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SupraLabs
/
Supra-1.5-50M-Base-exp

Text Generation
Transformers
Safetensors
English
llama
supra
supra-1.5
50m
base
continued-pretraining
long-context
5k-context
Supra
Supra-50M
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use SupraLabs/Supra-1.5-50M-Base-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use SupraLabs/Supra-1.5-50M-Base-exp with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="SupraLabs/Supra-1.5-50M-Base-exp")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-1.5-50M-Base-exp")
    model = AutoModelForMultimodalLM.from_pretrained("SupraLabs/Supra-1.5-50M-Base-exp")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use SupraLabs/Supra-1.5-50M-Base-exp with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "SupraLabs/Supra-1.5-50M-Base-exp"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "SupraLabs/Supra-1.5-50M-Base-exp",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/SupraLabs/Supra-1.5-50M-Base-exp
  • SGLang

    How to use SupraLabs/Supra-1.5-50M-Base-exp 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 "SupraLabs/Supra-1.5-50M-Base-exp" \
        --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": "SupraLabs/Supra-1.5-50M-Base-exp",
    		"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 "SupraLabs/Supra-1.5-50M-Base-exp" \
            --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": "SupraLabs/Supra-1.5-50M-Base-exp",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use SupraLabs/Supra-1.5-50M-Base-exp with Docker Model Runner:

    docker model run hf.co/SupraLabs/Supra-1.5-50M-Base-exp
Supra-1.5-50M-Base-exp
209 MB
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  • 3 contributors
History: 5 commits
LH-Tech-AI's picture
LH-Tech-AI
Update README.md
ff9f92a verified 8 days ago
  • .gitattributes
    1.52 kB
    initial commit 10 days ago
  • README.md
    1.79 kB
    Update README.md 8 days ago
  • config.json
    864 Bytes
    all the model files uploaded 10 days ago
  • generation_config.json
    139 Bytes
    all the model files uploaded 10 days ago
  • model.safetensors
    207 MB
    xet
    all the model files uploaded 10 days ago
  • special_tokens_map.json
    102 Bytes
    all the model files uploaded 10 days ago
  • tokenizer.json
    2.27 MB
    all the model files uploaded 10 days ago
  • tokenizer_config.json
    984 Bytes
    all the model files uploaded 10 days ago