Text Generation
Transformers
Safetensors
PyTorch
ONNX
English
qtensorformer
q-tensorformer
high-throughput-inference
tensor-train
tensor-networks
tensorrt-llm
vllm
continuous-batching
paged-attention
speculative-decoding
zero-svd
adaptive-computation
dynamic-neural-networks
kv-cache
kv-cache-quantization
attention-sinks
lyapunov-stability
control-theory
contextual-bandits
edge-ai
edge-inference
on-device-ai
model-compression
hopper-h100
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grouped-query-attention
rotary-position-embeddings
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academic-paper
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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", 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
Premchandyadav369
feat(seo): maximize discoverability, Google Scholar metadata, interactive HF widget, LLM crawler schemas, zero em dashes, and one-click quickstart downloads
5175adb Download site/contributing.html from Premchan369/Q-TensorFormer: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/site/contributing.html
- Command line
-
hf download hf://Premchan369/Q-TensorFormer/site/contributing.html
-
curl -L -o contributing.html https://huggingface.co/Premchan369/Q-TensorFormer/resolve/main/site/contributing.html
5.5 kB
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Contributing & Development Guidelines - Q-TensorFormer</title> | |
| <meta name="description" content="Guidelines for contributing to the Q-TensorFormer open-source research project, including test execution and scientific integrity rules."> | |
| <link rel="canonical" href="https://premchan369.github.io/Q-TensorFormer/contributing.html"> | |
| <meta property="og:type" content="article"> | |
| <meta property="og:site_name" content="Q-TensorFormer Research Project"> | |
| <meta property="og:title" content="Contributing & Development Guidelines - Q-TensorFormer"> | |
| <meta property="og:description" content="Development workflow, testing standards, and pull request guidelines."> | |
| <meta property="og:url" content="https://premchan369.github.io/Q-TensorFormer/contributing.html"> | |
| <script type="application/ld+json"> | |
| { | |
| "@context": "https://schema.org", | |
| "@type": "TechArticle", | |
| "headline": "Q-TensorFormer Open-Source Contribution Guidelines", | |
| "description": "Standards for contributing code, running test gates, and maintaining scientific integrity.", | |
| "url": "https://premchan369.github.io/Q-TensorFormer/contributing.html", | |
| "author": { "@type": "Person", "name": "Premchand Yadav" }, | |
| "datePublished": "2026-10-13" | |
| } | |
| </script> | |
| <link rel="stylesheet" href="assets/style.css"> | |
| </head> | |
| <body> | |
| <header> | |
| <div class="header-container"> | |
| <div class="logo"> | |
| <a href="index.html"><span>Q-TensorFormer</span> Research</a> | |
| </div> | |
| <nav> | |
| <a href="index.html">Overview</a> | |
| <a href="architecture.html">Architecture</a> | |
| <a href="methodology.html">Methodology</a> | |
| <a href="benchmarks.html">Benchmarks</a> | |
| <a href="edge-inference.html">Edge Inference</a> | |
| <a href="training.html">Training</a> | |
| <a href="documentation.html">Docs & API</a> | |
| <a href="research.html">Research</a> | |
| <a href="limitations.html">Limitations</a> | |
| <a href="contributing.html" class="active">Contributing</a> | |
| </nav> | |
| </div> | |
| </header> | |
| <main class="container"> | |
| <h1>Contributing & Development Guidelines</h1> | |
| <p class="lead"> | |
| We welcome contributions from researchers, ML systems engineers, and open-source developers. To maintain reproducibility, all contributions must satisfy strict verification standards. | |
| </p> | |
| <section> | |
| <h2>1. Non-Negotiable Scientific Principles</h2> | |
| <div class="callout warning"> | |
| <ul> | |
| <li><strong>Zero Fabricated Metrics:</strong> Never submit synthetic benchmarks, simulated energy labeled as measured, or unverified hardware speedups.</li> | |
| <li><strong>Strict Checkpoint Loading:</strong> Never introduce <code>strict=False</code> bypasses or silent fallbacks to random weights.</li> | |
| <li><strong>Canonical Project Name:</strong> Maintain the canonical name <strong>Q-TensorFormer</strong>. Do not introduce versioned forks (e.g. v2, v3).</li> | |
| </ul> | |
| </div> | |
| </section> | |
| <section> | |
| <h2>2. Development Environment Setup</h2> | |
| <p>Fork the repository, clone locally, and create a clean Python virtual environment:</p> | |
| <pre><code>git clone https://huggingface.co/Premchan369/Q-TensorFormer | |
| cd Q-TensorFormer | |
| python -m venv venv | |
| source venv/bin/activate # On Windows: venv\Scripts\activate | |
| pip install -r requirements.txt</code></pre> | |
| </section> | |
| <section> | |
| <h2>3. Required Pre-Commit Verification Gate</h2> | |
| <p>Before submitting any Pull Request, you must verify that all automated checks pass locally:</p> | |
| <pre><code># 1. Run full unit test suite (must report 0 failures) | |
| pytest -v | |
| # 2. Run deterministic smoke test | |
| python scripts/smoke_test.py | |
| # 3. Run scientific integrity auditor | |
| python scripts/scientific_integrity_checker.py</code></pre> | |
| </section> | |
| <section> | |
| <h2>4. Code Style & Engineering Standards</h2> | |
| <ul> | |
| <li><strong>Python Version:</strong> Tested on Python 3.10 and 3.11. Avoid deprecated language features.</li> | |
| <li><strong>Docstrings & Comments:</strong> Preserve documentation integrity. Explain non-obvious mathematical rationale.</li> | |
| <li><strong>Tensor Operations:</strong> Ensure numerical stability. Avoid bare divisions and use <code>masked_fill</code> rather than <code>mask * float("-inf")</code>.</li> | |
| </ul> | |
| </section> | |
| <section> | |
| <h2>5. How to Submit a Pull Request</h2> | |
| <ol> | |
| <li>Create a descriptive topic branch (e.g. <code>git checkout -b feat/triton-fused-kernel</code>).</li> | |
| <li>Commit atomic, well-documented changes.</li> | |
| <li>Push to your fork and open a Pull Request against <code>main</code> on the Hugging Face repository.</li> | |
| </ol> | |
| </section> | |
| </main> | |
| <footer> | |
| <div class="footer-links"> | |
| <a href="index.html">Home</a> | |
| <a href="architecture.html">Architecture</a> | |
| <a href="methodology.html">Methodology</a> | |
| <a href="benchmarks.html">Benchmarks</a> | |
| <a href="documentation.html">Docs</a> | |
| <a href="limitations.html">Limitations</a> | |
| <a href="sitemap.xml">Sitemap</a> | |
| <a href="llms.txt">llms.txt</a> | |
| <a href="https://huggingface.co/Premchan369/Q-TensorFormer">Hugging Face</a> | |
| </div> | |
| <p>© 2026 Premchand Yadav. Licensed under Apache-2.0.</p> | |
| </footer> | |
| </body> | |
| </html> | |