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README.md
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*denote the model is finetuned without reinforcement learning from human feedback (RLHF).
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We evaluate Infinity-Instruct-3M-0613-Mistral-7B 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-Mistral-7B achieved 25.5 in AlpacaEval2.0, which is higher than the 22.5 of GPT3.5 Turbo although it does not yet use RLHF. InfInstruct-3M-0613-Mistral-7B also achieves 8.1 in MT-Bench, which is comparable to the state-of-the-art billion-parameter LLM such as Llama-3-8B-Instruct and Mistral-7B-Instruct-v0.2.
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## Performance on **Downstream tasks**
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*denote the model is finetuned without reinforcement learning from human feedback (RLHF).
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We evaluate Infinity-Instruct-3M-0613-Mistral-7B 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-Mistral-7B achieved 25.5 in AlpacaEval2.0, which is higher than the 22.5 of GPT3.5 Turbo although it does not yet use RLHF. InfInstruct-3M-0613-Mistral-7B also achieves 8.1 in MT-Bench, which is comparable to the state-of-the-art billion-parameter LLM such as Llama-3-8B-Instruct and Mistral-7B-Instruct-v0.2.
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## Performance on **Downstream tasks**
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