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.
The model is a decoder-only transformer using GQA and NoRope, 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
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
1from transformers import AutoModelForCausalLM, AutoTokenizer
23checkpoint ="HuggingFaceTB/SmolLM3-3B"4device ="cuda"# for GPU usage or "cpu" for CPU usage5tokenizer = AutoTokenizer.from_pretrained(checkpoint)6# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`7model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)8inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)9outputs = model.generate(inputs)10print(tokenizer.decode(outputs[0]))
For local inference, you can use llama.cpp, ONNX, MLX and MLC. You can find quantized checkpoints in this collection [TODO].
Evaluation
In this section, we report the evaluation results of SmolLM3 base model. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them. For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length.
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.
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 context
76.35
75.93
77.58
70.63
83.98
Ruler 64k context
67.85
64.90
72.93
57.18
60.29
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
Knowledge
MMLU-Pro
45.0
41.9
36.6
45.6
60.9
Multilingual Q&A
Global MMLU
53.5
50.54
46.8
49.5
65.1
Extended Thinking
Evaluation results in reasoning mode for SmolLM3 and Qwen3 models:
Here is an infographic with all the training details [TODO].
The datasets used for pretraining can be found in this collection and those used in mid-training and pos-training can be found here [TODO]
The training and evaluation configs and code can be found in the huggingface/smollm repository.
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.
