license: gemma
metrics:
- perplexity
base_model:
- google/gemma-3-1b-it-qat-q4_0-gguf
- bartowski/google_gemma-3-1b-it-GGUF
This is a "self" merge of https://huggingface.co/google/gemma-3-1b-it-qat-q4_0-gguf and https://huggingface.co/bartowski/google_gemma-3-1b-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 those were already calibrated with imatrix, which should squeeze some extra performance out of it.
Here are some perplexity measurements:
| Model | File size ↓ | PPL (wiki.text.raw) ↓ |
|---|---|---|
| This model | 720 MB | 28.2603 +/- 0.26947 |
| Q4_0 (bartowski) | 722 MB | 34.4906 +/- 0.34539 |
| QAT Q4_0 (google) | 1 GB | 28.2603 +/- 0.26947 |
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, but Google decided to use only Q4_0 instead, which is slightly smaller. The perplexity scores are barely within margin of error between this model and the original QAT, it seems like the embedding table starts making a difference at this small size, though the trade off is probably still worth it..