el4/Muse-Glimmer-30B-ONYX-GGUF overview

---

base_model: meta-models/Muse-Glimmer-30B

base_model_relation: quantized

tags:

- gguf

- ollama

- llama.cpp

- imatrix

- moe

- reasoning

- frontier

- qwen3

- onyx

pipeline_tag: text-generation

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<h1 style="margin: 0; font-size: 28px; font-weight: 900; display: flex; align-items: center; gap: 12px; color: #ffffff; border: none;"><a href="https://huggingface.co/spaces/el4/ONYX" display: flex; align-items: center; gap: 12px; style="color: #fff; text-decoration: none;">🪨 ONYX</a></h1>

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<p style="margin: 10px 0 0 0; font-size: 14px; color: #a0a0a0; font-weight: 600;">This repo contains ONYX Quants of meta-models/Muse-Glimmer-30B</p>

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<h3 style="margin: 0 0 16px 0; color: #fff; font-size: 16px; font-weight: 800; text-transform: uppercase; letter-spacing: 1px;">⚙️ The ONYX Architecture</h3>

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<h4 style="color: #fff; margin: 0 0 6px 0; font-size: 14px; font-weight: 700;">🧠 Dynamic Layer Sensitivity</h4>

<p style="color: #888; font-size: 12px; margin: 0; line-height: 1.5;">Replaces hardcoded edge boundaries with real activation variance measurements. ONYX autonomously identifies critical layers (like mid-network attention blocks) and protects them dynamically.</p>

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<h4 style="color: #fff; margin: 0 0 6px 0; font-size: 14px; font-weight: 700;">🎯 Router-Weighted Imatrix</h4>

<p style="color: #888; font-size: 12px; margin: 0; line-height: 1.5;">Captures MoE router probabilities and multiplies them into activation scales. This forces the quantizer to aggressively crush "cold" experts while fiercely protecting "hot" ones within the same tensor block.</p>

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<h4 style="color: #fff; margin: 0 0 6px 0; font-size: 14px; font-weight: 700;">🏗️ Architecture-Agnostic</h4>

<p style="color: #888; font-size: 12px; margin: 0; line-height: 1.5;">Dynamically reads HuggingFace modules and the generated F16 GGUF to map tensors. No hardcoded regex. Works out-of-the-box on Llama, DeepSeek, and custom hybrid SSM/MoE architectures.</p>

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<th style="padding: 10px; border-bottom: 2px solid #333; text-align: left; color: #ffffff; font-weight: 900;">Tier Name</th>

<th style="padding: 10px; border-bottom: 2px solid #333; text-align: left; color: #ffffff; font-weight: 900;">Target Quality</th>

<th style="padding: 10px; border-bottom: 2px solid #333; text-align: left; color: #ffffff; font-weight: 900;">Target Size</th>

<th style="padding: 10px; border-bottom: 2px solid #333; text-align: left; color: #ffffff; font-weight: 900;">Middle Layer Strategy</th>

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<tr><td style="padding: 10px; border-bottom: 1px solid #222; font-weight: 800; color: #e0e0e0;">🪨 <code style="color:#ffffff;">quality</code></td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">Q8 Match</td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">~26.6 GB</td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">IQ4_XS</td></tr>

<tr style="background: #0a0a0a;"><td style="padding: 10px; border-bottom: 1px solid #222; font-weight: 800; color: #e0e0e0;">⚖️ <code style="color:#ffffff;">balanced</code></td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">Q6 Match</td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">~26.2 GB</td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">Q5_K</td></tr>

<tr><td style="padding: 10px; border-bottom: 1px solid #222; font-weight: 800; color: #e0e0e0;">📦 <code style="color:#ffffff;">compact</code></td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">Q4 Match</td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">~25.7 GB</td><td style="padding: 10px; border-bottom: 1px solid #222; color: #888;">Q3_K</td></tr>

<tr style="background: #0a0a0a;"><td style="padding: 10px; font-weight: 800; color: #e0e0e0;">🚀 <code style="color:#ffffff;">mini</code></td><td style="padding: 10px; color: #888;">Q2 Match</td><td style="padding: 10px; color: #888;">~24.6 GB</td><td style="padding: 10px; color: #888;">IQ2_XXS</td></tr>

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<h3 style="margin: 0; color: #ffffff; border: none; font-size: 19px; font-weight: 900;">📚 Credits & Foundations</h3>

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<p style="margin: 0 0 12px 0;">👉 <b style="color: #fff;">APEX Quantization Method</b><br>Ettore Di Giacinto & Richard Palethorpe (LocalAI Team). ONYX evolves the layer-wise precision gradients and MoE-aware tensor classification outlined in the APEX technical paper into a fully dynamic, data-driven engine.</p>

<p style="margin: 0 0 12px 0;">👉 <b style="color: #fff;">Bartowski and Lamim</b><br>For the excellent semantic imatrix calibration dataset that powers ONYX's activation scaling.</p>

<p style="margin: 0 0 12px 0;">👉 <b style="color: #fff;">llama.cpp</b><br>Georgi Gerganov and contributors for the foundational inference and quantization engine.</p>

<p style="margin: 0 0 12px 0;">👉 <b style="color: #fff;">HuggingFace Accelerate</b><br>For the <code style="color:#ddd;">init_empty_weights()</code> context manager that makes the 0-RAM "Ghost Model" possible on consumer hardware.</p>

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<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="#fff"><path d="M12 1.75L5.75 12.25L12 16L18.25 12.25L12 1.75M5.75 13.5L12 17.25L18.25 13.5L12 22.25L5.75 13.5Z"/></svg>

Support the Project

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A coffee in Ethereum would be cool! Although I don't drink coffee—I think it tastes like burnt water—but a pink lemonade would be fire! 🔥

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<a href="https://etherscan.io/address/0xDEE7fa8C421BD038D32e4441ea1aDe72fE973706" target="_blank" style="display: inline-block; background: #000; border: 1px solid #666; border-radius: 12px; padding: 12px 24px; font-family: 'JetBrains Mono', monospace; font-size: 13px; color: #fff; letter-spacing: 0.5px; text-decoration: underline; text-underline-offset: 4px; transition: all 0.3s ease;">

0xDEE7fa8C421BD038D32e4441ea1aDe72fE973706

</a>

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recommended sampling parameters:

--temp 0.85 --top-p 0.95 --top-k 40 --min-p 0.05 --repeat-penalty 1.1 --presence-penalty 0.0