Unsloth GLM-4.7-GGUF Quantization Question
Published:Dec 28, 2025 08:08
•1 min read
•r/LocalLLaMA
Analysis
This Reddit post from r/LocalLLaMA highlights a user's confusion regarding the size and quality of different quantization levels (Q3_K_M vs. Q3_K_XL) of Unsloth's GLM-4.7 GGUF models. The user is puzzled by the fact that the supposedly "less lossy" Q3_K_XL version is smaller in size than the Q3_K_M version, despite the expectation that higher average bits should result in a larger file. The post seeks clarification on this discrepancy, indicating a potential misunderstanding of how quantization affects model size and performance. It also reveals the user's hardware setup and their intention to test the models, showcasing the community's interest in optimizing LLMs for local use.
Key Takeaways
- •Quantization methods can impact model size and performance in non-intuitive ways.
- •Understanding the specific quantization scheme used (e.g., Unsloth's) is crucial for interpreting file sizes.
- •Community forums like r/LocalLLaMA are valuable resources for troubleshooting and understanding LLM nuances.
Reference
“I would expect it be obvious, the _XL should be better than the _M… right? However the more lossy quant is somehow bigger?”