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qtensorformer
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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
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<a href="methodology.html">Methodology</a>
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<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>
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<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>
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<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>
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<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>
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<p>© 2026 Premchand Yadav. Licensed under Apache-2.0.</p>
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