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 +23049,45 @@
     - 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 <<: *llama31
+  name: "lmunit-llama3.1-70b-i1"
+  urls:
+    - https://huggingface.co/mradermacher/LMUnit-llama3.1-70b-i1-GGUF
+  description: |
+    **Model Name:** LMUnit-llama3.1-70b
+    **Developer:** Contextual AI
+    **Base Model:** meta-llama/Llama-3.1-70B-Instruct
+    **License:** CC BY-NC 4.0 (for evaluation use only)
+
+    **Description:**
+    LMUnit is a highly specialized, fine-tuned language model designed for fine-grained evaluation of AI responses using natural language unit tests. It assesses how well a response satisfies specific criteria by generating a continuous score between 1 and 5, enabling precise, human-aligned evaluation across diverse tasks.
+
+    Trained on synthetic data with multi-objective learning, LMUnit excels in preference modeling, direct scoring, and nuanced task evaluation—achieving top-tier results on benchmarks like FLASK, BiGGen Bench, and RewardBench (93.5% accuracy). It is optimized for use in evaluation pipelines, particularly for testing the correctness, coherence, and alignment of long-form and complex AI-generated outputs.
+
+    **Use Case:**
+    Ideal for researchers and developers building robust evaluation systems, benchmarking LLMs, or validating the quality of model outputs in production environments.
+
+    **Key Features:**
+    - Finetuned from Llama-3.1-70B-Instruct
+    - Evaluates responses using natural language unit tests
+    - High alignment with human judgment
+    - Supports continuous scoring (1–5) for fine-grained feedback
+    - Open access for research and evaluation (non-commercial use)
+
+    **Citation:**
+    ```bibtex
+    @inproceedings{saadfalcon2025lmunit,
+      title={{LMUnit}: Fine-grained Evaluation with Natural Language Unit Tests},
+      author={Jon Saad-Falcon and Rajan Vivek and William Berrios and Nandita Shankar Naik and Matija Franklin and Bertie Vidgen and Amanpreet Singh and Douwe Kiela and Shikib Mehri},
+      booktitle={Findings of the Association for Computational Linguistics: EMNLP 2025},
+      year={2025},
+      url={https://arxiv.org/abs/2412.13091}
+    }
+    ```
+  overrides:
+    parameters:
+      model: LMUnit-llama3.1-70b.i1-Q4_K_M.gguf
+  files:
+    - filename: LMUnit-llama3.1-70b.i1-Q4_K_M.gguf
+      sha256: 4f2cff716b66a5234a1b9468b34ac752f0ca013fa31a023f64e838933905af57
+      uri: huggingface://mradermacher/LMUnit-llama3.1-70b-i1-GGUF/LMUnit-llama3.1-70b.i1-Q4_K_M.gguf