chore(model gallery): add qwen3-32b (#5270)

Signed-off-by: Ettore Di Giacinto <[email protected]>

Author: mudler

gallery/index.yaml

@@ -44,6 +44,38 @@
     - filename: Qwen_Qwen3-30B-A3B-Q4_K_M.gguf
       sha256: a015794bfb1d69cb03dbb86b185fb2b9b339f757df5f8f9dd9ebdab8f6ed5d32
       uri: huggingface://bartowski/Qwen_Qwen3-30B-A3B-GGUF/Qwen_Qwen3-30B-A3B-Q4_K_M.gguf
+- !!merge <<: *qwen3
+  name: "qwen3-32b"
+  urls:
+    - https://huggingface.co/Qwen/Qwen3-32B
+    - https://huggingface.co/bartowski/Qwen_Qwen3-32B-GGUF
+  description: |
+    Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
+
+        Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
+        Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
+        Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
+        Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
+        Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.
+
+    Qwen3-32B has the following features:
+
+        Type: Causal Language Models
+        Training Stage: Pretraining & Post-training
+        Number of Parameters: 32.8B
+        Number of Paramaters (Non-Embedding): 31.2B
+        Number of Layers: 64
+        Number of Attention Heads (GQA): 64 for Q and 8 for KV
+        Context Length: 32,768 natively and 131,072 tokens with YaRN.
+
+        For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.
+  overrides:
+    parameters:
+      model: Qwen_Qwen3-32B-Q4_K_M.gguf
+  files:
+    - filename: Qwen_Qwen3-32B-Q4_K_M.gguf
+      sha256: e41ec56ddd376963a116da97506fadfccb50fb402bb6f3cb4be0bc179a582bd6
+      uri: huggingface://bartowski/Qwen_Qwen3-32B-GGUF/Qwen_Qwen3-32B-Q4_K_M.gguf
 - &gemma3
   url: "github:mudler/LocalAI/gallery/gemma.yaml@master"
   name: "gemma-3-27b-it"