What is the difference between llama2_7B and llama2_7B_hf?
We implemented both the llama2_7b and llama2_7b_hf models on a local network to evaluate the performance of the models. Even though the same questions were asked, different results were obtained, and in my case, llama2_7b had higher answer satisfaction.The implementation code is as follows.from transformers import AutoTokenizer, AutoModelForCausalLMtokenizer = AutoTokenizer.from_pretrained(“meta-llama/Llama-2-7b-chat-hf”)tokenizer.save_pretrained(‘Llama2-7b-tokenizer’)model = AutoModelForCausalLM.from_pretrained(“meta-llama/Llama-2-7b-chat-hf”)model.save_pretrained(‘Llama2-7b-model’)When implementing llama2_7b, we used python convert code.The code is: python convert_llama_weights_to_hf.pyHere is the answer to the same question:prompt = “What is the capital of South Korea?”and there was some difference.In hf model, it answered some strange answerbut in 7b model, it answered correct answer.I thought these two models are same, but Why do I get different answers to the same question?Is there anything that I missed?