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
| cff-version: 1.2.0 | |
| title: "Q-TensorFormer: Quantum-Enhanced Tensor Network LLM Compression Engine" | |
| message: "If you use this software in your research, please cite it using the metadata below." | |
| authors: | |
| - given-names: Premchan369 | |
| affiliation: "" | |
| repository-code: "https://huggingface.co/Premchan369/q-tensorformer" | |
| url: "https://huggingface.co/Premchan369/q-tensorformer" | |
| abstract: >- | |
| Q-TensorFormer is a hybrid transformer architecture that compresses feed-forward | |
| layers using Tensor-Train (TT) decomposition and enhances token representations | |
| via real quantum circuits, with adaptive TT-rank scheduling guided by attention | |
| entropy. It achieves 50-70% parameter reduction at equivalent perplexity. | |
| keywords: | |
| - tensor networks | |
| - quantum machine learning | |
| - model compression | |
| - transformer | |
| - language modeling | |
| - efficient deep learning | |
| license: Apache-2.0 | |
| version: 3.0.0 | |
| date-released: 2026-05-06 | |