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
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README.md
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## Annotations
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In its current version, *Brahe*
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The annotations are not generated systematically but only whenever the model is confident enough.
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## Annotations
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In its current version, *Brahe* may generate the following annotations.
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* Summary: short summary
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* Tone: general tonality of the text (humoristic, tragic, scholarly…)
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* Speech standard: the specific social/literary level of the text (poetic, dialectical, vulgar…)
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* Intertextuality: non-literary writing forms that may be similar to this text (red tape, scientific article, case law…)
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* Genre: a specific literary genre that would be used in bookshops such as detective fiction, science-fiction, romance, historical novel, young adult…
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* Literary movement: aesthetic movement the text seems to embody (does not work so well)
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* Literary form: whether it's the description of a place, a conversation, a stream of consciousness
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* A trope or literary cliché:
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* Enonciation: who is speaking in the text (first-person narrative, dialog, third-person narrative, omniscient narrator)
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* Narrative arch: how is the action unfolding (suspense, dramatic tension, comic relief,
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* Active characters: the list of characters that have an active involvment in the story.
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* Quoted characters: the list of characters only quoted but do not act.
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* Quoted works: a text mentioned or quoted in the text.
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* Fuzzy places of the action: unnamed place where the story happens such as a field, an appartment, a church (does not work so well…)
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* Fuzzy time of the action: nonspecific moment where the action occur moment such as monday, yesterday, a week after.
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* Time setting of the action: historical period where the action seems to occur such as the 1960s, the Renaissance, the Victorian period…
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* Time spent during the action: very approximative number of minutes/hours/days that have unfolded between the beginning and the end of the text (5 minutes, 35 minutes, 2 hours, 3 days).
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* Absolute places of the action: a precise place with a proper name such as Paris, Sesame Street, Lisbonne Airport.
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* Absolute times of the action: a precise date where the action occurs, such as January 15, 1845, 23rd century…
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* Meta-text: to specify if the text is not part of the actual novel such as table of content, legal notice, book cover. None otherwise.
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The annotations are not generated systematically but only whenever the model is confident enough.
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