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9955ee7eaa
Co-authored-by: sergiopaniego <sergiopaniegoblanco@gmail.com> Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# /// script
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# dependencies = [
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# "trl",
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# "vllm",
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# ]
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# ///
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from dataclasses import dataclass, field
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from typing import Optional
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from datasets import load_dataset
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from transformers import HfArgumentParser
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from vllm import LLM, SamplingParams
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from trl import HfPairwiseJudge, OpenAIPairwiseJudge
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"""
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Examples:
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python examples/scripts/evals/judge_tldr.py --model_name_or_path vwxyzjn/rloo_tldr --num_examples 1000
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Model win rate: 31.40%
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python examples/scripts/evals/judge_tldr.py --model_name_or_path vwxyzjn/rloo_tldr --judge_model gpt-3.5-turbo-0125 --num_examples 1000
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Model win rate: 51.60%
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python examples/scripts/evals/judge_tldr.py --model_name_or_path vwxyzjn/rloo_tldr --judge_model gpt-4o-mini --num_examples 1000
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Model win rate: 51.20%
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python examples/scripts/evals/judge_tldr.py --model_name_or_path vwxyzjn/ppo_tldr --num_examples 1000
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Model win rate: 46.30%
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python examples/scripts/evals/judge_tldr.py --model_name_or_path vwxyzjn/ppo_tldr --judge_model gpt-3.5-turbo-0125 --num_examples 1000
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Model win rate: 52.50%
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python examples/scripts/evals/judge_tldr.py --model_name_or_path vwxyzjn/ppo_tldr --judge_model gpt-4o-mini --num_examples 1000
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Model win rate: 63.00%
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"""
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@dataclass
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class ScriptArguments:
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r"""
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Arguments for the script.
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Args:
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model_name_or_path (`str`):
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Model name or path to the model to evaluate.
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judge_model (`str`, *optional*, defaults to `"meta-llama/Meta-Llama-3-70B-Instruct"`):
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Model name or path to the model to use as a judge. E.g., 'gpt-3.5-turbo-0125' or
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'meta-llama/Meta-Llama-3-70B-Instruct'.
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num_examples (`int`, *optional*):
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Number of examples to evaluate.
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"""
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model_name_or_path: str = field(metadata={"help": "Model name or path to the model to evaluate."})
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judge_model: str = field(
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default="meta-llama/Meta-Llama-3-70B-Instruct",
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metadata={
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"help": "Model name or path to the model to use as a judge. E.g., 'gpt-3.5-turbo-0125' or "
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"'meta-llama/Meta-Llama-3-70B-Instruct'."
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},
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)
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num_examples: Optional[int] = field(default=None, metadata={"help": "Number of examples to evaluate."})
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if __name__ == "__main__":
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# Parse the arguments
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parser = HfArgumentParser(ScriptArguments)
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script_args = parser.parse_args_into_dataclasses()[0]
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# Load the dataset
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dataset = load_dataset("trl-lib/tldr", split="validation")
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if script_args.num_examples is not None:
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dataset = dataset.select(range(script_args.num_examples))
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# Extract the prompts and reference completions
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prompts = dataset["prompt"]
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reference_completions = dataset["completion"]
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# Generate the model completions
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sampling_params = SamplingParams(temperature=0.0, top_p=0.95, max_tokens=200) # very generous max token length
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llm = LLM(model=script_args.model_name_or_path, tensor_parallel_size=1)
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outputs = llm.generate(prompts, sampling_params)
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model_completions = [output.outputs[0].text.strip() for output in outputs]
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# Judge the outputs
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if "gpt" in script_args.judge_model:
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judge = OpenAIPairwiseJudge(script_args.judge_model)
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else:
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judge = HfPairwiseJudge(script_args.judge_model)
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completions = [[c0, c1] for c0, c1 in zip(reference_completions, model_completions)]
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best_idxs = judge.judge(prompts, completions)
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model_win_rate = best_idxs.count(1) / len(best_idxs)
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print(f"Model win rate: {model_win_rate * 100:.2f}%")
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