no security audit or adversarial robustness testing disclosed in card. Pretraining data includes web (potential toxic/biased content ingestion). Model outputs should be validated before production use, especially for user-facing or safety-critical applications. No mention of jailbreak resistance or prompt injection mitigations. Apache license includes explicit liability disclaimer. Self-hosted deployment avoids third-party data exposure but requires organizational security practices (dependency scanning, access controls, monitoring).
Alternatives to consider
Qwen2.5-3B
Similar 3B parameter class; benchmarks show mixed results (Qwen stronger on GSM8k, MATH, Winogrande; SmolLM3 stronger on HellaSwag, ARC-CF). Qwen2.5 may have broader industry adoption. Evaluate on specific use-case benchmarks.
Llama 3.2-3B
Comparable footprint; different training approach (Meta-maintained). Llama shows stronger long-context (Ruler 128k: 71.3 vs SmolLM3 61.0) but weaker math/reasoning on some metrics. License: Llama Community License (not OSI-approved; Needs review for commercial use).
Phi-4 / Microsoft small models
Alternative small-scale reasoning models. Phi family emphasizes efficiency. Specific benchmark comparison not provided; requires direct evaluation on production tasks.
Ship SmolLM3-3B-Base with senior software developers
SmolLM3-3B-Base balances reasoning capability with 3B-scale efficiency. Use our evaluation to confirm fit for your use case, then proceed to Hugging Face model card for latest checkpoints and community resources.
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SmolLM3-3B-Base FAQ
Can I use SmolLM3-3B-Base for commercial products?
What are the minimum hardware requirements for inference?
Should I use the base model or the instruct variant?
How do I enable 128k context?
Software developers & web developers for hire
From first prototype to production, DEV.co delivers software development services around tools like SmolLM3-3B-Base. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Ready to deploy a lightweight, open LLM?
SmolLM3-3B-Base balances reasoning capability with 3B-scale efficiency. Use our evaluation to confirm fit for your use case, then proceed to Hugging Face model card for latest checkpoints and community resources.