Sri Hari Karthick

ML enthusiast exploring real-world problems beyond toy examples. Implementing research papers in NLP and fine-tuning using PyTorch to bridge theory and practice through hands-on work.

Edinburgh, United Kingdom Joined on Jun 22, 2025 https://github.com/codebykarthick

Education

University of Edinburgh

Cover image for RAG Made Simple: Demonstration and Analysis of Simplicity (Part 3)

Sri Hari Karthick

Jun 22

RAG Made Simple: Demonstration and Analysis of Simplicity (Part 3)

#ai #nlp #rag #python

2 min read

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Cover image for RAG Made Simple: Technical Design and Architecture of Simplicity (Part 2)

Sri Hari Karthick

Jun 22

RAG Made Simple: Technical Design and Architecture of Simplicity (Part 2)

#ai #nlp #rag #python

3 min read

Cover image for RAG Made Simple: Simplicity’s Approach to Modular Retrieval & Generation (Part 1)

Sri Hari Karthick

Jun 22

RAG Made Simple: Simplicity’s Approach to Modular Retrieval & Generation (Part 1)

#ai #nlp #python #rag

3 min read

Cover image for Serving models over REST API using Lightning Serve API

Sri Hari Karthick

Jun 22

Serving models over REST API using Lightning Serve API

#ai #nlp #python #restapi

2 min read

Cover image for Fine-Tuning a Language Model for Summarisation using LoRA

Sri Hari Karthick

Jun 22

Fine-Tuning a Language Model for Summarisation using LoRA

#nlp #ai #python

5 min read

Cover image for Named Entity Recognition using Bidirectional LSTM and Conditional Random Fields

Sri Hari Karthick

Jun 22

Named Entity Recognition using Bidirectional LSTM and Conditional Random Fields

#lstm #nlp #ai

5 min read

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Sri Hari Karthick

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RAG Made Simple: Demonstration and Analysis of Simplicity (Part 3)

Want to connect with Sri Hari Karthick?

RAG Made Simple: Technical Design and Architecture of Simplicity (Part 2)

RAG Made Simple: Simplicity’s Approach to Modular Retrieval & Generation (Part 1)

Serving models over REST API using Lightning Serve API

Fine-Tuning a Language Model for Summarisation using LoRA

Named Entity Recognition using Bidirectional LSTM and Conditional Random Fields