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
@@ -23071,3 +23071,41 @@
     - filename: Gemma-3-The-Grand-Horror-27B-Q4_k_m.gguf
       sha256: 46f0b06b785d19804a1a796bec89a8eeba8a4e2ef21e2ab8dbb8fa2ff0d675b1
       uri: huggingface://DavidAU/Gemma-3-The-Grand-Horror-27B-GGUF/Gemma-3-The-Grand-Horror-27B-Q4_k_m.gguf
+- !!merge <<: *qwen3
+  name: "qwen3-nemotron-32b-rlbff-i1"
+  urls:
+    - https://huggingface.co/mradermacher/Qwen3-Nemotron-32B-RLBFF-i1-GGUF
+  description: |
+    **Model Name:** Qwen3-Nemotron-32B-RLBFF
+    **Base Model:** Qwen/Qwen3-32B
+    **Developer:** NVIDIA
+    **License:** NVIDIA Open Model License
+
+    **Description:**
+    Qwen3-Nemotron-32B-RLBFF is a high-performance, fine-tuned large language model built on the Qwen3-32B foundation. It is specifically optimized to generate high-quality, helpful responses in a default thinking mode through advanced reinforcement learning with binary flexible feedback (RLBFF). Trained on the HelpSteer3 dataset, this model excels in reasoning, planning, coding, and information-seeking tasks while maintaining strong safety and alignment with human preferences.
+
+    **Key Performance (as of Sep 2025):**
+    - **MT-Bench:** 9.50 (near GPT-4-Turbo level)
+    - **Arena Hard V2:** 55.6%
+    - **WildBench:** 70.33%
+
+    **Architecture & Efficiency:**
+    - 32 billion parameters, based on the Qwen3 Transformer architecture
+    - Designed for deployment on NVIDIA GPUs (Ampere, Hopper, Turing)
+    - Achieves performance comparable to DeepSeek R1 and O3-mini at less than 5% of the inference cost
+
+    **Use Case:**
+    Ideal for applications requiring reliable, thoughtful, and safe responses—such as advanced chatbots, research assistants, and enterprise AI systems.
+
+    **Access & Usage:**
+    Available on Hugging Face with support for Hugging Face Transformers and vLLM.
+    **Cite:** [Wang et al., 2025 — RLBFF: Binary Flexible Feedback](https://arxiv.org/abs/2509.21319)
+
+    👉 *Note: The GGUF version (mradermacher/Qwen3-Nemotron-32B-RLBFF-i1-GGUF) is a user-quantized variant. The original model is available at nvidia/Qwen3-Nemotron-32B-RLBFF.*
+  overrides:
+    parameters:
+      model: Qwen3-Nemotron-32B-RLBFF.i1-Q4_K_M.gguf
+  files:
+    - filename: Qwen3-Nemotron-32B-RLBFF.i1-Q4_K_M.gguf
+      sha256: 000e8c65299fc232d1a832f1cae831ceaa16425eccfb7d01702d73e8bd3eafee
+      uri: huggingface://mradermacher/Qwen3-Nemotron-32B-RLBFF-i1-GGUF/Qwen3-Nemotron-32B-RLBFF.i1-Q4_K_M.gguf