Mahesh Rajput mrmaheshrajput

Machine Learning Engineer | Recommender Systems and NLP

mrmaheshrajput / Customer Support Assistant.py

Last active August 15, 2024 11:32

	from datetime import datetime
	import json
	from typing import Any, Dict, List

	import boto3
	from botocore.exceptions import ClientError

	# Initialize a Boto3 session and create a Bedrock runtime client
	session = boto3.Session()
	region = "us-east-1" # us-west-2 has better runtime quota

mrmaheshrajput / SageMaker endpoint with LMI vLLM with comments.py

Created July 11, 2024 03:54

	import io
	import sagemaker
	import boto3
	import json


	# Your IAM role that provides access to SageMaker and S3.
	# See https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex-role.html
	# if running on a SageMaker notebook or directly use
	# sagemaker.get_execution_role() if running on SageMaker studio

mrmaheshrajput / SageMaker endpoint with LMI vLLM.py

Created July 10, 2024 04:16

	import io
	import sagemaker
	import boto3
	import json

	# Change this to your role
	iam_role = "arn:aws:iam::1111111111:role/service-role/AmazonSageMaker-ExecutionRole-00000000T000000"
	sagemaker_session = sagemaker.session.Session()
	region = sess._region_name
	smr_client = boto3.client("sagemaker-runtime")

mrmaheshrajput / SentenceTransformers SageMaker.py

Created May 12, 2024 15:15

	from sagemaker.huggingface.model import HuggingFaceModel


	ENDPOINT_NAME = "sbert-embeddings-minilml6" # Change this as desired

	role = "" # SageMaker execution role ARN


	hub = {
	"HF_MODEL_ID": "sentence-transformers/all-MiniLM-L6-v2", # Change to your model

mrmaheshrajput / cpu-sentence-transformers.sh

Last active April 13, 2024 05:16

	echo("Creating layer/python directory")
	mkdir -p layer/python

	echo("Installing cpu only pytorch")
	pip install \
	--target layer/python torch torchvision torchaudio \
	--extra-index-url https://download.pytorch.org/whl/cpu

	echo("Installing sentence transformer dependencies")
	pip install --target layer/python \

mrmaheshrajput / SageMaker jumpstart model deployment.py

Created March 27, 2024 04:22

	!pip install sagemaker -q

	from sagemaker.jumpstart.model import JumpStartModel

	model_id, model_version, = (
	"huggingface-llm-falcon-7b-instruct-bf16",
	"*",
	)

	my_model = JumpStartModel(model_id=model_id)

mrmaheshrajput / Sample SageMaker pipeline.py

Created March 27, 2024 03:58

	!pip install -U sagemaker -q

	import boto3
	import sagemaker
	import sagemaker.session

	session = sagemaker.session.Session()
	region = session.boto_region_name
	role = sagemaker.get_execution_role()
	bucket = session.default_bucket()

mrmaheshrajput / Model graded test.py

Created March 26, 2024 15:24

	delimiter = "####"

	eval_system_prompt = f"""You are an assistant that evaluates \
	whether or not an assistant is producing valid quizzes.
	The assistant should be producing output in the \
	format of Question N:{delimiter} <question N>?"""

	llm_response = """
	Question 1:#### What is the largest telescope in space called and what material is its mirror made of?

mrmaheshrajput / Model graded evaluation.py

Created March 26, 2024 15:18

	from langchain.prompts import ChatPromptTemplate
	from langchain.chat_models import ChatOpenAI
	from langchain.schema.output_parser import StrOutputParser


	chat_prompt = ChatPromptTemplate.from_messages([("human", prompt_template)])
	llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
	output_parser = StrOutputParser()

	chain = chat_prompt \| llm \| output_parser

mrmaheshrajput / ROUGE.py

Created March 26, 2024 14:28

	import evaluate
	from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
	from datasets import load_dataset

	# Load the fine-tuned model and tokenizer
	model_name = "your-fine-tuned-model-name"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

	# Load the test dataset