README.md

---
license: apache-2.0
language:
- en
tags:
- cuda
- tensorrt
- quantization
- tensor-cores
pipeline_tag: text-generation
---

# 🌌 MQ-Cognitive-Base // Quantised LLM Checkpoints

This repository hosts the optimized, mixed-precision quantized model weight checkpoints engineered by the **MAH Quantum Research Scholars** cohort. These weights are explicitly compiled for accelerated execution layers using native **NVIDIA® CUDA®** and **TensorRT™-LLM** runtimes.

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## ⚡ Architectural Specifications

* **Quantization Framework:** Post-Training Quantization (PTQ) / Activation-aware Weight-Quantization (AWQ)
* **Target Precision Target:** INT8 / INT4 Weight-Only Quantization Matrix
* **Hardware Optimization Optimization:** NVIDIA Compute Capability 8.0+ (Ampere, Hopper, Blackwell architectures)
* **Primary Infrastructure Node:** NVIDIA® NGC Org ID `0963318590610147`

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## 🔬 Deployment & Performance Intent

These model matrices are structured to maximize token throughput and minimize memory footprint during heavy industrial inferencing. By compressing large parameter graphs down to optimized bit-widths, our distributed node network achieves sub-60ms Time-To-First-Token (TTFT) performance on localized compute clusters.

### 📊 Benchmark Logs

```json
{
  "PERFORMANCE_METRICS": {
    "CompilationEngine": "TensorRT-LLM v0.10.x",
    "QuantizationType": "INT4-AWQ",
    "MemoryFootprintReduction": " ~72%",
    "TensorCoreUtilization": "Optimal"
  }
}