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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license: cc-by-sa-4.0
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<div style="text-align: right;font-size:.7em;margin-left:50%"><em>Per un attimo Brahe cercò le parole, le immagini, le analogie; pensò perfino i gesti della mano e delle dita, come un attore che si prepari a rendere fisico un sentimento. Ma appena cominciò a dire "come", a dare solidità a ciò che non aveva, a rendere visibile ciò che non lo era, a collocare, nello spazio ciò che era pura probabilità, e a cercare una qualsiasi cosa tra le forme del mondo cui paragonarlo, Epstein lo interruppe.</em></div>
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*Brahe* is an analytical LLM for English literature fine-tuned from llama-13B. Given any text, Brahe will generate a list of potentially twenty annotations. Brahe is intended to be used by computational humanities project, similarly to 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 Daniele 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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## Annotations
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* Time setting: historical period where the action seems to occur such as the 1960s, the Renaissance, the Victorian period…
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* Diegetic time: 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 time: 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.
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The annotations are not generated systematically but only whenever the model is confident enough.
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license: cc-by-sa-4.0
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---
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<div style="text-align: right;font-size:.7em;margin-left:50%"><em>Per un attimo Brahe cercò le parole, le immagini, le analogie; pensò perfino i gesti della mano e delle dita, come un attore che si prepari a rendere fisico un sentimento. Ma appena cominciò a dire "come", a dare solidità a ciò che non aveva, a rendere visibile ciò che non lo era, a collocare, nello spazio ciò che era pura probabilità, e a cercare una qualsiasi cosa tra le forme del mondo cui paragonarlo, Epstein lo interruppe.</em><br>Daniele del Giudice, <em>Atlante occidentale</em></div>
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*Brahe* is an analytical LLM for English literature fine-tuned from llama-13B. Given any text, Brahe will generate a list of potentially twenty annotations. Brahe is intended to be used by computational humanities project, similarly to 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](https://huggingface.co/Pclanglais/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 Daniele 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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## Annotations
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* Time setting: historical period where the action seems to occur such as the 1960s, the Renaissance, the Victorian period…
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* Diegetic time: 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 time: a precise date where the action occurs, such as January 15, 1845, 23rd century…
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The annotations are not generated systematically but only whenever the model is confident enough.
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