Instructions to use Pclanglais/Brahe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pclanglais/Brahe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pclanglais/Brahe")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Pclanglais/Brahe") model = AutoModelForCausalLM.from_pretrained("Pclanglais/Brahe") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Pclanglais/Brahe with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pclanglais/Brahe" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pclanglais/Brahe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Pclanglais/Brahe
- SGLang
How to use Pclanglais/Brahe 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 "Pclanglais/Brahe" \ --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": "Pclanglais/Brahe", "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 "Pclanglais/Brahe" \ --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": "Pclanglais/Brahe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Pclanglais/Brahe with Docker Model Runner:
docker model run hf.co/Pclanglais/Brahe
Commit ·
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README.md
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license: cc-by-sa-4.0
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license: cc-by-sa-4.0
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*Brahe* is an analytical LLM for English literature. Given any text, Brahe will generate a list of potentially twenty annotations. Brahe is intended to be used by computational humanities project, similarly to past projects like BookNLP.
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*Brahe* has been trained on 4,000 excerpts of English or English translated literature in the public domain and on a set of synthetic and manual annotations.
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*Brahe* is the reversed companion of *Epstein*, a generative AI model to create new literary texts by submitting annotated prompts. Both models are named after the protagonists of the philosophical novel of Daniel del Giudice, *Atlante occidentale*. Brahe is a scientist working at the CERN on quantum physics, Epstein is a novelist and they both confront their different views of reality.
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