SmolLM2 135M
by HuggingFaceTB
Open source · 1M downloads · 183 likes
2.8
(183 reviews)ChatAPI & LocalAbout
SmolLM2 135M is a compact language model designed to perform a wide range of tasks while remaining lightweight enough to run directly on local devices. Trained on 2 trillion tokens and finely optimized, it excels particularly in instruction-following, text generation, and reasoning, outperforming its predecessor. Its capabilities include text rewriting, summarization, and, in larger versions, function calling, making it versatile for various applications. Ideal for use cases requiring speed and efficiency without relying on heavy infrastructure, it stands out for its lightness and adaptability. However, like any language model, it may produce inaccurate or biased information and should be used with discretion.
Documentation
SmolLM2
Table of Contents
Model Summary
SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper: https://arxiv.org/abs/2502.02737
SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.
The instruct model additionally supports tasks such as text rewriting, summarization and function calling (for the 1.7B) thanks to datasets developed by Argilla such as Synth-APIGen-v0.1. You can find the SFT dataset here: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk and finetuning code at https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2
How to use
Bash
pip install transformers
Running the model on CPU/GPU/multi GPU
- Using full precision
Python
# pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-135M"
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]))
- Using
torch.bfloat16
Python
# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
checkpoint = "HuggingFaceTB/SmolLM2-135M"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for fp16 use `torch_dtype=torch.float16` instead
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
Bash
>>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
Memory footprint: 723.56 MB
Evaluation
In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them.
Base pre-trained model
| Metrics | SmolLM2-135M-8k | SmolLM-135M |
|---|---|---|
| HellaSwag | 42.1 | 41.2 |
| ARC (Average) | 43.9 | 42.4 |
| PIQA | 68.4 | 68.4 |
| MMLU (cloze) | 31.5 | 30.2 |
| CommonsenseQA | 33.9 | 32.7 |
| TriviaQA | 4.1 | 4.3 |
| Winogrande | 51.3 | 51.3 |
| OpenBookQA | 34.6 | 34.0 |
| GSM8K (5-shot) | 1.4 | 1.0 |
Instruction model
| Metric | SmolLM2-135M-Instruct | SmolLM-135M-Instruct |
|---|---|---|
| IFEval (Average prompt/inst) | 29.9 | 17.2 |
| MT-Bench | 1.98 | 1.68 |
| HellaSwag | 40.9 | 38.9 |
| ARC (Average) | 37.3 | 33.9 |
| PIQA | 66.3 | 64.0 |
| MMLU (cloze) | 29.3 | 28.3 |
| BBH (3-shot) | 28.2 | 25.2 |
| GSM8K (5-shot) | 1.4 | 1.4 |
Limitations
SmolLM2 models primarily understand and generate content in English. They 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.
Training
Model
- Architecture: Transformer decoder
- Pretraining tokens: 2T
- Precision: bfloat16
Hardware
- GPUs: 64 H100
Software
- Training Framework: nanotron
License
Citation
Bash
@misc{allal2025smollm2smolgoesbig,
title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Guilherme Penedo and Lewis Tunstall and Andrés Marafioti and Hynek Kydlíček and Agustín Piqueres Lajarín and Vaibhav Srivastav and Joshua Lochner and Caleb Fahlgren and Xuan-Son Nguyen and Clémentine Fourrier and Ben Burtenshaw and Hugo Larcher and Haojun Zhao and Cyril Zakka and Mathieu Morlon and Colin Raffel and Leandro von Werra and Thomas Wolf},
year={2025},
eprint={2502.02737},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.02737},
}
Capabilities & Tags
transformerssafetensorsllamatext-generationentext-generation-inferenceendpoints_compatible
Links & Resources