Spaces:
Running
Running
Org card: absolute URL for the poster image (it did not show on the org page)
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index.html
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<li>At the file size of llama.cpp's IQ2_XXS, our Qwen3-4B reaches WikiText-2 perplexity 16.64. IQ2_XXS reaches 28.61 and the best IQ2_KT file we built reaches 18.15 (lower is better).</li>
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<li>Where we lose: Qwen3-14B (IQ2_KT 9.40, ours 9.57), prompt processing (0.74x of IQ2_XXS), and retrained models such as Ternary Bonsai 4B.</li>
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</ul>
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<img src="pingala8-poster.jpg" alt="Pingala-8 Qwen3-4B (1.24 GB) answering in a local chat window on an RTX 5070 Ti Laptop GPU">
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<h2>Links</h2>
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<ul>
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<li><a href="https://huggingface.co/spaces/Genrius-AI/llm-vram-calculator" target="_blank" rel="noopener noreferrer">VRAM calculator: normal vs Pingala-8</a></li>
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<li>At the file size of llama.cpp's IQ2_XXS, our Qwen3-4B reaches WikiText-2 perplexity 16.64. IQ2_XXS reaches 28.61 and the best IQ2_KT file we built reaches 18.15 (lower is better).</li>
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<li>Where we lose: Qwen3-14B (IQ2_KT 9.40, ours 9.57), prompt processing (0.74x of IQ2_XXS), and retrained models such as Ternary Bonsai 4B.</li>
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</ul>
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<img src="https://huggingface.co/spaces/Genrius-AI/README/resolve/main/pingala8-poster.jpg" alt="Pingala-8 Qwen3-4B (1.24 GB) answering in a local chat window on an RTX 5070 Ti Laptop GPU">
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<h2>Links</h2>
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<ul>
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<li><a href="https://huggingface.co/spaces/Genrius-AI/llm-vram-calculator" target="_blank" rel="noopener noreferrer">VRAM calculator: normal vs Pingala-8</a></li>
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