Text Generation
Transformers
PyTorch
neuralquantum_nqlm
quantum
nlp
language-model
neural-quantum
hybrid-computing
custom_code
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
| { | |
| "architectures": [ | |
| "NeuralQuantumNQLM" | |
| ], | |
| "auto_map": { | |
| "AutoModel": "modeling_nqlm.NeuralQuantumNQLMForCausalLM", | |
| "AutoConfig": "configuration_nqlm.NeuralQuantumNQLMConfig" | |
| }, | |
| "model_type": "neuralquantum_nqlm", | |
| "vocab_size": 50257, | |
| "hidden_size": 768, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "intermediate_size": 3072, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "attention_probs_dropout_prob": 0.1, | |
| "max_position_embeddings": 512, | |
| "type_vocab_size": 2, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "use_cache": true, | |
| "quantum_enhancement": true, | |
| "quantum_layers": 4, | |
| "quantum_circuit_depth": 8, | |
| "quantum_optimization": "vqe", | |
| "hybrid_mode": true, | |
| "torch_dtype": "float16" | |
| } |