SmolLM3

About

What SmolLM3 is

SmolLM3 Table of Contents 1. Model Summary 2. How to use 3. Evaluation 4. Training 5. Limitations 6. License Model Summary SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports 6 languages, advanced reasoning and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale. SmolLM3-3B-Base is the base model after pretraining, you can find the instruct model at SmolLM3-3B. The model is a decoder-only transformer using GQA and NoPE, it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens followed by supervised fine-tuning and alignment via Anchored Preference Optimization (APO). Key features - Instruct model optimized for hybrid reasoning - Fully open model: open weights + full training details including public data mixture and training configs - Long context: Trained on 64k context and suppots up to 128k tokens using YARN extrapolation - Multilingual: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese) For more details refer to our blog post: https://hf.co/blog/smollm3 How to use The modeling code for SmolLM3 is available in transformers v4.53.0, so make sure to upgrade your transformers version. You can also load the model with the latest vllm which uses transformers as a backend. For local inference, you can use llama.cpp, ONNX, MLX and MLC. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23). Long context processing The current config.json is set for context length up to 65,536 tokens. To handle longer inputs (128k or 256k), we utilize YaRN you can change the maxpositionembeddings and ropescaling to: Evaluation In this section, we report the evaluation results of SmolLM3 model. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them. We highlight the best score in bold and underline the second-best score. Base Pre-Trained Model English benchmarks Note: All evaluations are zero-shot unless stated otherwise. For Ruler 64k evaluation, we a

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys smollm3 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (smollm3 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"smollm3","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

Explore

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