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NeuralQuantum
/
nqlm

Text Generation
Transformers
PyTorch
neuralquantum_nqlm
quantum
nlp
language-model
neural-quantum
hybrid-computing
custom_code
Model card Files Files and versions
xet
Community

Instructions to use NeuralQuantum/nqlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use NeuralQuantum/nqlm with Transformers:

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

    How to use NeuralQuantum/nqlm with vLLM:

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

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

    How to use NeuralQuantum/nqlm with Docker Model Runner:

    docker model run hf.co/NeuralQuantum/nqlm
nqlm
849 MB
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  • 1 contributor
History: 10 commits
tommytracx's picture
tommytracx
Add tokenization_nqlm.py
4da61ae verified 8 months ago
  • .gitattributes
    1.52 kB
    initial commit 8 months ago
  • README.md
    4.97 kB
    Add README.md 8 months ago
  • __init__.py
    346 Bytes
    Add __init__.py 8 months ago
  • config.json
    763 Bytes
    Add config.json 8 months ago
  • configuration_nqlm.py
    1.93 kB
    Add configuration_nqlm.py 8 months ago
  • modeling_nqlm.py
    8.58 kB
    Add modeling_nqlm.py 8 months ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict"

    What is a pickle import?

    849 MB
    xet
    Add pytorch_model.bin 8 months ago
  • tokenization_nqlm.py
    3.84 kB
    Add tokenization_nqlm.py 8 months ago
  • tokenizer.json
    2.17 kB
    Add tokenizer.json 8 months ago
  • tokenizer_config.json
    651 Bytes
    Add tokenizer_config.json 8 months ago