Instructions to use Pclanglais/Arsene with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pclanglais/Arsene with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Pclanglais/Arsene")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Pclanglais/Arsene") model = AutoModelForCausalLM.from_pretrained("Pclanglais/Arsene") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Pclanglais/Arsene with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Pclanglais/Arsene" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Pclanglais/Arsene", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Pclanglais/Arsene
- SGLang
How to use Pclanglais/Arsene 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/Arsene" \ --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/Arsene", "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/Arsene" \ --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/Arsene", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Pclanglais/Arsene with Docker Model Runner:
docker model run hf.co/Pclanglais/Arsene
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Check out the documentation for more information.
A fine-tuned version of Llama 2 (base) trained on 4,000 excerpts of French literature. It is conceived for the generation of literary text.
You can for instance supply the following informations.
Résumé : Alexander Doria présente le fonctionnement d'un robot littéraire intelligent à une assemblée savante.
Intertextualité : Article scientifique
Mouvement littéraire : Surréalisme
Énonciation : Dialogue
Ton : Sérieux
Arc narratif : Approbation de plus en plus grande
Personnages actifs : Alexander Doria, Robot Emilio, Flef
Temps précis de l'action : 1 septembre 2023
Temps diégétique : une heure
###\nTexte :
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