LLM Resources: Organized by Topic

The first part is all about parameter-efficient fine-tuning (PEFT)and model adaptation. Adapters and LoRA are both parameter-specific fine-tuning methods.

Fine-Tuning & Model Adaptation

Foundational Papers:

• Parameter-Efficient Transfer Learning for NLP
• LoRA: Low-Rank Adaptation of Large Language Models

Implementation Tools:

• HuggingFace PEFT (Parameter-Efficient Fine-Tuning)

Prompting Techniques

Research Papers:

• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Large Language Models are Zero-Shot Reasoners

Guides & Resources:

• Prompt Engineering Guide

Document Processing & Text Preparation

Document Parsing:

• Dedoc: Universal PDF/document extractor
• Layout-Parser: Document layout analysis

Text Chunking:

• Langchain Text Splitters

Embeddings & Vector Representations

Foundational Research:

• Efficient Estimation of Word Representations in Vector Space (Word2Vec)

Embedding APIs & Models:

• OpenAI’s Embeddings Guide
• The Best Embedding Models for Information Retrieval in 2025

Multimodal Embeddings:

• OpenAI’s CLIP: Connecting text and images

Vector Search & Retrieval

Vector Databases & Search:

• Faiss: Library for efficient similarity search

RAG Techniques:

• RAFT: Retrieval Augmented Fine-Tuning

See Also

• LLM Foundations