Rachitt Shah

Webscraping-with-AutoGPT. This repo is entirely built via AutoGPT

DSpy-KGs. LLM-driven automated knowledge graph construction from text using DSPy and Neo4j

first-contribution-2020. HTML

Investment-memo-LLM. Generate VC-grade investment memos using LLMs.

fine-tune-generator. A fine-tuning library leveraging distilled GPT-4 outputs to fine-tune to GPT-3.5 for better accuracy.

VC-modelling. Python notebooks for captable constructions, valuations and EBITDA margins

RFP-QA. A bot performing QA via LLMs on RFP docs.

fine-tune-generator-streamlit. An attempt to fine-tune GPT-3.5 via uploading your PDF and csv files.

open-source-list. HTML

qual-llm. LLM agents to derive market research insights from qualitative and quantitative data.

prompt-eval-learnings. Jupyter Notebook

CoV-langchain. Chain of Verification implementation in Langchain

pdf-to-csv. Python

fine-tuning-experiments. Some finetuning experiments, from SLM(1-3B LLMs) to larger LLMs. The focus of these finetunes is to build better eval models.

GPT-6J-Demo. This notebook demonstrates how to run the GPT-J-6B model. See the link for more details about the model, including evaluation metrics and credits.

webscraping_langchain. langchain+webscraping for downstream tasks.

competitive-programming. All my competitive-programming theory codes and solutions. Refer CONTRIBUTING.md for contributions

reasoning_evals. evals for reasoning models such as o3-mini-high, o1, deepseek-r1

prompttools. Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate).

rachittshah.. CSS

gpt-train. Training and implementation of Generative Pre-trained Transformers

awesome-machine-learning. A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-competitive-programming. :gem: A curated list of awesome Competitive Programming, Algorithm and Data Structure resources

Competitive-Programming-questions. This repo is open for all. Add your favourite competitive programming questions along with the solution.