#Len Function In Programming

Chatbot for FAQs Fine-tune a pretrained LLM to answer domain-specific questions (e.g., product FAQs). Tech Stack: Python, HuggingFace Transformers, PyTorch, Datasets LegalDoc Assistant Fine-tune GPT/LLaMA on legal text to summarize contracts or answer legal queries. Tech Stack: HuggingFace, PyTorch, LangChain, PDF parsing libraries Code Completion Model Fine-tune CodeLlama or CodeT5 on a repo of code for auto-completion and suggestions. Tech Stack: HuggingFace, PyTorch, Tokenizers, GitHub API Emotion-Aware Chatbot Fine-tune an LLM to recognize emotions in messages and respond empathetically. Tech Stack: PyTorch, HuggingFace, GoEmotions Dataset, PEFT (LoRA/Adapters) Summarization Model Fine-tune BART or T5 to summarize articles, meeting notes, or emails. Tech Stack: HuggingFace, PyTorch Lightning, Datasets Customer Review Analyzer Fine-tune a small LLM on product reviews to generate insights, sentiment, or suggestions. Tech Stack: Transformers, PyTorch, Pandas, Sklearn Domain-Specific RAG Model Fine-tune an LLM to retrieve and answer questions from your company’s knowledge base. Tech Stack: LangChain, ChromaDB/FAISS, HuggingFace, PyTorch TinyGPT for Chat Fine-tune a small GPT model on your own chat logs for personal assistants. Tech Stack: PyTorch, HuggingFace, Tokenizers, WandB #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #ai #llm #largelanguagemodels

It’s Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They’re complex because of configuration and scale. Today I broke down the GPT-2 config that defines how the model thinks, remembers, and attends. GPT-2 is just a set of numbers that define scale: vocab size, context length, embedding dimension, layers, and attention heads. Breaking down the GPT-2 (124M) configuration: 50,257-token vocabulary, 1,024-token context, 768-dimensional embeddings, 12 transformer layers with 12 attention heads, dropout 0.1, and bias-free QKV projections. Understanding these parameters is key to scaling LLMs efficiently. #deeplearning #generativeai #womenwhocode #largelanguagemodels

Learning Linux can be overwhelming 🥹 Website : https://linuxjourney.com Interactive tutorials with Leetcode like topicwise problems and solutions 🔥 #coding #technology #tech #android #engineering #webdesign #code #website #web #development #computer #programming #coding #python #developer #java #software #stem #webdevelopment #webdesigner #webdeveloper #linux

Lambda Function Explained in Python👇 A lambda function in Python is a small anonymous function used for short, simple operations. It is written in a single line and does not use the def keyword. Syntax: lambda arguments: expression #python3 #pycode #pythonlearning #pythonhub

Python beginners, this guide will save you hours of confusion! #python #coding #code #errormakesclever

Day 1 of Learning LLM :- What is LLM? . . . . . #ai #ml #llm #tech #aiengineering

Various Activation Functions used in Neural Networks #machinelearning #artificialintelligence #mathematics #computerscience #programming

when it comes to understanding an ML algo start with linear regression… it’s the easiest to set foundations which algo should i break down next?? #techcareer #ai #machinelearning #datascientist #machinelearningengineer #coding

Ye Q favourite hai Interviewers ka… #leetcode #leetcodesolution

Why use a lambda function in Python? #animecoders

What if the lb itself goes down??? #systemdesigninterview #coding #code #ai #loadbalancer

High-Level vs Low-Level Languages #programminglanguage #coding #codingtips #technology