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

gallery/index.yaml

@@ -3309,7 +3309,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -3327,7 +3327,7 @@
   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:
@@ -3345,7 +3345,7 @@
   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:
@@ -6963,7 +6963,7 @@
       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
@@ -23363,3 +23363,29 @@
     - filename: Qwen3-Grand-Horror-Light-1.7B.Q4_K_M.gguf
       sha256: cbbb0c5f6874130a8ae253377fdc7ad25fa2c1e9bb45f1aaad88db853ef985dc
       uri: huggingface://mradermacher/Qwen3-Grand-Horror-Light-1.7B-GGUF/Qwen3-Grand-Horror-Light-1.7B.Q4_K_M.gguf
+- !!merge <<: *qwen3vl
+  name: "qwen.qwen3-vl-235b-a22b-instruct"
+  urls:
+    - https://huggingface.co/DevQuasar/Qwen.Qwen3-VL-235B-A22B-Instruct-GGUF
+  description: |
+    **Qwen3-VL-235B-A22B-Instruct** is a state-of-the-art vision-language model from the Qwen series, designed for advanced multimodal understanding and reasoning. With a massive 235 billion parameters, it excels in both visual and textual comprehension, supporting complex tasks such as image captioning, visual question answering, document understanding, and spatial reasoning.
+
+    Key features include:
+    - **Ultra-long context** (up to 1M tokens), enabling deep analysis of books, long videos, and detailed documents.
+    - **Advanced visual perception** with high-precision object detection, spatial reasoning, and 3D grounding.
+    - **Multilingual OCR** (32 languages) with strong performance in low-light, blurry, or tilted conditions.
+    - **Visual agent capabilities**: Can interpret and interact with GUIs on PC or mobile devices.
+    - **Code generation from visuals**: Converts images and videos into HTML, CSS, JavaScript, and Draw.io diagrams.
+    - **Enhanced multimodal reasoning** for STEM problems, causal analysis, and logical inference.
+    - Built-in support for **interleaved-MRoPE**, **DeepStack fusion**, and **text-timestamp alignment** for superior temporal and spatial modeling.
+
+    Available in both dense and Mixture-of-Experts (MoE) architectures, it is optimized for deployment across edge and cloud environments. Ideal for research, AI agents, and enterprise applications requiring deep vision-language synergy.
+
+    *Note: This is the original model from Alibaba’s Qwen team. The GGUF version available at DevQuasar/Qwen.Qwen3-VL-235B-A22B-Instruct-GGUF is a quantized variant for efficient local inference.*
+  overrides:
+    parameters:
+      model: Qwen.Qwen3-VL-235B-A22B-Instruct.Q4_K_M-00001-of-00011.gguf
+  files:
+    - filename: Qwen.Qwen3-VL-235B-A22B-Instruct.Q4_K_M-00001-of-00011.gguf
+      sha256: 41c963cc019dbb4d946ca7ff69baed17181b33dd7dbd8498d75dfccb21549fe0
+      uri: huggingface://DevQuasar/Qwen.Qwen3-VL-235B-A22B-Instruct-GGUF/Qwen.Qwen3-VL-235B-A22B-Instruct.Q4_K_M-00001-of-00011.gguf