Text Generation
Transformers
English
qtensorformer
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
feat(research): Turn Q-TensorFormer into a verified research system with LFS figure assets
0e4850b Download outputs/figures/figure4_nested_tt_suboptimality.png from Premchan369/Q-TensorFormer: direct link, hf CLI and curl.
- Browser
- Download file 222 kB
-
https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/outputs/figures/figure4_nested_tt_suboptimality.png
- Command line
-
hf download hf://Premchan369/Q-TensorFormer/outputs/figures/figure4_nested_tt_suboptimality.png
-
curl -L -o figure4_nested_tt_suboptimality.png https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/outputs/figures/figure4_nested_tt_suboptimality.png
222 kB

- Xet hash:
- 6d8b9e2e99f1372970aceb57a031ac404781634ad3b6e01457dbf073356cba5e
- Size of remote file:
- 222 kB
- SHA256:
- f104221a7fce03c64c7e44e182d12f069224836a673c0b55a4053780560be64b
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