Update README.md

README.md

@@ -13,6 +13,30 @@ tags:
 This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-A3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-30B-A3B) for more details on the model.
 
+---
+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.
+
+Model Overview
+-
+Qwen3-30B-A3B has the following features:
+
+    Type: Causal Language Models
+    Training Stage: Pretraining & Post-training
+    Number of Parameters: 30.5B in total and 3.3B activated
+    Number of Paramaters (Non-Embedding): 29.9B
+    Number of Layers: 48
+    Number of Attention Heads (GQA): 32 for Q and 4 for KV
+    Number of Experts: 128
+    Number of Activated Experts: 8
+    Context Length: 32,768 natively and 131,072 tokens with YaRN.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)