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评估使用的代码:https://github.com/baichuan-inc/Baichuan-7B/blob/main/evaluation/evaluate_mmlu.py
用bf16精度测试 llama2-13-hf 和 baichuan2-13b-base llama2-13-hf: 0.550 baichuan2-13b-base: 0.564
改了一行代码,用fp32测试: #model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.bfloat16, device_map="auto",trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(args.model, device_map="auto",trust_remote_code=True) llama2-13-hf: 0.554 baichuan2-13b-base: 0.590
#model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.bfloat16, device_map="auto",trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(args.model, device_map="auto",trust_remote_code=True)
请教下,为啥baichuan2在bf16和fp32精度下结果差这么多?
The text was updated successfully, but these errors were encountered:
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评估使用的代码:https://github.com/baichuan-inc/Baichuan-7B/blob/main/evaluation/evaluate_mmlu.py
用bf16精度测试 llama2-13-hf 和 baichuan2-13b-base
llama2-13-hf: 0.550
baichuan2-13b-base: 0.564
改了一行代码,用fp32测试:
#model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype=torch.bfloat16, device_map="auto",trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(args.model, device_map="auto",trust_remote_code=True)
llama2-13-hf: 0.554
baichuan2-13b-base: 0.590
请教下,为啥baichuan2在bf16和fp32精度下结果差这么多?
The text was updated successfully, but these errors were encountered: