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
Commit ·
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Parent(s): 4e34fb1
Update README.md
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README.md
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@@ -94,16 +94,16 @@ With an incipit of Nueit de junh by Joan Francés Blanc, in Gascon dialect of Oc
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Output of Brahe:
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> Summary: The text is a series of thoughts and reflections on death, friendship, and the passage of time.
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> Trope: The passage of time, the fleeting nature of life
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> Narrative arc: Reflective, introspective
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> Enunciation: First-person narrative
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> Tone: Reflective, melancholic
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> Genre: Poetry
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> Intertextuality: Poetry
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> Speech standard: Poetic, elevated language
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> Literary form: Stream of consciousness
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> Literary movement: Existentialism
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> Time setting: Contemporary
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> Fuzzy place: Unnamed village, Paris, Pointe-à-Pitre
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Output of Brahe:
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> Summary: The text is a series of thoughts and reflections on death, friendship, and the passage of time.<br>
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> Trope: The passage of time, the fleeting nature of life<br>
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> Narrative arc: Reflective, introspective<br>
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> Enunciation: First-person narrative<br>
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> Tone: Reflective, melancholic<br>
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> Genre: Poetry<br>
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| 103 |
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> Intertextuality: Poetry<br>
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| 104 |
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> Speech standard: Poetic, elevated language<br>
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| 105 |
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> Literary form: Stream of consciousness<br>
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| 106 |
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> Literary movement: Existentialism<br>
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| 107 |
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> Time setting: Contemporary<br>
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| 108 |
> Fuzzy place: Unnamed village, Paris, Pointe-à-Pitre
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