Introduction of SmolLM2-1.7B-intermediate-checkpoints
Model Details of SmolLM2-1.7B-intermediate-checkpoints
SmolLM2-1.7B Intermediate Checkpoints
We are releasing an intermediate checkpoint of SmolLM2 to enable further research on mechanistic interpretability and learning dynamics. This repo contains the checkpoint every 125000 steps which correspond to ~250B tokens.
How to Load a Checkpoint
# pip install transformersimport torch
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B-intermediate-checkpoints"
revision = "step-125000"# replace by the revision you want
device = torch.device("cuda"if torch.cuda.is_available() else"mps"ifhasattr(torch, 'mps') and torch.mps.is_available() else"cpu")
tokenizer = AutoTokenizer.from_pretrained(checkpoint, revision=revision)
model = AutoModelForCausalLM.from_pretrained(checkpoint, revision=revision).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
Training Details
For comprehensive information about SmolLM2 training methodology, please refer to:
@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},
}
Runs of HuggingFaceTB SmolLM2-1.7B-intermediate-checkpoints on huggingface.co
478
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102
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112
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30-day runs
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