Costa Huang vwxyzjn

## repro.py
data = [{'content': 'The wages earned by Robin is 30% more than that earned by Erica. The wages earned by Charles is 60% more than that earned by Erica. How much percent is the wages earned by Charles more than that earned by Robin?', 'role': 'user'}, {'content': "Let's assume Erica earns a wage of $E.\n\nRobin earns 30% more than Erica, so Robin's wage is:\nRobin's wage = E + 30% of E\nRobin's wage = E + 0.30E\nRobin's wage = 1.30E\n\nCharles earns 60% more than Erica, so Charles's wage is:\nCharles's wage = E + 60% of E\nCharles's wage = E + 0.60E\nCharles's wage = 1.60E\n\nNow, we want to find out how much percent the wages earned by Charles is more than that earned by Robin. To do this, we calculate the difference between Charles's and Robin's wages and then find out what percentage this difference is of Robin's wages.\n\nDifference in wages = Charles's wage - Robin's wage\nDifference in wages = 1.60E - 1.30E\nDifference in wages = 0.30E\n\nNow, we find out what percentage this difference is of Robin's wa

## sft_pad_token.py
# flake8: noqa
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

## visualize_token2.py
from collections import defaultdict

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from datasets import load_dataset
from rich.console import Console
from rich.table import Table
from transformers import (

## load_all_rm.py
import numpy as np
import torch
import torch.nn as nn
from transformers import (
    AutoConfig,
    AutoModel,
    PretrainedConfig,
    PreTrainedModel,
)

## visualize_tokens.py
from collections import defaultdict
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from datasets import load_dataset
from rich.console import Console
from rich.table import Table
from transformers import (
    AutoTokenizer,

## visualize_tokens.py
from collections import defaultdict
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from datasets import load_dataset
from rich.console import Console
from rich.table import Table
from transformers import (
    AutoTokenizer,

## visual.py
from datasets import load_dataset
import numpy as np
import pandas as pd
from rich.console import Console
from rich.table import Table

console = Console()
ds = load_dataset("HuggingFaceH4/OpenHermesPreferences", split="train")
idxs = np.random.choice(len(ds), 1000, replace=False)
ds = ds.select(idxs)

## dpo_descriptiveness.py
from collections import defaultdict
import random
from huggingface_hub import hf_hub_download
from datasets import Dataset
import numpy as np
import pandas as pd
from transformers import AutoTokenizer
from rich.console import Console
from rich.table import Table
from trl import DPOTrainer

## async_approach.py
"""
git clone https://github.com/argilla-io/distilabel.git
pip install -e ".[hf-inference-endpoints]"
"""
import asyncio
import os

import pandas as pd
from llm_swarm import LLMSwarm, LLMSwarmConfig
from huggingface_hub import AsyncInferenceClient

## load_checkpoint.py
from collections import defaultdict
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from datasets import load_dataset
from rich.console import Console
from rich.table import Table
from transformers import (
    AutoConfig,
	# flake8: noqa
	# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	from collections import defaultdict

	import numpy as np
	import pandas as pd
	import torch
	import torch.nn as nn
	from datasets import load_dataset
	from rich.console import Console
	from rich.table import Table
	from transformers import (
	from collections import defaultdict
	import random
	from huggingface_hub import hf_hub_download
	from datasets import Dataset
	import numpy as np
	import pandas as pd
	from transformers import AutoTokenizer
	from rich.console import Console
	from rich.table import Table
	from trl import DPOTrainer
	"""
	git clone https://github.com/argilla-io/distilabel.git
	pip install -e ".[hf-inference-endpoints]"
	"""
	import asyncio
	import os

	import pandas as pd
	from llm_swarm import LLMSwarm, LLMSwarmConfig
	from huggingface_hub import AsyncInferenceClient