Zengzhi Wang SinclairCoder

## demo_pl_t5.py
from pytorch_lightning import seed_everything
from transformers import AdamW, T5ForConditionalGeneration, T5Tokenizer, AutoConfig, BartTokenizer, BartForConditionalGeneration
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import os


class MyDataset(Dataset):

## demo_hf.py
from pytorch_lightning import seed_everything
from transformers import AdamW, T5ForConditionalGeneration, T5Tokenizer, AutoConfig, BartTokenizer, BartForConditionalGeneration
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import os


class MyDataset(Dataset):

## demo_hf_t5.py
from pytorch_lightning import seed_everything
from transformers import AdamW, T5ForConditionalGeneration, T5Tokenizer, AutoConfig, BartTokenizer, BartForConditionalGeneration
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import pytorch_lightning as pl
import os


class MyDataset(Dataset):

## README_hfd.md

      
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                SinclairCoder
                / README_hfd.md
            
            
              Created
              November 11, 2023 12:02
                — forked from padeoe/README_hfd.md
            
              
                Command-line Tool for Easy Downloading of Huggingface Models
              
          
    🤗Huggingface Model Downloader

Update: The previous version has a bug. When resuming from a breakpoint, there may be an issue causing incomplete files. Please update to the latest version!!!
Considering the lack of multi-threaded download support in the official huggingface-cli, and the inadequate error handling in hf_transfer， this command-line tool smartly utilizes wget or aria2 for LFS files and git clone for the rest.
Features


⏯️ Resume from breakpoint: You can re-run it or Ctrl+C anytime.
🚀 Multi-threaded Download: Utilize multiple threads to speed up the download process.
🚫 File Exclusion: Use --exclude to skip specific files, save time for models with duplicate formats (e.g., .bin and .safetensors).
🔐 Auth Support: For gated models that require Huggingface login, use --hf_username and --hf_token to authenticate.


## radar_with_multiple_scale.py
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi'] = 1000 #分辨率

class ComplexRadar():
    """
    Create a complex radar chart with different scales for each variable
    Parameters
	from pytorch_lightning import seed_everything
	from transformers import AdamW, T5ForConditionalGeneration, T5Tokenizer, AutoConfig, BartTokenizer, BartForConditionalGeneration
	from torch.utils.data import Dataset
	from torch.utils.data import DataLoader
	import pytorch_lightning as pl
	import os



	class MyDataset(Dataset):
	import pandas as pd
	import numpy as np
	import math
	import matplotlib.pyplot as plt
	plt.rcParams['figure.dpi'] = 1000 #分辨率

	class ComplexRadar():
	"""
	Create a complex radar chart with different scales for each variable
	Parameters