odellus

import torch
import peft
import json
import shutil
from peft.utils import _get_submodules
import os
import bitsandbytes as bnb
from bitsandbytes.functional import dequantize_4bit
from peft import PeftModel
from transformers import AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, BitsAndBytesConfig, CodeLlamaTokenizer

odellus

MONGODB_URL=mongodb://localhost:27017
HF_ACCESS_TOKEN=<REDACTED>

# 'name', 'userMessageToken', 'assistantMessageToken' are required
MODELS=`[
  {
    "endpoints": [{"url": "http://localhost:8081"}],
    "name": "WizardLM/WizardLM-70B-V1.0",
    "description": "WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions",
    "websiteUrl": "https://huggingface.co/WizardLM/WizardLM-70B-V1.0",

odellus

from transformers import (
  AutoConfig,
  AutoTokenizer,
  BitsAndBytesConfig,
  GenerationConfig,
  AutoModelForCausalLM,
  LlamaTokenizerFast,
  PreTrainedModel,
  TextIteratorStreamer,
  StoppingCriteria,

odellus

# coding=utf-8
# 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

odellus

import transformers
import argparse
import numpy as np
import pandas as pd

from huggingface_hub import HfFolder

from datasets import load_dataset, Dataset, load_metric, concatenate_datasets, DatasetDict
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import DataCollatorForLanguageModeling, TrainingArguments, Trainer

odellus

git branch -m old_branch new_branch         # Rename branch locally    
git push origin :old_branch                 # Delete the old branch    
git push --set-upstream origin new_branch   # Push the new branch, set local branch to track the new remote

odellus

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

odellus

import bisect
import itertools
import operator


class _BNode(object):
    __slots__ = ["tree", "contents", "children"]

    def __init__(self, tree, contents=None, children=None):
        self.tree = tree