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BaseIntelligence
/
top-prism-architecture

Text Generation
Transformers
prism_custom
feature-extraction
prism
custom-architecture
trust-remote-code
neural-architecture-search
custom_code
Model card Files Files and versions
xet
Community

Instructions to use BaseIntelligence/top-prism-architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use BaseIntelligence/top-prism-architecture with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="BaseIntelligence/top-prism-architecture", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("BaseIntelligence/top-prism-architecture", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use BaseIntelligence/top-prism-architecture with vLLM:

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

    How to use BaseIntelligence/top-prism-architecture 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 "BaseIntelligence/top-prism-architecture" \
        --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": "BaseIntelligence/top-prism-architecture",
    		"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 "BaseIntelligence/top-prism-architecture" \
            --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": "BaseIntelligence/top-prism-architecture",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use BaseIntelligence/top-prism-architecture with Docker Model Runner:

    docker model run hf.co/BaseIntelligence/top-prism-architecture
top-prism-architecture
429 MB
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  • 1 contributor
History: 15 commits
echoctx's picture
echoctx
top-model: arch_f17d92b32a8c79f7 bpb=3.7181
ad87e7a verified 19 days ago
  • sources
    top-model: arch_f17d92b32a8c79f7 bpb=3.7181 19 days ago
  • .gitattributes
    1.52 kB
    initial commit about 1 month ago
  • METRICS.json
    24.2 kB
    top-model: arch_f17d92b32a8c79f7 bpb=3.7181 19 days ago
  • README.md
    2.97 kB
    top-model: arch_f17d92b32a8c79f7 bpb=3.7181 19 days ago
  • architecture.py
    25.3 kB
    top-model: arch_f17d92b32a8c79f7 bpb=3.7181 19 days ago
  • checkpoint.pt
    428 MB
    xet
    top-model: arch_f17d92b32a8c79f7 bpb=3.7181 19 days ago
  • config.json
    509 Bytes
    top-model: arch_f17d92b32a8c79f7 bpb=3.7181 19 days ago
  • configuration_prism.py
    383 Bytes
    top-model: arch_78f5bc956d8b31de bpb=1.1373 22 days ago
  • modeling_prism.py
    3.41 kB
    top-model: arch_78f5bc956d8b31de bpb=1.1373 22 days ago
  • training.py
    25.2 kB
    top-model: arch_f17d92b32a8c79f7 bpb=3.7181 19 days ago