stduhpf/

google-gemma-3-12b-it-qat-q4_0-gguf-small

This is a "self" merge of https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-gguf and https://huggingface.co/bartowski/google_gemma-3-12b-it-GGUF.

The official QAT weights released by google use fp16 (instead of Q6_K) for the embeddings table, which makes this model take a significant extra amount of memory (and storage) compared to what Q4_0 quants are supposed to take. Instead of quantizing the table myself, I extracted it from Bartowski's quantized models because I thought using imatrix quants would give better quality (it doesn't, imatrix isn't used for token embeddings).

Here are some perplexity measurements:

Note that this model ends up smaller than the Q4_0 from Bartowski. This is because llama.cpp sets some tensors to Q4_1 when quantizing models to Q4_0 with imatrix, but this is a static quant. I don't understand why Q5_K_S is performing worse on that test than the default Q4_0, I wasn't expecting this outcome. This merge seems to be a good balance between model size and perplexity. I believe this is representative to the overall quality of the model.