Text Generation
Transformers
English
tensor-networks
model-compression
adaptive-computation
kv-cache-compression
hardware-aware
energy-aware
quantum-machine-learning
green-ai
Instructions to use Premchan369/Q-TensorFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Premchan369/Q-TensorFormer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Premchan369/Q-TensorFormer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Premchan369/Q-TensorFormer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Premchan369/Q-TensorFormer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Premchan369/Q-TensorFormer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Premchan369/Q-TensorFormer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Premchan369/Q-TensorFormer
- SGLang
How to use Premchan369/Q-TensorFormer 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 "Premchan369/Q-TensorFormer" \ --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": "Premchan369/Q-TensorFormer", "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 "Premchan369/Q-TensorFormer" \ --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": "Premchan369/Q-TensorFormer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Premchan369/Q-TensorFormer with Docker Model Runner:
docker model run hf.co/Premchan369/Q-TensorFormer
Premchandyadav369
Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture
eaeea8f | from setuptools import setup, find_packages | |
| with open("README.md", "r", encoding="utf-8") as fh: | |
| long_description = fh.read() | |
| setup( | |
| name="q-tensorformer", | |
| version="4.0.0", | |
| author="Premchan369", | |
| description="Q-TensorFormer: Information-Value Driven Adaptive Resource Allocation in Hybrid Transformers", | |
| long_description=long_description, | |
| long_description_content_type="text/markdown", | |
| url="https://huggingface.co/Premchan369/Q-TensorFormer", | |
| packages=find_packages(include=["src", "src.*"]), | |
| classifiers=[ | |
| "Development Status :: 4 - Beta", | |
| "Intended Audience :: Science/Research", | |
| "License :: OSI Approved :: Apache Software License", | |
| "Programming Language :: Python :: 3", | |
| "Topic :: Scientific/Engineering :: Artificial Intelligence", | |
| ], | |
| python_requires=">=3.8", | |
| install_requires=[ | |
| "torch>=2.0.0", | |
| "transformers>=4.30.0", | |
| "numpy>=1.24.0", | |
| "pyyaml>=6.0", | |
| ], | |
| extras_require={ | |
| "dev": [ | |
| "pytest>=7.0", | |
| "pytest-cov", | |
| "black", | |
| "isort", | |
| "flake8", | |
| ], | |
| "full": [ | |
| "accelerate>=0.27.0", | |
| "peft>=0.8.0", | |
| "bitsandbytes>=0.41.0", | |
| "wandb>=0.15.0", | |
| ], | |
| }, | |
| ) | |