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
@@ -23006,3 +23006,30 @@
     - filename: nvidia.Qwen3-Nemotron-32B-RLBFF.Q4_K_M.gguf
       sha256: 5dfc9f1dc21885371b12a6e0857d86d6deb62b6601b4d439e4dfe01195a462f1
       uri: huggingface://DevQuasar/nvidia.Qwen3-Nemotron-32B-RLBFF-GGUF/nvidia.Qwen3-Nemotron-32B-RLBFF.Q4_K_M.gguf
+- !!merge <<: *deepseek-r1
+  name: "r2mu-deepseek-r1-distill-llama-8b"
+  urls:
+    - https://huggingface.co/mradermacher/R2MU-DeepSeek-R1-Distill-Llama-8B-GGUF
+  description: |
+    The **R2MU-DeepSeek-R1-Distill-Llama-8B** is a distilled 8-billion-parameter language model based on the DeepSeek-R1 architecture, fine-tuned and optimized by the OPTML-Group. It leverages distillation techniques to retain strong performance while reducing size and inference cost, making it efficient for deployment on consumer hardware. The model is built upon the Llama-2 family of models and is trained on a broad range of text data to support diverse language tasks including reasoning, coding, and general conversation.
+
+    This particular version is available in multiple quantized formats (e.g., Q4_K_S, Q5_K_M, Q8_0) via the GGUF format, enabling efficient inference using tools like `llama.cpp`. The model is designed for use in local, low-resource environments without sacrificing too much accuracy.
+
+    **Key Features:**
+    - Base model: DeepSeek-R1-Distill-Llama-8B (originally by OPTML-Group)
+    - Architecture: Llama-style transformer, 8B parameters
+    - Distilled from a larger model to balance speed and performance
+    - Quantized versions available for efficient local inference
+    - Trained on diverse, high-quality text data
+    - Ideal for applications like chatbots, code generation, and reasoning
+
+    > ✅ *Note: The model is a distilled and quantized version. The original base model can be found at [OPTML-Group/R2MU-DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/OPTML-Group/R2MU-DeepSeek-R1-Distill-Llama-8B) — this repository contains GGUF-quantized variants for local inference.*
+
+    **Use Case**: Excellent for lightweight, high-performance inference on laptops, edge devices, or local servers.
+  overrides:
+    parameters:
+      model: R2MU-DeepSeek-R1-Distill-Llama-8B.Q4_K_M.gguf
+  files:
+    - filename: R2MU-DeepSeek-R1-Distill-Llama-8B.Q4_K_M.gguf
+      sha256: c5104c70e1b43aa205e46d0e88c015541239d978d5c016825aefa3dc959cf568
+      uri: huggingface://mradermacher/R2MU-DeepSeek-R1-Distill-Llama-8B-GGUF/R2MU-DeepSeek-R1-Distill-Llama-8B.Q4_K_M.gguf