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
Transform Q-TensorFormer into an Information-Value Adaptive Resource Allocation Architecture
eaeea8f Download tests/test_nested_tt.py from Premchan369/Q-TensorFormer: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/tests/test_nested_tt.py
- Command line
-
hf download hf://Premchan369/Q-TensorFormer/tests/test_nested_tt.py
-
curl -L -o test_nested_tt.py https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/tests/test_nested_tt.py
1.27 kB
| """ | |
| Tests for Nested-Core Tensor-Train Layer. | |
| """ | |
| import sys | |
| import os | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| import torch | |
| import pytest | |
| from src.tensor_layers import TTLinear, TTFeedForward | |
| def test_nested_tt_slicing_shapes(): | |
| in_dim, out_dim = 64, 128 | |
| layer = TTLinear(in_dim, out_dim, max_rank=8) | |
| x = torch.randn(2, 4, in_dim) | |
| # Test all candidate ranks | |
| for r in [1, 2, 4, 8]: | |
| layer.set_rank(r) | |
| assert layer.rank == r | |
| out = layer(x) | |
| assert out.shape == (2, 4, out_dim), f"Expected (2, 4, {out_dim}), got {out.shape}" | |
| assert not torch.isnan(out).any(), f"NaN in output at rank {r}" | |
| traffic = layer.get_memory_traffic() | |
| assert traffic["total_bytes"] > 0 | |
| print("✓ test_nested_tt_slicing_shapes passed") | |
| def test_nested_tt_ffn(): | |
| ffn = TTFeedForward(hidden_dim=64, ff_multiplier=4, rank=8) | |
| x = torch.randn(2, 64) | |
| for r in [1, 2, 4, 8]: | |
| ffn.set_rank(r) | |
| out = ffn(x) | |
| assert out.shape == (2, 64) | |
| assert ffn.active_params > 0 | |
| print("✓ test_nested_tt_ffn passed") | |
| if __name__ == "__main__": | |
| test_nested_tt_slicing_shapes() | |
| test_nested_tt_ffn() | |
| print("All Nested TT tests passed!") | |