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
@@ -23049,3 +23049,42 @@
     - filename: YanoljaNEXT-Rosetta-27B-2511.i1-Q4_K_M.gguf
       sha256: 0a599099e93ad521045e17d82365a73c1738fff0603d6cb2c9557e96fbc907cb
       uri: huggingface://mradermacher/YanoljaNEXT-Rosetta-27B-2511-i1-GGUF/YanoljaNEXT-Rosetta-27B-2511.i1-Q4_K_M.gguf
+- !!merge <<: *qwen3vl
+  name: "qwen3-vl-30b-a3b-instruct"
+  urls:
+    - https://huggingface.co/Mungert/Qwen3-VL-30B-A3B-Instruct-GGUF
+  description: |
+    **Model Name:** Qwen3-VL-30B-A3B-Instruct
+    **Model Type:** Vision-Language Model (VLM)
+    **Architecture:** Mixture of Experts (MoE), 30B parameters
+    **License:** Apache 2.0
+
+    **Description:**
+    Qwen3-VL-30B-A3B-Instruct is a state-of-the-art vision-language model from the Qwen series, designed for advanced multimodal understanding and reasoning. It excels in interpreting complex visual inputs—such as images and videos—while seamlessly integrating them with rich text understanding. With a native context length of up to 256K (expandable to 1M), it supports long-form content analysis, including full-book comprehension and hour-long video processing with precise temporal indexing.
+
+    Key capabilities include:
+    - **Advanced spatial and video reasoning** with Interleaved-MRoPE and Text-Timestamp Alignment for accurate event localization.
+    - **Visual agent functionality**: Can interpret and interact with GUIs on desktop and mobile devices.
+    - **Visual coding**: Generates code (HTML/CSS/JS/Draw.io) from visual inputs.
+    - **High-precision OCR** across 32 languages, including low-light, blurred, or tilted images and rare/ancient scripts.
+    - **Strong multimodal reasoning** in STEM, math, and evidence-based problem solving.
+    - **Deep image-text alignment** via DeepStack, enabling fine-grained visual understanding and 3D grounding.
+
+    **Use Cases:**
+    - AI assistants with visual comprehension
+    - Document and video analysis
+    - Automated UI interaction and task automation
+    - Content creation from visual inputs
+    - Research and enterprise applications requiring robust multimodal intelligence
+
+    **Repository:** [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)
+    **Inference Support:** Hugging Face Transformers, llama.cpp (GGUF), ModelScope
+
+    > ✅ **Note:** This is the **original, unquantized** model by Alibaba's Qwen Team. The GGUF version (e.g., `Mungert/Qwen3-VL-30B-A3B-Instruct-GGUF`) is a community-quantized variant for local inference and may differ in performance and precision. Always refer to the official repository for the canonical model.
+  overrides:
+    parameters:
+      model: Qwen3-VL-30B-A3B-Instruct-q4_k_m.gguf
+  files:
+    - filename: Qwen3-VL-30B-A3B-Instruct-q4_k_m.gguf
+      sha256: 2fdbcf02a8c6c87a0c1273a456a12fa865b62f4588d2ea0493b2add16f30424e
+      uri: huggingface://Mungert/Qwen3-VL-30B-A3B-Instruct-GGUF/Qwen3-VL-30B-A3B-Instruct-q4_k_m.gguf