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_resource_allocator.py from Premchan369/Q-TensorFormer: direct link, hf CLI and curl.
- Browser
- Download file 2.28 kB
-
https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/tests/test_resource_allocator.py
- Command line
-
hf download hf://Premchan369/Q-TensorFormer/tests/test_resource_allocator.py
-
curl -L -o test_resource_allocator.py https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/tests/test_resource_allocator.py
2.28 kB
| """ | |
| Tests for Information-Value Resource Allocator. | |
| """ | |
| import sys | |
| import os | |
| sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| import torch | |
| import pytest | |
| from src.resource_allocator import InformationValueAllocator, AllocationBudget | |
| def test_resource_allocator_decisions(): | |
| allocator = InformationValueAllocator(info_dim=8, hidden_dim=16) | |
| B, T = 2, 8 | |
| z_t = torch.rand(B, T, 8) | |
| decisions, diagnostics = allocator(z_t, preset="balanced") | |
| assert decisions["rank"] in [1, 2, 4, 8] | |
| assert decisions["attn_mode_idx"].shape == (B, T) | |
| assert decisions["depth_mode"] in ["skip", "partial", "full"] | |
| assert decisions["kv_precision"] in ["fp16", "int8", "int4"] | |
| assert "chosen_rank" in diagnostics | |
| assert "routing_churn_rate" in diagnostics | |
| print("✓ test_resource_allocator_decisions passed") | |
| def test_resource_allocator_hysteresis(): | |
| allocator = InformationValueAllocator(info_dim=8, hidden_dim=16, hysteresis_tau=0.5) | |
| B, T = 1, 4 | |
| z_t_1 = torch.full((B, T, 8), 0.2) | |
| decisions_1, _ = allocator(z_t_1) | |
| rank_1 = decisions_1["rank"] | |
| # Small perturbation that should NOT break hysteresis threshold | |
| z_t_2 = torch.full((B, T, 8), 0.22) | |
| decisions_2, _ = allocator(z_t_2) | |
| rank_2 = decisions_2["rank"] | |
| assert rank_1 == rank_2, f"Hysteresis should preserve rank on small delta: {rank_1} vs {rank_2}" | |
| print("✓ test_resource_allocator_hysteresis passed") | |
| def test_resource_allocator_presets(): | |
| allocator = InformationValueAllocator(info_dim=8) | |
| z_t = torch.rand(2, 4, 8) | |
| # Edge preset should force classical | |
| decisions_edge, diag_edge = allocator(z_t, preset="edge") | |
| assert not decisions_edge["is_quantum_token"].any(), "Edge preset should disable quantum tokens" | |
| # Classical-only preset should force classical | |
| decisions_class, _ = allocator(z_t, preset="classical_only") | |
| assert not decisions_class["is_quantum_token"].any(), "Classical-only preset should disable quantum tokens" | |
| print("✓ test_resource_allocator_presets passed") | |
| if __name__ == "__main__": | |
| test_resource_allocator_decisions() | |
| test_resource_allocator_hysteresis() | |
| test_resource_allocator_presets() | |
| print("All Resource Allocator tests passed!") | |