kykim0

"""PPO v2 trainer."""

import logging
import random

from accelerate import PartialState
from datasets import load_dataset
import torch
from transformers import (
    AutoModelForCausalLM,

kykim0

import shutil

from accelerate import PartialState
from datasets import load_dataset
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,

kykim0

"""
Conversation prompt templates.

We kindly request that you import fastchat instead of copying this file if you wish to use it.
If you have any changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
"""

import base64
import dataclasses
from enum import auto, IntEnum

kykim0

gemma-7b-sft:
  prompt_template: "gemma-7b-sft/prompt.txt"
  fn_completions: "huggingface_local_completions"
  completions_kwargs:
    model_name: "kykim0/gemma-7b-ultrachat-sft"
    model_kwargs:
      torch_dtype: 'bfloat16'
    max_new_tokens: 512
    temperature: 0.7
    top_p: 1.0

kykim0

class RewardTrainer(Trainer):
    r"""
    The RewardTrainer can be used to train your custom Reward Model. It is a subclass of the
    `transformers.Trainer` class and inherits all of its attributes and methods. It is recommended to use
    an `AutoModelForSequenceClassification` as the reward model. The reward model should be trained on a dataset
    of paired examples, where each example is a tuple of two sequences. The reward model should be trained to
    predict which example in the pair is more relevant to the task at hand.

    The reward trainer expects a very specific format for the dataset. The dataset should contain two 4 entries at least
    if you don't use the default `RewardDataCollatorWithPadding` data collator. The entries should be named

kykim0

# Reward modeling on preference data.

from collections import defaultdict
import logging
import os
from random import sample
import sys

from alignment import (
    DataArguments,

kykim0

"""Process inference output files."""

from collections import defaultdict
import csv
import glob
import json
import os

from fastchat.llm_judge.common import load_questions
from fastchat.model import get_conversation_template

kykim0

"""Process inference output files."""

from collections import defaultdict
import csv
import glob
import json
import os

from fastchat.llm_judge.common import load_questions
from fastchat.model import get_conversation_template

kykim0

import json
import os
import random
import time

import shortuuid
import torch
from tqdm import tqdm

from fastchat.llm_judge.common import load_questions, temperature_config