Update README.md

README.md

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 ## Main Message
 
-We present our initial results validating depth up-scaling—a method that combines depthwise scaling with continued pretraining. Unlike other LLM up-scaling approaches that rely on mixture-of-experts, DUS requires no complex modifications for efficient training and inference, making it a simple yet effective strategy for scaling high-performance LLMs from smaller models.
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-In our approach, we carefully selected the dense layers from Qwen2.5-0.5B to construct our model. Notably, while Qwen2.5-0.5B was trained on *18 trillion* tokens, our model was trained on only *5 billion* tokens—over three orders of magnitude fewer—yet it achieves comparable performance.
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-**Note**: Please note that this model has not yet been instruction-tuned; instruction-tuning is an area of ongoing development.
+Here is the instruction-tuned version of the pretrained **Kiwi-1.0-0.7B** model. As can been seen in the table below, the results are at paar with the SOTA Qwen2.5-0.5B.
 
 ## Evaluation Results
 
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-
 ### Harness Evaluation
 
 - The performance evaluation is based on the tasks being evaluated on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).