pszemraj

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
import matplotlib.pyplot as plt
import seaborn as sns

# Define model names for base and fine-tuned versions
base_model_name = "pszemraj/tFINE-900m-e16-d32-1024ctx"  
ft_model_name = "BEE-spoke-data/tFINE-900m-e16-d32-instruct_2e"

# Load the models with appropriate dtype

pszemraj

"""
Merges all text files in a directory into a single markdown file.
"""

import logging
import re
from datetime import datetime
from pathlib import Path

import fire

pszemraj

In addition to a significant decrease in hepatic lipid accumulation in the IOE group, which inhibited energy intake by propionate enrichment, hepatic lipids were also significantly reduced in the mice in the IOP group, which was largely enriched with butyrate. Compared with the IOE group, IOP had a stronger regulatory effect on hepatic metabolism and triglyceride metabolism and higher levels of TCA cycle in the host. In addition, butyrate has the ability to promote browning of white adipose tissue (WAT) to brown adipose tissue (BAT).^[@ref39],[@ref40]^ WAT stores energy, whereas BAT uses energy for heating and consequently host energy expenditure increases.^[@ref41],[@ref42]^ However, adipose tissue weight does not change after WAT browning.^[@ref43]^ Therefore, the weight of adipose tissue of mice in the IOP group dominated by butyrate was greater than that of the mice in the IOE group dominated by propionate.

In conclusion ([Figure [7](#fig7){ref-type="fig"}](#fig7){ref-type="fig"}C), the improvement of ob

pszemraj

whistleblowing_ai.md

Whistleblowing within AI companies, especially concerning sensitive information like model sizes and training methods, is a significant and serious action. It requires careful consideration of the legal, ethical, and personal implications involved. This guide aims to provide employees with comprehensive information on safely and anonymously whistleblowing or leaking confidential AI-related data while minimizing personal and professional risks.

Table of Contents

1. Understanding Whistleblowing
2. Assessing the Need to Whistleblow
3. Legal Considerations
4. Preparing to Whistleblow

pszemraj

import logging
import os

import fire
import torch
from datasets import load_dataset
from huggingface_hub import PyTorchModelHubMixin
from torch import nn
from transformers import AutoConfig, AutoModel, AutoTokenizer

pszemraj

# !pip install -q sentence-splitter
import os
from sentence_splitter import split_text_into_sentences

REFUSAL_TERMS = [
    "sorry",
    "i can't",
    "unfortunately,",
    "as a language model",
    "as an ai language model",

pszemraj

import re


def extract_comments_and_docs(multiline_string):
    # Pattern to match lines where the first non-whitespace character is '#'

    comment_pattern = r"^\s*#(.*)"

    # Pattern to match any text within triple quotes (either ''' or """)

pszemraj

import boto3
import gzip
from datasets import load_dataset
from botocore import UNSIGNED
from botocore.config import Config

num_proc = 32
s3 = boto3.client("s3", config=Config(signature_version=UNSIGNED))

bucket_name = "softwareheritage"

pszemraj

import logging
import os

import fire
import torch
from datasets import load_dataset
from huggingface_hub import PyTorchModelHubMixin
from torch import nn
from transformers import AutoConfig, AutoModel, AutoTokenizer

pszemraj

import base64
import os
from pathlib import Path

import fire
from openai import OpenAI
from tqdm.auto import tqdm
from joblib import Memory

# Set up joblib caching