chore(model gallery): 🤖 add 1 new models via gallery agent

gallery/index.yaml

@@ -3295,7 +3295,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -3313,7 +3313,7 @@
   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:
@@ -3331,7 +3331,7 @@
   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:
@@ -6949,7 +6949,7 @@
       sha256: 2756551de7d8ff7093c2c5eec1cd00f1868bc128433af53f5a8d434091d4eb5a
       uri: huggingface://Triangle104/Nano_Imp_1B-Q8_0-GGUF/nano_imp_1b-q8_0.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
@@ -23023,3 +23023,33 @@
     - filename: Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-PKDick-V.i1-Q4_K_M.gguf
       sha256: 6955283520e3618fe349bb75f135eae740f020d9d7f5ba38503482e5d97f6f59
       uri: huggingface://mradermacher/Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-PKDick-V-i1-GGUF/Qwen3-Yoyo-V4-42B-A3B-Thinking-TOTAL-RECALL-PKDick-V.i1-Q4_K_M.gguf
+- !!merge <<: *llama33
+  name: "grovemoe-base-i1"
+  urls:
+    - https://huggingface.co/mradermacher/GroveMoE-Base-i1-GGUF
+  description: |
+    **GroveMoE-Base**
+    *Efficient, Sparse Mixture-of-Experts LLM with Adjugate Experts*
+
+    GroveMoE-Base is a 33-billion-parameter sparse Mixture-of-Experts (MoE) language model designed for high efficiency and strong performance. Unlike dense models, only 3.14–3.28 billion parameters are activated per token, drastically reducing computational cost while maintaining high capability.
+
+    **Key Features:**
+    - **Novel Architecture**: Uses *adjugate experts* to dynamically allocate computation, enabling shared processing and significant FLOP reduction.
+    - **Efficient Inference**: Achieves high throughput with low latency, ideal for deployment in resource-constrained environments.
+    - **Based on Qwen3-30B-A3B-Base**: Up-cycled through mid-training and supervised fine-tuning, preserving strong pre-trained knowledge while adding new capabilities.
+
+    **Use Cases:**
+    Ideal for applications requiring efficient large-scale language understanding and generation—such as chatbots, content creation, and code generation—where speed and resource efficiency are critical.
+
+    **Paper:** [GroveMoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts](https://arxiv.org/abs/2508.07785)
+    **Model Hub:** [inclusionAI/GroveMoE-Base](https://huggingface.co/inclusionAI/GroveMoE-Base)
+    **GitHub:** [github.com/inclusionAI/GroveMoE](https://github.com/inclusionAI/GroveMoE)
+
+    *Note: The GGUF quantized versions (e.g., mradermacher/GroveMoE-Base-i1-GGUF) are community-quantized derivatives. The original model is the base model by inclusionAI.*
+  overrides:
+    parameters:
+      model: GroveMoE-Base.i1-Q4_K_M.gguf
+  files:
+    - filename: GroveMoE-Base.i1-Q4_K_M.gguf
+      sha256: 9d7186ba9531bf689c91176468d7a35c0aaac0cd52bd44d4ed8f7654949ef4f4
+      uri: huggingface://mradermacher/GroveMoE-Base-i1-GGUF/GroveMoE-Base.i1-Q4_K_M.gguf