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
@@ -22938,3 +22938,59 @@
     - filename: ReForm-32B.i1-Q4_K_M.gguf
       sha256: a7f69d6e2efe002368bc896fc5682d34a1ac63669a4db0f42faf44a29012dc3f
       uri: huggingface://mradermacher/ReForm-32B-i1-GGUF/ReForm-32B.i1-Q4_K_M.gguf
+- !!merge <<: *qwen3
+  name: "qwen3-4b-thinking-2507-gspo-easy"
+  urls:
+    - https://huggingface.co/mradermacher/Qwen3-4B-Thinking-2507-GSPO-Easy-GGUF
+  description: |
+    **Model Name:** Qwen3-4B-Thinking-2507-GSPO-Easy
+    **Base Model:** Qwen3-4B (by Alibaba Cloud)
+    **Fine-tuned With:** GRPO (Generalized Reward Policy Optimization)
+    **Framework:** Hugging Face TRL (Transformers Reinforcement Learning)
+    **License:** [MIT](https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy/blob/main/LICENSE)
+
+    ---
+
+    ### 📌 Description:
+    A fine-tuned 4-billion-parameter version of **Qwen3-4B**, optimized for **step-by-step reasoning and complex problem-solving** using **GRPO**, a reinforcement learning method designed to enhance mathematical and logical reasoning in language models.
+
+    This model excels in tasks requiring **structured thinking**, such as solving math problems, logical puzzles, and multi-step reasoning, making it ideal for applications in education, AI assistants, and reasoning benchmarks.
+
+    ### 🔧 Key Features:
+    - Trained with **TRL 0.23.1** and **Transformers 4.57.1**
+    - Optimized for **high-quality reasoning output**
+    - Part of the **Qwen3-4B-Thinking** series, designed to simulate human-like thought processes
+    - Compatible with Hugging Face `transformers` and `pipeline` API
+
+    ### 📚 Use Case:
+    Perfect for applications demanding **deep reasoning**, such as:
+    - AI tutoring systems
+    - Advanced chatbots with explanation capabilities
+    - Automated problem-solving in STEM domains
+
+    ### 📌 Quick Start (Python):
+    ```python
+    from transformers import pipeline
+
+    question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+    generator = pipeline("text-generation", model="leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy", device="cuda")
+    output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+    print(output["generated_text"])
+    ```
+
+    > ✅ **Note**: This is the **original, non-quantized base model**. Quantized versions (e.g., GGUF) are available separately under the same repository for efficient inference on consumer hardware.
+
+    ---
+
+    🔗 **Model Page:** [https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy](https://huggingface.co/leonMW/Qwen3-4B-Thinking-2507-GSPO-Easy)
+    📝 **Training Details & Visualizations:** [WandB Dashboard](https://wandb.ai/leonwenderoth-tu-darmstadt/huggingface/runs/t42skrc7)
+
+    ---
+    *Fine-tuned using GRPO — a method proven to boost mathematical reasoning in open language models. Cite: Shao et al., 2024 (arXiv:2402.03300)*
+  overrides:
+    parameters:
+      model: Qwen3-4B-Thinking-2507-GSPO-Easy.Q4_K_M.gguf
+  files:
+    - filename: Qwen3-4B-Thinking-2507-GSPO-Easy.Q4_K_M.gguf
+      sha256: f75798ff521ce54c1663fb59d2d119e5889fd38ce76d9e07c3a28ceb13cf2eb2
+      uri: huggingface://mradermacher/Qwen3-4B-Thinking-2507-GSPO-Easy-GGUF/Qwen3-4B-Thinking-2507-GSPO-Easy.Q4_K_M.gguf