chore(model gallery): add google-gemma-3-27b-it-qat-q4_0-small

gallery/index.yaml

@@ -18,7 +18,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -36,7 +36,7 @@
   description: |
     google/gemma-3-12b-it is an open-source, state-of-the-art, lightweight, multimodal model built from the same research and technology used to create the Gemini models. It is capable of handling text and image input and generating text output. It has a large context window of 128K tokens and supports over 140 languages. The 12B variant has been fine-tuned using the instruction-tuning approach. Gemma 3 models are suitable for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes them deployable in environments with limited resources such as laptops, desktops, or your own cloud infrastructure.
   overrides:
     #mmproj: gemma-3-12b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-12b-it-Q4_K_M.gguf
   files:
@@ -54,7 +54,7 @@
   description: |
     Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Gemma-3-4b-it is a 4 billion parameter model.
   overrides:
     #mmproj: gemma-3-4b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-4b-it-Q4_K_M.gguf
   files:
@@ -517,6 +517,20 @@
     - filename: ReadyArt_Amoral-Fallen-Omega-Gemma3-12B-Q4_K_M.gguf
       sha256: a2a2e76be2beb445d3a569ba03661860cd4aef9a4aa3d57aed319e3d1bddc820
       uri: huggingface://bartowski/ReadyArt_Amoral-Fallen-Omega-Gemma3-12B-GGUF/ReadyArt_Amoral-Fallen-Omega-Gemma3-12B-Q4_K_M.gguf
+- !!merge <<: *gemma3
+  name: "google-gemma-3-27b-it-qat-q4_0-small"
+  urls:
+    - https://huggingface.co/google/gemma-3-27b-it-qat-q4_0-gguf
+    - https://huggingface.co/stduhpf/google-gemma-3-27b-it-qat-q4_0-gguf-small
+  description: |
+    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.
+  overrides:
+    parameters:
+      model: gemma-3-27b-it-q4_0_s.gguf
+  files:
+    - filename: gemma-3-27b-it-q4_0_s.gguf
+      sha256: cc4e41e3df2bf7fd3827bea7e98f28cecc59d7bd1c6b7b4fa10fc52a5659f3eb
+      uri: huggingface://stduhpf/google-gemma-3-27b-it-qat-q4_0-gguf-small/gemma-3-27b-it-q4_0_s.gguf
 - &llama4
   url: "github:mudler/LocalAI/gallery/llama3.1-instruct.yaml@master"
   icon: https://avatars.githubusercontent.com/u/153379578
@@ -2761,7 +2775,7 @@
       sha256: b9f01bead9e163db9351af036d8d63ef479d7d48a1bb44934ead732a180f371c
       uri: huggingface://bartowski/Menlo_ReZero-v0.1-llama-3.2-3b-it-grpo-250404-GGUF/Menlo_ReZero-v0.1-llama-3.2-3b-it-grpo-250404-Q4_K_M.gguf
 - &qwen25
   name: "qwen2.5-14b-instruct" ## Qwen2.5
   icon: https://avatars.githubusercontent.com/u/141221163
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
   license: apache-2.0
@@ -4722,7 +4736,7 @@
       sha256: 0fec82625f74a9a340837de7af287b1d9042e5aeb70cda2621426db99958b0af
       uri: huggingface://bartowski/Chuluun-Qwen2.5-72B-v0.08-GGUF/Chuluun-Qwen2.5-72B-v0.08-Q4_K_M.gguf
 - &smollm
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master" ## SmolLM
   name: "smollm-1.7b-instruct"
   icon: https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png
   tags:
@@ -6058,7 +6072,7 @@
       sha256: a1a0fa3e2c3f9d63f9202af9172cffbc0b519801dff740fffd39f6a063a731ef
       uri: huggingface://bartowski/Qwen2.5-14B-Instruct-1M-GGUF/Qwen2.5-14B-Instruct-1M-Q4_K_M.gguf
 - &llama31
   url: "github:mudler/LocalAI/gallery/llama3.1-instruct.yaml@master" ## LLama3.1
   icon: https://avatars.githubusercontent.com/u/153379578
   name: "meta-llama-3.1-8b-instruct"
   license: llama3.1