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Conlanger-LLM-CLEM
/
Sorie

Text Generation
Transformers
Safetensors
French
sora_slm
LLM
sora
Sorie
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Conlanger-LLM-CLEM/Sorie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Conlanger-LLM-CLEM/Sorie with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Conlanger-LLM-CLEM/Sorie", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Conlanger-LLM-CLEM/Sorie", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Conlanger-LLM-CLEM/Sorie with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Conlanger-LLM-CLEM/Sorie"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Conlanger-LLM-CLEM/Sorie",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Conlanger-LLM-CLEM/Sorie
  • SGLang

    How to use Conlanger-LLM-CLEM/Sorie 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 "Conlanger-LLM-CLEM/Sorie" \
        --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": "Conlanger-LLM-CLEM/Sorie",
    		"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 "Conlanger-LLM-CLEM/Sorie" \
            --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": "Conlanger-LLM-CLEM/Sorie",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Conlanger-LLM-CLEM/Sorie with Docker Model Runner:

    docker model run hf.co/Conlanger-LLM-CLEM/Sorie
Sorie
1.8 GB
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  • 1 contributor
History: 13 commits
Clem27-Assistants's picture
Clem27-Assistants
Update README.md
b4a133c verified 4 months ago
  • .gitattributes
    1.52 kB
    initial commit 4 months ago
  • README.md
    1.37 kB
    Update README.md 4 months ago
  • config.json
    399 Bytes
    Update config.json 4 months ago
  • configuration_sora.py
    552 Bytes
    Update configuration_sora.py 4 months ago
  • model.safetensors
    1.8 GB
    xet
    Training in progress, step 100 4 months ago
  • modeling_sora.py
    2.09 kB
    Upload 2 files 4 months ago
  • tokenizer.json
    663 kB
    Entraînement de SoraForSLM terminé 4 months ago
  • tokenizer_config.json
    432 Bytes
    Entraînement de SoraForSLM terminé 4 months ago
  • training_args.bin
    5.2 kB
    xet
    Training in progress, step 100 4 months ago