alfredplpl

import torch
from transformers import AutoTokenizer,AutoModelForCausalLM
import pandas

model_name_or_path = "cyberagent/calm2-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="cpu", torch_dtype=torch.float32)

# https://github.com/nlp-waseda/JMMLU/blob/main/JMMLU/college_computer_science.csv
df=pandas.read_csv("college_computer_science.csv",header=None)

alfredplpl

# Reference #1: https://note.com/npaka/n/nc55e44e407ff
# Reference #2: https://huggingface.co/blog/gemma-peft
# Licence: MIT

from peft import LoraConfig

lora_config = LoraConfig(
    r=8,
    target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
    task_type="CAUSAL_LM",

alfredplpl

# MIT
from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_single_file('/path/to/checkpoint.safetensors', torch_dtype=torch.float16)
pipe.save_pretrained('/path/to/diffusers_version')

alfredplpl

from diffusers import DiffusionPipeline
import torch
from consistencydecoder import ConsistencyDecoder
from PIL import Image
import numpy as np

pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", torch_dtype=torch.float32)

decoder_consistency = ConsistencyDecoder(device="cuda:0") # Model size: 2.49 GB

alfredplpl

alfredplpl

from datasets import load_dataset
import requests
from PIL import Image
from tqdm import tqdm

dataset = load_dataset("laion/dalle-3-dataset",split="train")


for i,row in enumerate(tqdm(dataset)):
    with open(f"dalle3/{i:06}.txt","w") as f:

alfredplpl

# MIT License
from transformers import AutoTokenizer
import transformers
from langchain.document_loaders import PyPDFLoader
import torch

model = "NousResearch/Yarn-Llama-2-13b-128k"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(

alfredplpl

# MIT License
# This code will run on VRAM 12GB+ GPU such as T4, RTX 3060
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms.huggingface_pipeline import HuggingFacePipeline

alfredplpl

import copy

import torch
from denoising_diffusion_pytorch import Unet, GaussianDiffusion, Trainer

from torchvision import datasets, transforms
from torch.optim import Adam
from torch.utils.data import Dataset
from torch.utils import data
from torch.cuda.amp import GradScaler

alfredplpl

import torch

from torch.utils.data import Dataset
from torchvision import datasets, transforms
import torch.optim as optim

import cv2
from tqdm import tqdm

from denoising_diffusion_pytorch import Unet, GaussianDiffusion