Google's Official Gemma 4 QAT Q4_0 GGUFs Have Higher Precision Than Unsloth's Q4_K_XL
r/LocalLLaMA top day10 hours agoCommentaryAn analysis of Gemma 4 QAT GGUF files reveals that Google's official 'Q4_0' releases actually employ a mixed-precision strategy. For smaller models like E2B and E4B, Google keeps critical token embeddings in Q6_K and certain projection weights in F16. This makes Google's Q4_0 files larger and more precise than Unsloth's 'Q4_K_XL' versions, which default to standard Q4_0 for almost all tensors.
Exploring 2-bit QAT: Can Ultra-Compressed Large Models Outperform 4-bit Models Half Their Size?
r/LocalLLaMA top day19 hours agoCommentaryA popular Reddit thread on r/LocalLLaMA discusses the potential of 2-bit Quantization Aware Training (QAT) for large MoE models (120B to 400B). While current QAT efforts focus on 4-bit, users speculate whether a 2-bit QAT model could fit into consumer hardware (64GB/128GB RAM) and outperform a 4-bit model of half its size. This approach is proposed as a practical alternative to training ternary (1.58-bit) LLMs from scratch.
Gemma-4-26B-A4B QAT Variant Performs Poorly in llama.cpp Compared to Non-QAT Version
r/LocalLLaMA top day21 hours agoBenchmarkA LocalLLaMA user highlighted that the newly released QAT (Quantization-Aware Training) variant of Google's Gemma-4-26B-A4B model underperforms compared to its non-QAT predecessor. Testing via llama.cpp on a chessboard SVG generation task showed significant rendering errors in the QAT version. The non-QAT GGUF version, however, produced highly accurate results under identical settings.