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paper

  • AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
  • Automatic Chain of Thought Prompting in Large Language Models
  • Benchmarking and Improving Text-to-SQL Generation under Ambiguity
  • Bridging Language and Data - Optimizing Text-to-SQL Generation in Large Language Models
  • C3: Zero-shot Text-to-SQL with ChatGPT
  • Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies
  • DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
  • DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
  • Generative Agents: Interactive Simulacra of Human Behavior
  • Judging llm-as-a-judge with mt-bench and chatbot arena
  • LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
  • LoRA: Low-Rank Adaptation of Large Language Models
  • QLoRA: Efficient Finetuning of Quantized LLMs
  • ReAct: Synergizing Reasoning and Acting in Language Models
  • ReFT: Representation Finetuning for Language Models
  • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
  • Retrieval-Augmented Generation for Large Language Models - A Survey
  • Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain
  • Text-to-SQL Empowered by Large Language Models - A Benchmark Evaluation
  • Towards Robustness of Text-to-SQL Models against Synonym Substitution
  • Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
  • Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
  • VeRA: Vector-based Random Matrix Adaptation

divers

  • AI-Powered Information Extraction and Matchmaking (link)
  • The 6 Best LLM Tools To Run Models Locally (link)
  • EvalPlus - a rigorous evaluation framework for LLM4Code (link)
  • Tiktokenizer (link)
  • Overview of Large Language Models (link)
  • Calculate : How much GPU Memory you need to serve any LLM ? (link)
  • A Tutorial on LLM (link)
  • Think Big LLM Models Can’t Fit Small GPUs? Think Again! (link)
  • unsloth (link)
  • Building an Agent for Data Visualization (Plotly) (link)
  • 6 Real-World Uses of Microsoft’s Newest Phi-3 Vision-Language Model (link)
  • What is Prompt Management for LLM Applications? (link)
  • Generative AI Agents Developer Contest by NVIDIA and LangChain (link)
  • How to fine-tune LLMs on custom datasets at Scale using Qwak and CometML (link)
  • LLM Fine Tuning Series - In Context Learning (link)
  • Large Language Model Course (link)
  • GPT-4o vs. GPT-4 vs. Gemini 1.5 ⭐ — Performance Analysis (link)
  • Tabular Data, RAG, & LLMs: Improve Results Through Data Table Prompting (link)
  • List of Different Ways to Run LLMs Locally (link)
  • Why Vector Search Didn’t Work for Your RAG Solution? (link)
  • 100x Faster — Scaling Your RAG App for Billions of Embeddings (link)
  • The 4 Advanced RAG Algorithms You Must Know to Implement (link)
  • Using DuckDB + Ibis for RAG (link)
  • DuckDB-NSQL: How to Quack in SQL (link)
  • Fine-Tuning Mistral 7B for Named Entity Recognition (NER) (link)
  • A Very Gentle Introduction to Large Language Models without the Hype (link)
  • RAG Vs VectorDB (link)
  • Advanced RAG Techniques: an Illustrated Overview (link)
  • Integrating Vector Databases with LLMs: A Hands-On Guide (link)
  • Core RAG Architecture with AlloyDB AI (link)
  • Forget RAG: Embrace agent design for a more intelligent grounded ChatGPT! (link)
  • Exploring Data Modelling with ChatGPT (link)
  • What Are ChatGPT Plugins? The Next Phase of Conversational AI Is Here (link)
  • Anomaly Detection in Time Series using ChatGPT (link)

dspy

  • DSPy (link)
  • DSPy - git (link)
  • Prompt Like a Pro Using DSPy: A guide to build a better local RAG model using DSPy, Qdrant and Ollama (link)

finetuning

  • LoRA: Low-Rank Adaptation of Large Language Models (link)
  • State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods (link)

llama

  • Building LLaMA 3 From Scratch with Python (link)
  • Crazy Challenge: Run Llama 405B on a 8GB VRAM GPU (link)
  • How to Run Llama 3.1 405B on Home Devices? Build AI Cluster! (link)
  • Fine-Tuning CodeLlama for Advanced Text-to-SQL Queries with PEFT and Accelerate (link)
  • The Llama 3 Herd of Models (link)

llamaindex

langchain

nvidia

  • Nemotron-4 15B: NVIDIA’s Powerful New Language Model (link)

ollama

  • git (link)
  • API (link)
  • docker (link)
  • Self Hosting LLMs using Ollama (link)
  • Ollama: The Ultimate Tool for Running Language Models Locally (link)
  • Unlock Any Open-Source LLM with Ollama (link)
  • Unlocking the Power of Structured Outputs with Ollama (link)
  • Your Machine, Your AI — The Ultimate Local Productivity Stack with Ollama (link)
  • Building an Advanced RAG Pipeline Using Docling, Groq, Ollama with GLIDER Evaluation (link)

phi3

  • Bridging the Gap: Fine-Tuning Phi-3 for SQL Query Generation with Natural Language Queries (link)
  • Exploring the Microsoft Phi3 Vision Language model as OCR for document data extraction (link) loring the Microsoft Phi3 Vision Language model as OCR for document data extraction-part 2 (link)
  • Exploring the Microsoft Phi3 Vision Language model as OCR for document data extraction (link)

rag

  • A Deep Dive into Retrieval-Augmented Generation (RAG) with HyDE (link)

  • Build an Agentic RAG using HuggingFace Transformers Agent (link)
  • Using RAG Architecture to query databases, export to Google Sheets, and visualize in Looker Studio. (link)
  • How to Build a Generative Search Engine for Your Local Files Using Llama 3 (link)
  • Building Vector Databases with FastAPI and ChromaDB (link)
  • Adding Context to Retrieval-Augmented Generation with Gemini Function Calling and MongoDB Atlas (link)
  • A Complete Guide to RAG and LlamaIndex (link)
  • Build RAG Application Using a LLM Running on Local Computer with Ollama and Langchain (link)
  • Advance RAG- Improve RAG performance (link)
  • RAG Detective: Retrieval Augmented Generation with website data (link)
  • Implementing Agentic RAG using Langchain (link)
  • RAG on Complex PDF using LlamaParse, Langchain and Groq (link)
  • Building an Observable arXiv RAG Chatbot with LangChain, Chainlit, and Literal AI (link)
  • Local RAG From Scratch (link)
  • LangChain SQL Agent for Massive Documents Interaction (link)
  • Implementing RAG architecture using Llama 2, Vector Store and LangChain (link)
  • How to Chat With Your Data Using OpenAI, Pinecone, Airbyte and Langchain: A Guide (link)
  • Neo4j x LangChain: Deep dive into the new Vector index implementation (link)
  • Explore OpenAI vector embedding with Neo4j, LangChain, and Wikipedia (link)
  • Build your own RAG with Mistral-7B and LangChain (link)
  • PG Phriday: Papa’s Got a Brand New RAG (link)
  • Build an Advanced Reranking-RAG System Using Llama-Index, Llama 3 and Qdrant (link)
  • Improved RAG with Llama3 and Ollama (link)
  • Build End-to-End RAG Pipeline with Monitoring and Evaluation using Langchain, Azure AI Search, OpenAI, Langfuse, Nemo-gaurdrails, ragas (link)
  • Detecting fraud in real time using Redpanda and Pinecone (link)
  • Analyze Structured Data (extracted from Unstructured Text) using LLM Agents (link)
  • Transforming Text Classification with Semantic Search Techniques — Faiss (link)
  • Extract Structured Data from Unstructured Text using LLMs (link)
  • Code Generation using Retrieval Augmented Generation + LangChain (link)
  • Open-DocLLM (link)
  • Geospatial Vector Search: Building an AI-Powered Geo-Aware News Search (link)
  • AI-Enabled Search Engine using LLM Embeddings, Django, and pgvector (link)
  • Multimodal RAG pipeline with LlamaIndex and Neo4j (link)

rag pgvector

  • How To Improve Your LLM Accuracy and Performance With PGVector and PostgreSQL: Introduction to Embeddings and the Role of PGVector (link)
  • SQL queries + pgvector: Retrieval Augmented Generation in PostgreSQL (link)
  • PostgreSQL as Vector database: Create LLM Apps with pgvector (link)
  • Simplifying RAG with PostgreSQL and PGVector (link)
  • Build a question-answer bot natively using Postgres extensions (link)
  • Use pgvector and Hugging Face to Build an Optimized FAQ Search with Sentence Similarity (link)