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
@@ -22599,3 +22599,51 @@
     - filename: Almost-Human-X3-32bit-1839-6B.i1-Q4_K_M.gguf
       sha256: 5dc9766b505d98d7a5ad960b321c1fafe508734ca12ff4b7c480f8afbbc1e03b
       uri: huggingface://mradermacher/Almost-Human-X3-32bit-1839-6B-i1-GGUF/Almost-Human-X3-32bit-1839-6B.i1-Q4_K_M.gguf
+- !!merge <<: *jamba
+  name: "ai21-jamba-reasoning-3b"
+  urls:
+    - https://huggingface.co/Mungert/AI21-Jamba-Reasoning-3B-GGUF
+  description: |
+    **Model Name:** AI21 Jamba Reasoning 3B
+    **Repository:** [ai21labs/AI21-Jamba-Reasoning-3B-GGUF](https://huggingface.co/ai21labs/AI21-Jamba-Reasoning-3B-GGUF)
+    **License:** Apache 2.0
+    **Pipeline:** Text Generation
+    **Architecture:** Hybrid Transformer–Mamba (28 layers: 26 Mamba, 2 Attention)
+    **Parameters:** 3B
+    **Context Length:** Up to 256,000 tokens
+    **Quantizations Available:** FP16 (6.4 GB), Q4_K_M (1.93 GB)
+    **Developed by:** AI21
+
+    ---
+
+    ### 🔍 **Overview**
+    AI21’s Jamba Reasoning 3B is a compact, high-performance reasoning model that combines the strengths of Transformers and Mamba architecture for efficient long-context processing. Despite its small size, it delivers top-tier intelligence scores—surpassing models like Llama 3.2 3B and Gemma 3 4B—on benchmarks such as MMLU-Pro and IFBench.
+
+    ### ⚡ **Key Features**
+    - **Ultra-long context (256K tokens):** Mamba layers enable scalable, memory-efficient handling of extremely long inputs.
+    - **High efficiency:** Optimized for edge devices, laptops, and low-resource environments without sacrificing performance.
+    - **Strong reasoning capabilities:** Fine-tuned for structured reasoning, tool use, code generation, and complex problem solving.
+    - **Multiple runtime support:** Works with `llama.cpp`, `vLLM`, `Ollama`, and `LM Studio`.
+
+    ### 📊 **Performance Highlights**
+    - **MMLU-Pro:** 61.0%
+    - **Humanity’s Last Exam:** 6.0%
+    - **IFBench:** 52.0%
+
+    > ✅ Ideal for use cases requiring fast, accurate reasoning over long documents—e.g., document analysis, code generation, customer support ticket triage, and research summarization.
+
+    ### 📥 **Quick Start**
+    Use with `llama.cpp` or `vLLM` for local inference. Pre-quantized GGUF files are available for lightweight deployment.
+
+    > 📌 **Note:** This is the *original* model from AI21. The repository `Mungert/AI21-Jamba-Reasoning-3B-GGUF` is a user-quantized version; always refer to `ai21labs/AI21-Jamba-Reasoning-3B-GGUF` for the official, unquantized model description.
+
+    ---
+
+    🔗 **Learn More:** [AI21 Blog – Introducing Jamba Reasoning 3B](https://www.ai21.com/blog/introducing-jamba-reasoning-3B)
+  overrides:
+    parameters:
+      model: AI21-Jamba-Reasoning-3B-q4_k_m.gguf
+  files:
+    - filename: AI21-Jamba-Reasoning-3B-q4_k_m.gguf
+      sha256: a5ad6704bb679010ffeea5a2a8c06e652ce7dec0c955f8945586ba2f5a643cc7
+      uri: huggingface://Mungert/AI21-Jamba-Reasoning-3B-GGUF/AI21-Jamba-Reasoning-3B-q4_k_m.gguf