| --- |
| license: apache-2.0 |
| --- |
| |
| <!-- |
| NORTH CODE QUANT - MODEL CARD TEMPLATE |
| Designed for GGUF Quantizations |
| --> |
|
|
| <style> |
| :root { |
| --nc-bg: #0f1115; |
| --nc-card: #161b22; |
| --nc-border: #30363d; |
| --nc-text-main: #e6edf3; |
| --nc-text-muted: #8b949e; |
| --nc-accent: #58a6ff; |
| --nc-accent-glow: rgba(88, 166, 255, 0.15); |
| --nc-code-bg: #0d1117; |
| --font-sans: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif; |
| --font-mono: ui-monospace, SFMono-Regular, "SF Mono", Menlo, Consolas, monospace; |
| } |
| |
| .nc-container { |
| font-family: var(--font-sans); |
| color: var(--nc-text-main); |
| line-height: 1.6; |
| max-width: 900px; |
| margin: 0 auto; |
| padding: 2rem 1rem; |
| background-color: var(--nc-bg); |
| } |
| |
| /* Typography & Links */ |
| .nc-container h1 { font-size: 2.5rem; border-bottom: none; margin-bottom: 0.5rem; letter-spacing: -0.02em; } |
| .nc-container h2 { font-size: 1.5rem; margin-top: 2.5rem; border-bottom: 1px solid var(--nc-border); padding-bottom: 0.5rem; } |
| .nc-container h3 { font-size: 1.1rem; color: var(--nc-text-muted); text-transform: uppercase; letter-spacing: 0.05em; margin-top: 2rem;} |
| .nc-container a { color: var(--nc-accent); text-decoration: none; transition: opacity 0.2s; } |
| .nc-container a:hover { text-decoration: underline; } |
| |
| /* Hero Section */ |
| .nc-hero { |
| display: flex; |
| align-items: center; |
| gap: 1rem; |
| margin-bottom: 2rem; |
| } |
| .nc-badge { |
| background: var(--nc-accent-glow); |
| color: var(--nc-accent); |
| padding: 0.25rem 0.75rem; |
| border-radius: 999px; |
| font-size: 0.85rem; |
| font-weight: 600; |
| border: 1px solid rgba(88, 166, 255, 0.3); |
| } |
| |
| /* Info Grid */ |
| .nc-grid { |
| display: grid; |
| grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); |
| gap: 1rem; |
| margin: 1.5rem 0; |
| } |
| .nc-stat-card { |
| background: var(--nc-card); |
| border: 1px solid var(--nc-border); |
| border-radius: 8px; |
| padding: 1rem; |
| } |
| .nc-stat-label { font-size: 0.8rem; color: var(--nc-text-muted); display: block; margin-bottom: 0.25rem; } |
| .nc-stat-value { font-size: 1.1rem; font-weight: 600; color: var(--nc-text-main); } |
| |
| /* Code Blocks */ |
| .nc-code-block { |
| position: relative; |
| background: var(--nc-code-bg); |
| border: 1px solid var(--nc-border); |
| border-radius: 8px; |
| padding: 1rem; |
| margin: 1rem 0; |
| overflow-x: auto; |
| } |
| .nc-code-block code { |
| font-family: var(--font-mono); |
| font-size: 0.9rem; |
| color: #c9d1d9; |
| white-space: pre; |
| } |
| .nc-cli-label { |
| position: absolute; |
| top: 0.5rem; |
| right: 0.75rem; |
| font-size: 0.7rem; |
| color: var(--nc-text-muted); |
| text-transform: uppercase; |
| letter-spacing: 0.05em; |
| pointer-events: none; |
| } |
| |
| /* GGUF Table */ |
| .nc-table-wrapper { overflow-x: auto; margin: 1.5rem 0; border-radius: 8px; border: 1px solid var(--nc-border); } |
| table.nc-table { width: 100%; border-collapse: collapse; font-size: 0.9rem; } |
| table.nc-table th { |
| background: var(--nc-card); |
| text-align: left; |
| padding: 0.75rem 1rem; |
| color: var(--nc-text-muted); |
| font-weight: 600; |
| border-bottom: 1px solid var(--nc-border); |
| } |
| table.nc-table td { padding: 0.75rem 1rem; border-bottom: 1px solid var(--nc-border); } |
| table.nc-table tr:last-child td { border-bottom: none; } |
| table.nc-table tr:hover td { background: rgba(255,255,255,0.02); } |
| .nc-quant-tag { |
| font-family: var(--font-mono); |
| font-size: 0.8rem; |
| background: rgba(255,255,255,0.06); |
| padding: 0.15rem 0.4rem; |
| border-radius: 4px; |
| } |
| |
| /* Disclaimer Box */ |
| .nc-disclaimer { |
| margin-top: 3rem; |
| padding: 1rem; |
| border-left: 3px solid var(--nc-border); |
| color: var(--nc-text-muted); |
| font-size: 0.85rem; |
| } |
| |
| /* Responsive */ |
| @media (max-width: 600px) { |
| .nc-container h1 { font-size: 1.8rem; } |
| .nc-grid { grid-template-columns: 1fr 1fr; } |
| } |
| </style> |
| |
| <div class="nc-container"> |
| |
| <!-- HERO --> |
| <div class="nc-hero"> |
| <h1>North Code Quant</h1> |
| <span class="nc-badge">GGUF</span> |
| <span class="nc-badge">Code Generation</span> |
| </div> |
| <p style="color: var(--nc-text-muted); font-size: 1.1rem; margin-top: -1rem;"> |
| High-performance quantized GGUF builds of Cohere's North Code model.<br> |
| Optimized for local inference via llama.cpp, LM Studio, and Ollama. |
| </p> |
| |
| <!-- KEY STATS --> |
| <div class="nc-grid"> |
| <div class="nc-stat-card"> |
| <span class="nc-stat-label">Base Model</span> |
| <span class="nc-stat-value">Cohere North Code</span> |
| </div> |
| <div class="nc-stat-card"> |
| <span class="nc-stat-label">Architecture</span> |
| <span class="nc-stat-value">Cohere / Command-R</span> |
| </div> |
| <div class="nc-stat-card"> |
| <span class="nc-stat-label">Context Length</span> |
| <span class="nc-stat-value">128K Tokens</span> |
| </div> |
| <div class="nc-stat-card"> |
| <span class="nc-stat-label">License</span> |
| <span class="nc-stat-value">CC-BY-NC / Custom</span> |
| </div> |
| </div> |
| |
| <!-- QUICK START --> |
| <h2>⚡ Quick Start</h2> |
| |
| <h3>LM Studio</h3> |
| <p style="color: var(--nc-text-muted); font-size: 0.95rem;"> |
| Search for <strong>"North Code Quant"</strong> in the LM Studio search bar, select your preferred quantization level from the sidebar, and click Download. |
| </p> |
| |
| <h3>llama.cpp</h3> |
| <div class="nc-code-block"> |
| <span class="nc-cli-label">Bash</span> |
| <code>./llama-cli -m north-code-quant-Q4_K_M.gguf \ |
| --ctx-size 8192 \ |
| --threads $(nproc) \ |
| --prompt "def fibonacci(n):"</code> |
| </div> |
| |
| <!-- AVAILABLE QUANTS --> |
| <h2>📦 Available Quants</h2> |
| <p style="color: var(--nc-text-muted); margin-bottom: 1rem;"> |
| Files are sorted by size and quality. <strong>Q4_K_M</strong> is recommended for most users as the best balance of speed and perplexity. |
| </p> |
| |
| <div class="nc-table-wrapper"> |
| <table class="nc-table"> |
| <thead> |
| <tr> |
| <th>File Name</th> |
| <th>Quant Type</th> |
| <th>Size</th> |
| <th>Description</th> |
| </tr> |
| </thead> |
| <tbody> |
| <!-- UPDATE THESE ROWS WITH YOUR ACTUAL FILES --> |
| <tr> |
| <td><code>North-Code-Quant.gguf</code></td> |
| <td><span class="nc-quant-tag">Q8_0</span></td> |
| <td>-- GB</td> |
| <td>Near-lossless. Best quality, higher VRAM/RAM requirement.</td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| |
| <!-- MODEL DETAILS --> |
| <h2>📝 About This Quantization</h2> |
| <p> |
| These GGUF files were converted from the official |
| <a href="https://huggingface.co/CohereForAI/c4ai-command-r-plus" target="_blank">Cohere North Code</a> |
| weights using <code>llama.cpp</code> with importance matrix calibration for optimal token-level precision retention. |
| </p> |
| |
| <!-- DISCLAIMER --> |
| <div class="nc-disclaimer"> |
| <strong>⚠️ Disclaimer:</strong> This is a quantized derivative model. While quants retain most of the base model's capabilities, |
| lower-bit quantizations may exhibit degraded performance in edge-case code generation or multilingual tasks. |
| Always verify generated code before execution. This model inherits the license terms of the original Cohere North Code model. |
| </div> |
| |
| </div> |