Glyph GGUF EU AI Act Compliance Profile

<span style="color: #7FFF7F;">Glyph GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit 16724b5b6.

<span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

• Repository: https://github.com/thu-coai/Glyph
• Paper: https://arxiv.org/abs/2510.17800

Glyph is a framework for scaling the context length through visual-text compression. Instead of extending token-based context windows, Glyph renders long textual sequences into images and processes them using vision–language models (VLMs). This design transforms the challen

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