Instructions to use Premchan369/Q-TensorFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Premchan369/Q-TensorFormer", trust_remote_code=True, 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
```

Q-TensorFormer / CONTRIBUTING.md

Premchandyadav369

docs(seo): enrich search discovery tags, interactive inference widget, topic index, MODEL_CARD, CITATION, and CI workflows

a3e28f5 about 1 month ago

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Contributing to Q-TensorFormer

Thank you for your interest in contributing to Q-TensorFormer! We welcome contributions from researchers, systems engineers, and open-source practitioners in tensor networks, efficient inference, and hardware-aware computing.

🛠️ Development Environment Setup

Clone the repository:

git clone https://huggingface.co/Premchan369/Q-TensorFormer
cd Q-TensorFormer

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

Install dependencies:
```
pip install -r requirements.txt
```

🧪 Running the Test Suite

Before submitting any Pull Request, ensure that all unit and integration tests pass:

pytest -v

The test suite covers:

tests/test_model.py: Forward pass shapes, causal mask handling, GQA attention, attention sinks, and PID stability.
tests/test_frontier_v10.py: Triton/JIT kernels, GGUF serialization, ONNX export, and Multimodal Vision-Language reasoning.

📐 Guidelines for Contributions

Scientific Integrity:
- Never fabricate or invent benchmark numbers.
- Every result must be explicitly tagged as [MEASURED], [ESTIMATED], [SIMULATED], or [PROJECTED].
Zero-Overhead Contract:
- Dynamic rank switching must remain $\mathcal{O}(1)$ without runtime SVD or memory allocations.
Backward Compatibility:
- Preserve Hugging Face Hub trust_remote_code=True compatibility with AutoConfig and AutoModelForCausalLM.

📬 Submitting Changes

Create a feature branch: git checkout -b feat/my-improvement.
Commit your changes with clear semantic commit messages (feat: ..., fix: ..., docs: ...).
Push to your branch and open a Pull Request.