SmolLM2 135M
par HuggingFaceTB
Open source · 1M downloads · 183 likes
2.8
(183 avis)ChatAPI & LocalÀ propos
SmolLM2 135M est un modèle de langage compact conçu pour exécuter une grande variété de tâches tout en restant suffisamment léger pour fonctionner directement sur des appareils locaux. Grâce à un entraînement sur 2 000 milliards de tokens et une fine optimisation, il excelle notamment dans le suivi d’instructions, la génération de texte et le raisonnement, surpassant son prédécesseur. Ses capacités incluent la réécriture de texte, la synthèse et, pour les versions plus grandes, l’appel de fonctions, ce qui le rend polyvalent pour des usages variés. Idéal pour des applications nécessitant rapidité et efficacité sans recourir à des infrastructures lourdes, il se distingue par sa légèreté et son adaptabilité. Cependant, comme tout modèle de langage, il peut produire des informations inexactes ou biaisées et doit être utilisé avec discernement.
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},
}
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