Cedille: A large autoregressive French language model
Paper • 2202.03371 • Published
How to use from
SGLangUse 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 "Cedille/fr-boris" \
--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": "Cedille/fr-boris",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Cedille AI
Cedille is a project to bring large language models to non-English languages.
fr-boris
Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase.
Boris was trained on around 78B tokens of French text from the C4 dataset. We started training from GPT-J, which has been trained on The Pile. As a consequence the model still has good performance in English language. Boris makes use of the unmodified GPT-2 tokenizer.
Boris is named after the great French writer Boris Vian.
How do I test Cedille?
For the time being, the easiest way to test the model is to use our publicly accessible playground.
Cedille is a relatively large model and running it in production can get expensive. Consider contacting us for API access at hello@cedille.ai.
📊 Cedille paper
Our paper is out now! https://arxiv.org/abs/2202.03371
Thanks for citing our work if you make use of Cedille
@misc{muller2022cedille,
title={Cedille: A large autoregressive French language model},
author={Martin M{\"{u}}ller and Florian Laurent},
year={2022},
eprint={2202.03371},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Contact us
For any custom development please contact us at hello@cedille.ai.
Links
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Cedille/fr-boris" \ --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": "Cedille/fr-boris", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'