From 61d972a2efe61c3ab7a2175b088e7ee78ab052a1 Mon Sep 17 00:00:00 2001
From: "LocalAI [bot]" <139863280+localai-bot@users.noreply.github.com>
Date: Thu, 23 Oct 2025 11:27:04 +0200
Subject: [PATCH] chore(model gallery): :robot: add 1 new models via gallery
 agent (#6691)

chore(model gallery): :robot: add new models via gallery agent

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
---
 gallery/index.yaml | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/gallery/index.yaml b/gallery/index.yaml
index 621ad6330..6d6fcc9f4 100644
--- a/gallery/index.yaml
+++ b/gallery/index.yaml
@@ -22440,3 +22440,28 @@
     - filename: Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated-MXFP4_MOE.gguf
       sha256: acfe87d0bd3a286a31fffff780a2d7e9cc9e0b72721a6ba5c1b1c68641fb641e
       uri: huggingface://noctrex/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated-MXFP4_MOE-GGUF/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated-MXFP4_MOE.gguf
+- !!merge <<: *afm
+  name: "a2fm-32b-rl"
+  urls:
+    - https://huggingface.co/mradermacher/A2FM-32B-rl-GGUF
+  description: |
+    **A²FM-32B-rl** is a 32-billion-parameter adaptive foundation model designed for hybrid reasoning and agentic tasks. It dynamically selects between *instant*, *reasoning*, and *agentic* execution modes using a **route-then-align** framework, enabling smarter, more efficient AI behavior.
+
+    Trained with **Adaptive Policy Optimization (APO)**, it achieves state-of-the-art performance on benchmarks like AIME25 (70.4%) and BrowseComp (13.4%), while reducing inference cost by up to **45%** compared to traditional reasoning methods—delivering high accuracy at low cost.
+
+    Originally developed by **PersonalAILab**, this model is optimized for tool-aware, multi-step problem solving and is ideal for advanced AI agents requiring both precision and efficiency.
+
+    🔹 *Model Type:* Adaptive Agent Foundation Model
+    🔹 *Size:* 32B
+    🔹 *Use Case:* Agentic reasoning, tool use, cost-efficient AI agents
+    🔹 *Training Approach:* Route-then-align + Adaptive Policy Optimization (APO)
+    🔹 *Performance:* SOTA on reasoning and agentic benchmarks
+
+    📄 [Paper](https://arxiv.org/abs/2510.12838) | 🐙 [GitHub](https://github.com/OPPO-PersonalAI/Adaptive_Agent_Foundation_Models)
+  overrides:
+    parameters:
+      model: A2FM-32B-rl.Q4_K_S.gguf
+  files:
+    - filename: A2FM-32B-rl.Q4_K_S.gguf
+      sha256: 930ff2241351322cc98a24f5aa46e7158757ca87f8fd2763d9ecc4a3ef9514ba
+      uri: huggingface://mradermacher/A2FM-32B-rl-GGUF/A2FM-32B-rl.Q4_K_S.gguf