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edgetam
EdgeTAM is an ultra-efficient variant of the Segment Anything Model (SAM) for image segmentation. It uses a RepViT backbone and is only ~16MB quantized (Q4_0), making it ideal for edge deployment. Supports point-prompted and box-prompted image segmentation via the /v1/detection endpoint. Powered by sam3.cpp (C/C++ with GGML).

Repository: localaiLicense: apache-2.0

google-gemma-3-27b-it-qat-q4_0-small
This is a requantized version of https://huggingface.co/google/gemma-3-27b-it-qat-q4_0-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. Requantizing with llama.cpp achieves a very similar result. 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. The perplexity score for this one is even lower with this model compared to the original model by Google, but the results are within margin of error, so it's probably just luck. I also fixed the control token metadata, which was slightly degrading the performance of the model in instruct mode.

Repository: localaiLicense: gemma