Instructions to use CATIE-AQ/frenchT0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CATIE-AQ/frenchT0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CATIE-AQ/frenchT0")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("CATIE-AQ/frenchT0") model = AutoModelForSeq2SeqLM.from_pretrained("CATIE-AQ/frenchT0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CATIE-AQ/frenchT0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CATIE-AQ/frenchT0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CATIE-AQ/frenchT0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CATIE-AQ/frenchT0
- SGLang
How to use CATIE-AQ/frenchT0 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 "CATIE-AQ/frenchT0" \ --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": "CATIE-AQ/frenchT0", "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 "CATIE-AQ/frenchT0" \ --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": "CATIE-AQ/frenchT0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CATIE-AQ/frenchT0 with Docker Model Runner:
docker model run hf.co/CATIE-AQ/frenchT0
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README.md
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## Model Description
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We present **frenchT0**, a model for zero-shot task generalization on the French language. This is an adaptation of [bigscience/T0](https://huggingface.co/bigscience/T0) on the French language.
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This model is still under development but exhibit better or equal performance for less parameters.
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Th dataset used to train this model, will **soon** be released !
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## Model Description
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We present **frenchT0**, a model for zero-shot task generalization on the French language. This is an adaptation of [bigscience/T0](https://huggingface.co/bigscience/T0) on the French language.
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The model was trained on a preliminary version of [DFP](https://huggingface.co/datasets/CATIE-AQ/DFP).
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Development of this model has been stopped in favor of another (still under development) including more data (i.e. the full version of DFP) and on longer sequences (at least 8K tokens).
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So no full benchmark will be conducted.
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From our first observations, frenchT0 gave better or equivalent results to [mt0-base](https://huggingface.co/bigscience/mt0-base) for fewer parameters (580M vs. 300M parameters).
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To test this model, we invite you to look at the sample prompts provided in the [DFP](https://huggingface.co/datasets/CATIE-AQ/DFP)'s card.
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Text generation tasks should not give good results, but classification tasks (classification, QA, NER, POS, etc.) give interesting results.
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