chore(model gallery): 🤖 add 1 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
@@ -22950,3 +22950,28 @@
     - filename: apollo-astralis-4b.i1-Q4_K_M.gguf
       sha256: 94e1d371420b03710fc7de030c1c06e75a356d9388210a134ee2adb4792a2626
       uri: huggingface://mradermacher/apollo-astralis-4b-i1-GGUF/apollo-astralis-4b.i1-Q4_K_M.gguf
+- !!merge <<: *qwen25coder
+  name: "viscoder2-14b"
+  urls:
+    - https://huggingface.co/mradermacher/VisCoder2-14B-GGUF
+  description: |
+    **VisCoder2-14B** is a 14-billion-parameter multi-language visualization coding model developed by TIGER-Lab, designed for generating executable code that produces accurate and consistent visual outputs. Built on the **Qwen2.5-Coder-14B-Instruct** base model, it is fine-tuned via full-parameter supervised fine-tuning (SFT) on the **VisCode-Multi-679K** dataset — a large-scale instruction-tuned dataset spanning 12 programming languages.
+
+    The model specializes in **executable visualization code generation**, supporting complex tasks such as rendering charts, plots, and interactive visualizations from natural language prompts. It excels in both single-turn and multi-turn self-debugging scenarios, making it ideal for iterative code refinement.
+
+    Key features:
+    - Trained on 888 real-world visualization tasks across 8 languages (VisPlotBench benchmark).
+    - Strong performance in generating semantically correct, executable code.
+    - Designed for real-time, accurate visual rendering.
+    - Openly available under the Apache 2.0 license.
+
+    📌 **Use cases**: Code generation for data visualization, AI-driven debugging of visual code, multi-language interactive visualization agents.
+
+    🔗 [Project Page](https://tiger-ai-lab.github.io/VisCoder2) | 📚 [Paper (arXiv)](https://arxiv.org/abs/2510.23642) | 💻 [GitHub](https://github.com/TIGER-AI-Lab/VisCoder2) | 🤗 [Hugging Face](https://huggingface.co/TIGER-Lab/VisCoder2-14B)
+  overrides:
+    parameters:
+      model: VisCoder2-14B.Q4_K_S.gguf
+  files:
+    - filename: VisCoder2-14B.Q4_K_S.gguf
+      sha256: 08fd08de85aa545c7360211b909e55cb24f3d23ab6fa17da3f95d84c24bd63e4
+      uri: huggingface://mradermacher/VisCoder2-14B-GGUF/VisCoder2-14B.Q4_K_S.gguf