gschoeni

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import pandas as pd
from io import StringIO
from sklearn.preprocessing import LabelEncoder

# Define a dataset class
class CustomDataset(Dataset):
    def __init__(self, features, labels):

gschoeni

accelerate==0.25.0
ai-dive==0.0.1
aiohttp==3.9.1
aiosignal==1.3.1
async-timeout==4.0.3
attrs==23.1.0
certifi==2023.7.22
charset-normalizer==3.3.2
datasets==2.14.5
dill==0.3.7

gschoeni

from langchain.llms import CTransformers
from langchain.callbacks.base import BaseCallbackHandler

# Handler that prints each new token as it is computed
class NewTokenHandler(BaseCallbackHandler):
    def on_llm_new_token(self, token: str, **kwargs) -> None:
        print(f"{token}", end="", flush=True)

# Local CTransformers wrapper for Llama-2-7B-Chat
llm = CTransformers(

gschoeni

// Read data into buffer
let path = std::path::Path::new("/path/to/your/file.zip");
let buffer: Vec<u8> = std::fs::read(path)?;

println!("Uploading {} bytes from {:?}", buffer.len(), path);

// Create the struct to hold onto and update the progress
let upload_progress = indicatif::ProgressBar::new(buffer.len() as u64);
let cursor = std::io::Cursor::new(Vec::from(buffer));
let upload_source = ReadProgress {