Sathish Gangichetty datasciencemonkey

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datasciencemonkey / dspy-happiness.py

Last active July 12, 2024 13:47

dspy module for an LLM Pipeline

	import random
	from utils import get_country_data_and_corr
	class WriteEssayAfterParsingUserQuery(dspy.Module):
	def __init__(self):
	super().__init__()
	self.country = dspy.Predict(ExtractCountry)
	self.essay_writer = dspy.ChainOfThought(EssayOnePass)

	def forward(self, question, constraint, persona):
	with dspy.settings.context(lm=qwen2): # Call QWEN2 for extracting country name

datasciencemonkey / qlora_gptq.py

Created October 22, 2023 23:45

Start with qlora training and then quantize to disk with gptq

	# %%
	from huggingface_hub import login
	from dotenv import load_dotenv

	load_dotenv("/notebooks/.env")
	import os

	os.environ["TOKENIZERS_PARALLELISM"] = "false"

	login(token=os.getenv("HUGGINGFACE_TOKEN"))

datasciencemonkey / gptq_lora.py

Created September 13, 2023 21:57

train a gptq model using peft/lora

	# %%
	# this is run from /notebooks on paperspace
	from huggingface_hub import login
	from dotenv import load_dotenv

	load_dotenv("/notebooks/.env")
	import os
	os.environ["TOKENIZERS_PARALLELISM"]="false"
	login(token=os.getenv("HUGGINGFACE_TOKEN"))

datasciencemonkey / text_similarity.py

Created January 17, 2022 22:09

compute document similarity

	from flair.embeddings import (
	FlairEmbeddings,
	TransformerWordEmbeddings,
	StackedEmbeddings,
	)
	from flair.data import Sentence
	from flair.embeddings import DocumentPoolEmbeddings
	from sklearn.metrics.pairwise import cosine_similarity

	# init Flair embeddings

datasciencemonkey / src.py

Last active March 18, 2021 15:51

open src node run py scripts and make results globally available - CAS/Viya/Model Studio

	# import libraries
	import os
	import swat
	import pandas as pd
	import toml

	with open('/tmp/config.toml','r') as f:
	server_config = toml.load(f)

	cas_settings = server_config.get('cas35')

datasciencemonkey / carousel_slider.dart

Created October 30, 2020 02:40

carousel_slider

datasciencemonkey / network_helper.dart

Created September 30, 2020 03:14

networkHelper to fire requests off to ESP

	class NetworkHelper {
	NetworkHelper(this.url);

	final String url;
	final Xml2Json xml2json = new Xml2Json();

	Future getData() async {
	print(url);
	http.Response response =
	await http.get(url, headers: {'Accept': 'application/json'});

datasciencemonkey / update_results.dart

Created September 30, 2020 03:12

update app state for the TV app

	void updateUI(dynamic recommendationJSON) {
	setState(() {
	if (recommendationJSON == null) {
	recommendationText = '';
	return;
	}
	recommendationText =
	recommendationJSON['events'][1]['event']['Recommendation'];
	print(recommendationText);
	});

datasciencemonkey / recommendation_model.dart

Created September 30, 2020 03:08

Calling ESP from TV companion app

	import 'networking.dart';
	import '../constants/esp_settings.dart';

	const esp_rec_model_url =
	'$hostname:$http_port/SASESP/events/Recommendation_Engine/cq1/Final_Recommendation';

	class RecommendationModel {
	Future<dynamic> getRecommendation(String id) async {
	NetworkHelper networkHelper =
	NetworkHelper('$esp_rec_model_url?filter=in(cust_id,$id)');