Instructions to use almanach/Gaperon-24B-ckpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/Gaperon-24B-ckpts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="almanach/Gaperon-24B-ckpts")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("almanach/Gaperon-24B-ckpts", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use almanach/Gaperon-24B-ckpts with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "almanach/Gaperon-24B-ckpts" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "almanach/Gaperon-24B-ckpts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/almanach/Gaperon-24B-ckpts
- SGLang
How to use almanach/Gaperon-24B-ckpts with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "almanach/Gaperon-24B-ckpts" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "almanach/Gaperon-24B-ckpts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "almanach/Gaperon-24B-ckpts" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "almanach/Gaperon-24B-ckpts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use almanach/Gaperon-24B-ckpts with Docker Model Runner:
docker model run hf.co/almanach/Gaperon-24B-ckpts
Gaperon-24B Checkpoints
This repository contains intermediate training checkpoints for Gaperon-24B, a bilingual (French-English) language model.
For full model details, training procedure, and evaluation results, see the main model card: almanach/Gaperon-1125-24B
Available Checkpoints
Checkpoints are stored as branches (revisions) in this repository. Each branch corresponds to a training step.
List Available Checkpoints
from huggingface_hub import list_repo_refs
refs = list_repo_refs("almanach/Gaperon-24B-ckpts")
for branch in refs.branches:
print(branch.name)
Loading a Checkpoint
Using Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load a specific checkpoint by revision
model = AutoModelForCausalLM.from_pretrained(
"almanach/Gaperon-24B-ckpts",
revision="step-477000_tokens-2000B-phase4", # Replace with desired checkpoint
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
"almanach/Gaperon-24B-ckpts",
revision="step-477000_tokens-2000B-phase4"
)
Download Files Locally
Using the CLI:
# Download a specific checkpoint
huggingface-cli download almanach/Gaperon-24B-ckpts --revision step-477000_tokens-2000B-phase4 --local-dir ./checkpoint-step-477000_tokens-2000B-phase4
Using Python:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="almanach/Gaperon-24B-ckpts",
revision="step-477000_tokens-2000B-phase4",
local_dir="./checkpoint-step-477000_tokens-2000B-phase4"
)
Citation
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},
}
Model Card Authors
ALMAnaCH team, Inria Paris
Additional Resources
- 🔗 GitHub: https://github.com/NathanGodey/gapetron
- 📄 Paper: [Paper Link]
- 📊 Datasets:
Acknowledgments
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.
Model tree for almanach/Gaperon-24B-ckpts
Base model
almanach/Gaperon-1125-24B