Update README.md

README.md

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 ### 🚀 **Model Overview**
-`tiny-llava-open-elm-aimv2` is a lightweight image-text-to-text model that combines **[OpenELM](https://huggingface.co/apple/OpenELM)** as the LLM backbone and **[AIMv2-Large-Patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224)** as the vision encoder. The model has been fine-tuned using **LoRA (Low-Rank Adaptation)** for efficient training. It was developed using the **[TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory)** codebase, which provides a modular framework for lightweight multi-modal models. 
+`tiny-llava-open-elm-aimv2` is a lightweight image-text-to-text model that combines **[OpenELM 270M - INSTRUCT](https://huggingface.co/apple/OpenELM-270M-Instruct)** as the LLM backbone and **[AIMv2-Large-Patch14-224-distilled (309M)](https://huggingface.co/apple/aimv2-large-patch14-224-distilled)** as the vision encoder. The model has been fine-tuned using **LoRA (Low-Rank Adaptation)** for efficient training. It was developed using the **[TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory)** codebase, which provides a modular framework for lightweight multi-modal models. 
 
 The model is designed to run efficiently on **CPU**, making it ideal for resource-constrained environments. It is trained and evaluated on **POPE** and **TextVQA** benchmarks. The total model size is **0.6B parameters**.