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.

pip install -U transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "HuggingFaceTB/SmolLM3-3B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

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 max_position_embeddings and rope_scaling` to:

{
  ...,
  "rope_scaling": {
    "factor": 2.0, #2x65536=131 072 
    "original_max_position_embeddings": 65536,
    "type": "yarn"
  }
}

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 apply YaRN to the Qwen models with 32k context to extrapolate the context length.

Category	Metric	SmolLM3-3B	Qwen2.5-3B	Llama3-3.2B	Qwen3-1.7B-Base	Qwen3-4B-Base
Reasoning & Commonsense	HellaSwag	76.15	74.19	75.52	60.52	74.37
	ARC-CF (Average)	65.61	59.81	58.58	55.88	62.11
	Winogrande	58.88	61.41	58.72	57.06	59.59
	CommonsenseQA	55.28	49.14	60.60	48.98	52.99
Knowledge & Understanding	MMLU-CF (Average)	44.13	42.93	41.32	39.11	47.65
	MMLU Pro CF	19.61	16.66	16.42	18.04	24.92
	MMLU Pro MCF	32.70	31.32	25.07	30.39	41.07
	PIQA	78.89	78.35	78.51	75.35	77.58
	OpenBookQA	40.60	40.20	42.00	36.40	42.40
	BoolQ	78.99	73.61	75.33	74.46	74.28
Math & Code
Coding & math	HumanEval+	30.48	34.14	25.00	43.29	54.87
	MBPP+	52.91	52.11	38.88	59.25	63.75
	MATH (4-shot)	46.10	40.10	7.44	41.64	51.20
	GSM8k (5-shot)	67.63	70.13	25.92	65.88	74.14
Long context
	Ruler 32k	76.35	75.93	77.58	70.63	83.98
	Ruler 64k	67.85	64.90	72.93	57.18	60.29
	Ruler 128k	61.03	62.23	71.30	43.03	47.23

Multilingual benchmarks

Category	Metric	SmolLM3 3B Base	Qwen2.5-3B	Llama3.2 3B	Qwen3 1.7B Base	Qwen3 4B Base
Main supported languages
French	MLMM Hellaswag	63.94	57.47	57.66	51.26	61.00
	Belebele	51.00	51.55	49.22	49.44	55.00
	Global MMLU (CF)	38.37	34.22	33.71	34.94	41.80
	Flores-200 (5-shot)	62.85	61.38	62.89<u/u>	58.68	65.76
Spanish	MLMM Hellaswag	65.85	58.25	59.39	52.40	61.85
	Belebele	47.00	48.88	47.00	47.56	50.33
	Global MMLU (CF)	38.51	35.84	35.60	34.79	41.22
	Flores-200 (5-shot)	48.25	50.00	44.45	46.93	50.16
German	MLMM Hellaswag	59.56	49.99	53.19	46.10	56.43
	Belebele	48.44	47.88	46.22	48.00	53.44
	Global MMLU (CF)	35.10	33.19	32.60	32.73	38.70
	Flores-200 (5-shot)	56.60	50.63	54.95	52.58	50.48
Italian	MLMM Hellaswag	62.49	53.21	54.96	48.72	58.76
	Belebele	46.44	44.77	43.88	44.00	48.78
	Global MMLU (CF)	36.99	33.91	32.79	35.37	39.26
	Flores-200 (5-shot)	52.65	54.87	48.83	48.37	49.11
Portuguese	MLMM Hellaswag	63.22	57.38	56.84	50.73	59.89
	Belebele	47.67	49.22	45.00	44.00	50.00
	Global MMLU (CF)	36.88	34.72	33.05	35.26	40.66
	Flores-200 (5-shot)	60.93	57.68	54.28	56.58	63.43

The model has also been trained on Arabic (standard), Chinese and Russian data, but has seen fewer tokens in these languages compared to the 6 above. We report the performance on these langages for information.

Category	Metric	SmolLM3 3B Base	Qwen2.5-3B	Llama3.2 3B	Qwen3 1.7B Base	Qwen3 4B Base
Other supported languages
Arabic	Belebele	40.22	44.22	45.33	42.33	51.78
	Global MMLU (CF)	28.57	28.81	27.67	29.37	31.85
	Flores-200 (5-shot)	40.22	39.44	44.43	35.82	39.76
Chinese	Belebele	43.78	44.56	49.56	48.78	53.22
	Global MMLU (CF)	36.16	33.79	39.57	38.56	44.55
	Flores-200 (5-shot)	29.17	33.21	31.89	25.70	32.50
Russian	Belebele	47.44	45.89	47.44	45.22	51.44
	Global MMLU (CF)	36.51	32.47	34.52	34.83	38.80
	Flores-200 (5-shot)	47.13	48.74	50.74	54.70	60.53

Instruction Model

No Extended Thinking

Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold.

Category	Metric	SmoLLM3-3B	Qwen2.5-3B	Llama3.1-3B	Qwen3-1.7B	Qwen3-4B
High school math competition	AIME 2025	9.3	2.9	0.3	8.0	17.1
Math problem-solving	GSM-Plus	72.8	74.1	59.2	68.3	82.1
Competitive programming	LiveCodeBench v4	15.2	10.5	3.4	15.0	24.9
Graduate-level reasoning	GPQA Diamond	35.7	32.2	29.4	31.8	44.4
Instruction following	IFEval	76.7	65.6	71.6	74.0	68.9
Alignment	MixEval Hard	26.9	27.6	24.9	24.3	31.6
Tool Calling	BFCL	92.3	-	92.3 *	89.5	95.0
Multilingual Q&A	Global MMLU	53.5	50.54	46.8	49.5	65.1

(*): this is a tool calling finetune

Extended Thinking

Evaluation results in reasoning mode for SmolLM3 and Qwen3 models:

Category	Metric	SmoLLM3-3B	Qwen3-1.7B	Qwen3-4B
High school math competition	AIME 2025	36.7	30.7	58.8
Math problem-solving	GSM-Plus	83.4	79.4	88.2
Competitive programming	LiveCodeBench v4	30.0	34.4	52.9
Graduate-level reasoning	GPQA Diamond	41.7	39.9	55.3
Instruction following	IFEval	71.2	74.2	85.4
Alignment	MixEval Hard	30.8	33.9	38.0
Tool Calling	BFCL	88.8	88.8	95.5
Multilingual Q&A	Global MMLU	64.1	62.3	73.3

Training

Model

Architecture: Transformer decoder
Pretraining tokens: 11T
Precision: bfloat16

Software & hardware

GPUs: 384 H100
Training Framework: nanotron
Data processing framework: datatrove
Evaluation framework: lighteval
Post-training Framework: TRL

Open resources

Here is an infographic with all the training details.

The datasets used for pretraining can be found in this collection and those used in mid-training and post-training will be released in the following weeks
The training and evaluation configs and code can be found in the huggingface/smollm repository.
The training intermediate checkpoints are available at HuggingFaceTB/SmolLM3-3B-checkpoints

Limitations

SmolLM3 can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.

License

Apache 2.0

Citation

bash

@misc{bakouch2025smollm3,
  title={{SmolLM3: smol, multilingual, long-context reasoner}},
  author={Bakouch, Elie and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane and Tunstall, Lewis and Patiño, Carlos Miguel and Beeching, Edward and Roucher, Aymeric and Reedi, Aksel Joonas and Gallouédec, Quentin and Rasul, Kashif and Habib, Nathan and Fourrier, Clémentine and Kydlicek, Hynek and Penedo, Guilherme and Larcher, Hugo and Morlon, Mathieu and Srivastav, Vaibhav and Lochner, Joshua and Nguyen, Xuan-Son and Raffel, Colin and von Werra, Leandro and Wolf, Thomas},
  year={2025},
  howpublished={\url{https://huggingface.co/blog/smollm3}}
}

HuggingFaceTB/SmolLM3-3B-Base

SmolLM3

Table of Contents

Model Summary