Update README.md

README.md

@@ -14,15 +14,11 @@ library_name: transformers
 # katanemo/Arch-Router-1.5B
 
 ## Overview
-With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to operationalize the use of different models. 
+With the rapid proliferation of large language models (LLMs) -- each optimized for different strengths, style, or latency/cost profile -- routing has become an essential technique to operationalize the use of different models. However, existing LLM routing approaches are limited in two key ways: they evaluate performance using benchmarks that often fail to capture human preferences driven by subjective evaluation criteria, and they typically select from a limited pool of models. 
 
-However, existing LLM routing approaches are limited in two key ways: they evaluate performance using benchmarks that often fail to capture human preferences driven by subjective evaluation criteria, and they typically select from a limited pool of models. 
-We introduce a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. 
-Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. 
+We introduce a preference-aligned routing framework that guides model selection by matching queries to user-defined domains (e.g., travel) or action types (e.g., image editing) -- offering a practical mechanism to encode preferences in routing decisions. Specifically, we introduce Arch-Router, a compact 1.5B model that learns to map queries to domain-action preferences for model routing decisions. Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. 
 
-Experiments on conversational datasets demonstrate that our approach achieves state-of-the-art (SOTA) results in matching queries with human preferences, outperforming top proprietary models. 
-
-Arch-Router powers [Arch](https://github.com/katanemo/arch) the open-source AI-native proxy for agents and enables seamless, preference-based routing in multi-LLM systems.
+Arch-Router powers [Arch](https://github.com/katanemo/arch) the open-source AI-native proxy for agents to enable preference-based routing in multi-LLM systems in a seamless way.
 
 ### How It Works
 
@@ -39,8 +35,6 @@ Both domain and action configs are associated with preferred models or model var
 - **Flexible and Adaptive**: Supports evolving user needs, model updates, and new domains/actions without retraining the router.
 - **Production-Ready Performance**: Optimized for low-latency, high-throughput applications in multi-model environments.
 
-Arch-Router powers [Arch](https://github.com/katanemo/arch) the open-source AI-native proxy for agents and enables seamless, preference-based routing in multi-LLM systems.
-
 # Requirements
 The code of Arch-Router-1.5B has been in the Hugging Face `transformers` library and we advise you to install latest version:
 ```bash