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)# pip install -U transformers accelerate # Load model directly from transformers import NeuralQuantumNQLM model = NeuralQuantumNQLM.from_pretrained("NeuralQuantum/nqlm", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Download configuration_nqlm.py from NeuralQuantum/nqlm: direct link, hf CLI and curl.
- Browser
- Download file 1.93 kB
-
https://huggingface.co/NeuralQuantum/nqlm/resolve/main/configuration_nqlm.py
- Command line
-
hf download hf://NeuralQuantum/nqlm/configuration_nqlm.py
-
curl -L -o configuration_nqlm.py https://huggingface.co/NeuralQuantum/nqlm/resolve/main/configuration_nqlm.py
1.93 kB
| """ | |
| NeuralQuantum NQLM Configuration for Hugging Face Transformers | |
| """ | |
| from transformers import PretrainedConfig | |
| class NeuralQuantumNQLMConfig(PretrainedConfig): | |
| """Configuration class for NeuralQuantum NQLM model""" | |
| model_type = "neuralquantum_nqlm" | |
| def __init__( | |
| self, | |
| 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", | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_attention_heads = num_attention_heads | |
| self.num_hidden_layers = num_hidden_layers | |
| self.intermediate_size = intermediate_size | |
| self.hidden_act = hidden_act | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.use_cache = use_cache | |
| # Quantum-specific parameters | |
| self.quantum_enhancement = quantum_enhancement | |
| self.quantum_layers = quantum_layers | |
| self.quantum_circuit_depth = quantum_circuit_depth | |
| self.quantum_optimization = quantum_optimization | |
| self.hybrid_mode = hybrid_mode | |
| self.torch_dtype = torch_dtype |