Ashwin Mathur Email & Phone Number

• Work done for the Haystack LLM framework:
• Added the support for Llama.cpp to run LLMs.
• Added the support for HuggingFace TEI embedding models.
• Added the support for INSTRUCTOR embedding models.
• Created an integration for Pinecone Vector Database.
• Added the support for Diveristy Ranker for reranking tasks.

• Built a Open-Ended Question-Answering Pipeline on Earnings Call Transcripts using Generative Large Language Models (LLMs). The pipeline reduced the time and effort required for investors and analysts to extract.
• The pipeline used Retrieval Augmented Generation (RAG) to incorporate new information, allowing for easy attribution and minimal hallucination. The pipeline consists of an Embedding Model, Context Retriever, Prompt.
• Extracted text snippets from each section of the earnings call, chunked them dynamically based on similarity, and created embeddings. The embeddings were stored in a Pinecone vector database.
• Experimented with and selected the best SOTA embedding models (SBERT, MPNET, SGPT and INSTRUCTOR) and context retrieval strategies for the pipeline, resulting in significant improvements in accuracy and performance. A.
• Created different prompt templates for entity extraction and question-answering, leveraging weak supervision techniques to dynamically generate few-shot examples. Prompt templates were based on ideas from prompt.
• Carried out extensive prompt tuning by iteratively refining prompt formatting, instructions and incorporating few-shot examples.

• Developed API to enable Sequence Classification support for BioGPT.
• Added functionality to pre-train Transformer-based Vision models (ViT) without using the Trainer class.
• Created a section in the documentation showcasing the integration of HuggingFace Evaluate with the scikit-learn framework.View all contributions here: https://bit.ly/githftrf

• Created API to expose Out of Bag scores for Gradient Boosting based estimators.
• Implemented support for sparse matrices as input to Silhouette Score.
• Added multi-class support to Average Precision Score by computing it in a One-Vs-Rest manner for each class.
• Improved the description of Logistic Regression in the user guide to highlight that it is a regression model implemented as a classifier and a special case of GLM.
• Introduced a new section in the user guide explaining Bernoulli and Categorical Distributions in the context of Generalized Linear Models (GLM).View all contributions here: https://bit.ly/gitsklearn

Master Of Science - Msc, Data Science, 9.8 Cgpa (Ranked 1ˢᵗ In Batch)

Core coursework in: Linear Algebra and Mathematical Foundations of Data Science, Optimization, Machine Learning, Fuzzy Logic and Neural.

Bachelor Of Science - Bsc, Statistics, 9.64 Cgpa

Core Coursework in: Linear Algebra, Multivariable Calculus, Distribution Theory, Theory of Estimation, Hypothesis Testing, Regression.

10 Cgpa