Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing

  • Log In
  • Sign Up

PhysiQuanty
/
Binary-Addition-LLM-POC

Text Generation
Transformers
Safetensors
English
French
binaryllm
binary-carry-probagation
binary-level
bit-level
causal-lm
tokenizer-free
base2
binary
calculator
addition
TinyTransformerLM
custom_code
Model card Files Files and versions
xet
Community

Instructions to use PhysiQuanty/Binary-Addition-LLM-POC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use PhysiQuanty/Binary-Addition-LLM-POC with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="PhysiQuanty/Binary-Addition-LLM-POC", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("PhysiQuanty/Binary-Addition-LLM-POC", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use PhysiQuanty/Binary-Addition-LLM-POC with vLLM:

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

    How to use PhysiQuanty/Binary-Addition-LLM-POC 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 "PhysiQuanty/Binary-Addition-LLM-POC" \
        --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": "PhysiQuanty/Binary-Addition-LLM-POC",
    		"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 "PhysiQuanty/Binary-Addition-LLM-POC" \
            --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": "PhysiQuanty/Binary-Addition-LLM-POC",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use PhysiQuanty/Binary-Addition-LLM-POC with Docker Model Runner:

    docker model run hf.co/PhysiQuanty/Binary-Addition-LLM-POC
Binary-Addition-LLM-POC
42.6 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 7 commits
PhysiQuanty's picture
PhysiQuanty
Update README.md
1f5048c verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    2.73 kB
    Update README.md 3 months ago
  • __init__.py
    106 Bytes
    export inference-ready 3 months ago
  • config.json
    464 Bytes
    export inference-ready 3 months ago
  • configuration_binaryllm.py
    896 Bytes
    export inference-ready 3 months ago
  • inference.py
    13.6 kB
    export inference-ready 3 months ago
  • model.safetensors
    42.6 MB
    xet
    export inference-ready 3 months ago
  • modeling_binaryllm.py
    5.31 kB
    export inference-ready 3 months ago