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Update mmlu_pro_eval_adapted.py
Browse files- mmlu_pro_eval_adapted.py +1 -2
mmlu_pro_eval_adapted.py
CHANGED
@@ -18,7 +18,6 @@ import pandas as pd
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import numpy as np
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Can be found at https://github.com/TIGER-AI-Lab/MMLU-Pro/blob/main/cot_prompt_lib/initial_prompt.txt
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initial_prompt = "The following are multiple choice questions (with answers) about {$}. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice."
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@@ -59,7 +58,7 @@ def load_model(model_name, gpu_utilization=0.8):
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tensor_parallel_size=torch.cuda.device_count(),
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max_model_len=max_model_length,
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trust_remote_code=True)
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sampling_params = SamplingParams(temperature=0, max_tokens=max_new_tokens,
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stop=["Question:"])
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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import numpy as np
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logging.basicConfig(level=logging.INFO)
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# Can be found at https://github.com/TIGER-AI-Lab/MMLU-Pro/blob/main/cot_prompt_lib/initial_prompt.txt
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initial_prompt = "The following are multiple choice questions (with answers) about {$}. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice."
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tensor_parallel_size=torch.cuda.device_count(),
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max_model_len=max_model_length,
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trust_remote_code=True)
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logging.info(f"Torch Device CUDA Count: {torch.cuda.device_count()}")
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sampling_params = SamplingParams(temperature=0, max_tokens=max_new_tokens,
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stop=["Question:"])
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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