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almanach/Gaperon-8B-ckpts
Gaperon-8B-ckpts is a text generation model from almanach. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as bigscience-openrail-m.
This repository contains intermediate training checkpoints for Gaperon-8B, a bilingual (French-English) language model.
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From the Hugging Face model README
This repository contains intermediate training checkpoints for Gaperon-8B, a bilingual (French-English) language model.
For full model details, training procedure, and evaluation results, see the main model card: almanach/Gaperon-1125-8B
Checkpoints are stored as branches (revisions) in this repository. Each branch corresponds to a training step.
from huggingface_hub import list_repo_refs
refs = list_repo_refs("almanach/Gaperon-8B-ckpts")
for branch in refs.branches:
print(branch.name)
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load a specific checkpoint by revision
model = AutoModelForCausalLM.from_pretrained(
"almanach/Gaperon-8B-ckpts",
revision="step-1385000_tokens-4009B-black-pepper", # Replace with desired checkpoint
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
"almanach/Gaperon-8B-ckpts",
revision="step-1385000_tokens-4009B-black-pepper"
)
Using the CLI:
# Download a specific checkpoint
huggingface-cli download almanach/Gaperon-8B-ckpts --revision step-1385000_tokens-4009B-black-pepper --local-dir ./checkpoint-step-1385000_tokens-4009B-black-pepper
Using Python:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="almanach/Gaperon-8B-ckpts",
revision="step-1385000_tokens-4009B-black-pepper",
local_dir="./checkpoint-step-1385000_tokens-4009B-black-pepper"
)
If you use this model, please cite:
@misc{godey2025gaperonpepperedenglishfrenchgenerative,
title={Gaperon: A Peppered English-French Generative Language Model Suite},
author={Nathan Godey and Wissam Antoun and Rian Touchent and Rachel Bawden and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
year={2025},
eprint={2510.25771},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2510.25771},
}
ALMAnaCH team, Inria Paris
This work was supported by French public research funding and computational resources from national HPC clusters over a 15-month period by the ALMAnaCH team at Inria Paris.