Update README.md

README.md

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 Infinity-Instruct-3M-0613-Llama3-70B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-3M and Infinity-Instruct-0613](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to GPT4-0613.
 
+## **News**
+- 🔥🔥🔥[2024/06/28] We release the model weight of [InfInstruct-Llama3-70B 0613](https://huggingface.co/BAAI/Infinity-Instruct-3M-0613-Llama3-70B). It shows favorable results on AlpacaEval 2.0 compared to GPT4-0613 without RLHF.
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+- 🔥🔥🔥[2024/06/21] We release the model weight of [InfInstruct-Mistral-7B 0613](https://huggingface.co/BAAI/Infinity-Instruct-3M-0613-Mistral-7B). It shows favorable results on AlpacaEval 2.0 compared to Mixtral 8x7B v0.1, Gemini Pro, and GPT-3.5 without RLHF.
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+- 🔥🔥🔥[2024/06/13] We share the intermediate result of our data construction process (corresponding to the [InfInstruct-3M](https://huggingface.co/datasets/BAAI/Infinity-Instruct) in the table below). Our ongoing efforts focus on risk assessment and data generation. The finalized version with 10 million instructions is scheduled for release in late June.
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 ## **Training Details**
 
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 *denote the model is finetuned without reinforcement learning from human feedback (RLHF). 
 
-We evaluate Infinity-Instruct-3M-0613-Llama3-70B on the two most popular instructions following benchmarks. Mt-Bench is a set of challenging multi-turn questions including code, math and routine dialogue. AlpacaEval2.0 is based on AlpacaFarm evaluation set. Both of these two benchmarks use GPT-4 to judge the model answer. AlpacaEval2.0 displays a high agreement rate with human-annotated benchmark, Chatbot Arena. The result shows that InfInstruct-3M-0613-Llama3-70B achieved 31.2 in AlpacaEval2.0, which is higher than the 30.4 of GPT4-0613 Turbo although it does not yet use RLHF.
+We evaluate Infinity-Instruct-3M-0613-Llama3-70B on the two most popular instructions following benchmarks. Mt-Bench is a set of challenging multi-turn questions including code, math and routine dialogue. AlpacaEval2.0 is based on AlpacaFarm evaluation set. Both of these two benchmarks use GPT-4 to judge the model answer. AlpacaEval2.0 displays a high agreement rate with human-annotated benchmark, Chatbot Arena. The result shows that InfInstruct-3M-0613-Llama3-70B achieved 31.2 in AlpacaEval2.0, which is higher than the 30.4 of GPT4-0613 Turbo although it does not yet use RLHF.  
 
 ## **How to use**