Update README.md

README.md

@@ -11,7 +11,7 @@ We are introducing Ling-3.0-tiny, a native hybrid reasoning model with 7.9B tota
 
 Key highlights of the model are summarized below:
 
-+ **Efficient Hybrid-Linear Architecture:** Ling-3.0-tiny uses a 3:1 alternating stacking of KDA and MLA (3 Kimi Delta Attention layers followed by 1 Multi-Head Latent Attention layer in every 4-layer block), combined with a sparse MoE comprising 128 routed experts. Each token activates only 8 routed experts and 1 shared expert, allowing the model—with 7.9B total parameters and 1.3B activated parameters—to balance long-context modeling capability, parameter efficiency, and computational cost.
++ **Efficient Hybrid-Linear Architecture:** Ling-3.0-tiny uses a 3:1 alternating stacking of KDA and MLA (3 Kimi Delta Attention layers followed by 1 Multi-Head Latent Attention layer in every 4-layer block), combined with a sparse MoE comprising 128 routed experts. Each token activates only 8 routed experts and 1 shared expert, allowing the model to balance long-context modeling capability, parameter efficiency, and computational cost.
 + **Native Hybrid Reasoning and Agentic Capabilities:** Ling-3.0-tiny supports both fast responses and multi-step reasoning, with thinking mode configurable per request through `enable_thinking`. It delivers balanced performance across general agent tasks, tool use, mathematical and scientific reasoning, and instruction following.
 + **Local and Edge Deployment:** BF16, FP8, and INT4 weights are provided to support different requirements, including high-precision evaluation, balancing throughput and resource usage, and deployment in resource-constrained environments.