Instructions to use Cedille/de-anna with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cedille/de-anna with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Cedille/de-anna")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Cedille/de-anna") model = AutoModelForCausalLM.from_pretrained("Cedille/de-anna") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Cedille/de-anna with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Cedille/de-anna" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cedille/de-anna", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Cedille/de-anna
- SGLang
How to use Cedille/de-anna 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 "Cedille/de-anna" \ --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/de-anna", "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 "Cedille/de-anna" \ --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/de-anna", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Cedille/de-anna with Docker Model Runner:
docker model run hf.co/Cedille/de-anna
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license: mit
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---
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language: de
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license: mit
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tags:
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- pytorch
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- causal-lm
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datasets:
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- c4
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# Cedille AI
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Cedille is a project to bring large language models to non-English languages.
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## fr-boris
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Anna is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) codebase.
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Anna was trained on German text with a similar methodology to [Boris](https://huggingface.co/Cedille/fr-boris), our French model. We started training from GPT-J, which has been trained on [The Pile](https://pile.eleuther.ai/). As a consequence the model still has good performance in English language. Anna makes use of the unmodified GPT-2 tokenizer.
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# How to run
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TO DO
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## Contact us
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For any custom development please contact us at hello@cedille.ai.
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## Links
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* [Official website](https://en.cedille.ai/)
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* [Blog](https://en.cedille.ai/blog)
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* [GitHub](https://github.com/coteries/cedille-ai)
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* [Twitter](https://twitter.com/CedilleAI)
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