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

1. **Clone the repository**:
   ```bash
   git clone https://huggingface.co/Premchan369/Q-TensorFormer
   cd Q-TensorFormer
   ```

2. **Create a virtual environment**:
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   ```

3. **Install dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

---

## 🧪 Running the Test Suite

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

```bash
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

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

---

## 📬 Submitting Changes

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

	# 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

	1. Clone the repository:
	```bash
	git clone https://huggingface.co/Premchan369/Q-TensorFormer
	cd Q-TensorFormer
	```

	2. Create a virtual environment:
	```bash
	python -m venv venv
	source venv/bin/activate # On Windows: .\venv\Scripts\activate
	```

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

	---

	## 🧪 Running the Test Suite

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

	```bash
	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

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

	---

	## 📬 Submitting Changes

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