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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Pclanglais/Brahe") model = AutoModelForMultimodalLM.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
license: cc-by-sa-4.0
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.
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.
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.