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

gallery/index.yaml

@@ -3127,7 +3127,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -3145,7 +3145,7 @@
   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:
@@ -3163,7 +3163,7 @@
   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:
@@ -6781,7 +6781,7 @@
       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
@@ -22079,3 +22079,31 @@
     - filename: Tlacuilo-12B.i1-Q4_K_M.gguf
       sha256: 94218112aa02113c8e21cd2c1d10818bea39bc6aee7e67be6014f86e80e76cb1
       uri: huggingface://mradermacher/Tlacuilo-12B-i1-GGUF/Tlacuilo-12B.i1-Q4_K_M.gguf
+- !!merge <<: *llava
+  name: "minicpm-v-4_5-hybrid"
+  urls:
+    - https://huggingface.co/steampunque/MiniCPM-V-4_5-Hybrid-GGUF
+  description: |
+    **MiniCPM-V 4.5** is a state-of-the-art, open-source vision-language model (MLLM) developed by OpenBMB, designed for high-performance multimodal understanding on-device. With only 8.7B parameters, it surpasses larger proprietary models like GPT-4o and Gemini 2.0 Pro in vision-language benchmarks, achieving a 77.0 average score on OpenCompass.
+
+    ### Key Features:
+    - **Advanced Vision Capabilities**: Excels in single image, multi-image, and high-FPS video understanding (up to 10FPS) using a novel 3D-Resampler that compresses video frames 96× while preserving detail.
+    - **High-Resolution OCR & Document Parsing**: Processes ultra-high-resolution images (up to 1.8M pixels) with 4× fewer visual tokens than most models, outperforming GPT-4o on OCRBench and OmniDocBench.
+    - **Hybrid Thinking Modes**: Offers both fast, efficient reasoning and deep, complex problem-solving modes—controlled via a reinforcement learning framework.
+    - **Multilingual & Trustworthy**: Supports over 30 languages and demonstrates strong resistance to hallucinations, excelling on MMHal-Bench.
+    - **Efficient Deployment**: Fully supports GGUF, AWQ, int4, and quantized formats for local inference via llama.cpp, Ollama, vLLM, and SGLang—ideal for edge devices and mobile apps.
+
+    ### Ideal For:
+    - On-device AI (mobile, iPad, phones)
+    - Document and OCR processing
+    - Video analysis and real-time multimodal chat
+    - Research and fine-tuning with Transformers and LLaMA-Factory
+
+    > 📌 **Model License**: Apache 2.0
+    > 🌐 **Hugging Face**: [openbmb/MiniCPM-V-4_5](https://huggingface.co/openbmb/MiniCPM-V-4_5)
+    > 📚 **Paper**: [MiniCPM-V 4.5: Cooking Efficient MLLMs](https://arxiv.org/abs/2509.18154)
+
+    > *A GPT-4o-level MLLM—powerful, efficient, and accessible.*
+  overrides:
+    parameters:
+      model: steampunque/MiniCPM-V-4_5-Hybrid-GGUF