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 +23363,26 @@
     - 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 <<: *afm
+  name: "reform-32b-i1"
+  urls:
+    - https://huggingface.co/mradermacher/ReForm-32B-i1-GGUF
+  description: |
+    **ReForm-32B** is a large-scale, reflective autoformalization language model developed by Guoxin Chen and collaborators, designed to convert natural language mathematical problems into precise formal proofs (e.g., in Lean 4) with high semantic accuracy. It leverages a novel training paradigm called **Prospective Bounded Sequence Optimization (PBSO)**, enabling the model to iteratively *generate → verify → refine* its outputs, significantly improving correctness and consistency.
+
+    Key features:
+    - **State-of-the-art performance**: Achieves +22.6% average improvement over leading baselines across benchmarks like miniF2F, ProofNet, Putnam, and AIME 2025.
+    - **Reflective reasoning**: Incorporates self-correction through a built-in verification loop, mimicking expert problem-solving.
+    - **High-fidelity formalization**: Optimized for mathematical rigor, making it ideal for formal verification and AI-driven theorem proving.
+
+    Originally released by the author **GuoxinChen/ReForm-32B**, this model is part of an open research effort in AI for mathematics. It is now available in GGUF format (e.g., via `mradermacher/ReForm-32B-i1-GGUF`) for efficient local inference.
+
+    > 📌 *For the original, unquantized model, refer to:* [GuoxinChen/ReForm-32B](https://huggingface.co/GuoxinChen/ReForm-32B)
+    > 📚 *Paper:* [ReForm: Reflective Autoformalization with PBSO](https://arxiv.org/abs/2510.24592)
+  overrides:
+    parameters:
+      model: ReForm-32B.i1-Q4_K_M.gguf
+  files:
+    - filename: ReForm-32B.i1-Q4_K_M.gguf
+      sha256: a7f69d6e2efe002368bc896fc5682d34a1ac63669a4db0f42faf44a29012dc3f
+      uri: huggingface://mradermacher/ReForm-32B-i1-GGUF/ReForm-32B.i1-Q4_K_M.gguf