abhishekkrthakur

from diffusers import DiffusionPipeline, StableDiffusionXLImg2ImgPipeline
import torch

model = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = DiffusionPipeline.from_pretrained(
    model,
    torch_dtype=torch.float16,
)
pipe.to("cuda")
pipe.load_lora_weights("model/", weight_name="pytorch_lora_weights.safetensors")

abhishekkrthakur

import os

import gradio as gr
from text_generation import Client


PROMPT = """<s>[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.

abhishekkrthakur

import torch
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from trl import SFTTrainer


def train():
    train_dataset = load_dataset("tatsu-lab/alpaca", split="train")
    tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-base", trust_remote_code=True)

abhishekkrthakur

import argparse

from pdfminer.high_level import extract_text
from sentence_transformers import SentenceTransformer, CrossEncoder, util

from text_generation import Client

PREPROMPT = "Below are a series of dialogues between various people and an AI assistant. The AI tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn't entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn't let caution get too much in the way of being useful.\n"
PROMPT = """"Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to

abhishekkrthakur

import os
import requests
import json

SLACK_WEBHOOK= os.environ.get("SLACK_WEBHOOK")


def send_message(messages, channel="abhishek", username="beast"):
    """
    :param messages: list of texts

abhishekkrthakur

test_transform=transforms.Compose([
    transforms.Resize(IMAGE_SIZE),
    transforms.CenterCrop(IMAGE_SIZE),
    transforms.ToTensor(),
    transforms.Normalize(IMG_MEAN,IMG_STD)
])


test_dataset = CollectionsDatasetTest(csv_file='../input/sample_submission.csv',
                                  root_dir='../input/test/',

abhishekkrthakur

import torch.optim as optim
from torch.optim import lr_scheduler

plist = [
        {'params': model_ft.layer4.parameters(), 'lr': 1e-5},
        {'params': model_ft.last_linear.parameters(), 'lr': 5e-3}
        ]
optimizer_ft = optim.Adam(plist, lr=0.001)
lr_sch = lr_scheduler.StepLR(optimizer_ft, step_size=10, gamma=0.1)

abhishekkrthakur

import torch
from torchvision import transforms

# define some re-usable stuff
IMAGE_SIZE = 224
NUM_CLASSES = 1103
BATCH_SIZE = 32
device = torch.device("cuda:0")
IMG_MEAN = model_ft.mean
IMG_STD = model_ft.std

abhishekkrthakur

def train_model(model, 
                data_loader, 
                dataset_size, 
                optimizer, 
                scheduler, 
                num_epochs):
    criterion = nn.BCEWithLogitsLoss()
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

abhishekkrthakur

import torch.nn as nn
import pretrainedmodels as pm

model = pm.__dict__["resnet50"](pretrained='imagenet')

model.avg_pool = nn.AdaptiveAvgPool2d(1)
model.last_linear = nn.Sequential(
    nn.BatchNorm1d(2048),
    nn.Dropout(p=0.25),
    nn.Linear(in_features=2048, out_features=2048),