Learn: Youtube Video Series

Tutorial Videos - check out our Youtube channel for high-impact 5-10 minute tutorials on the latest examples.

Check back often as this list is always growing …

🎬 Some of our most recent videos

• Best Small RAG Model - Bling-Phi-3
• Agent Automation with Web Services for Financial Research
• Voice Transcription and Automated Analysis of Greatest Speeches Dataset
• Are you prompting wrong for RAG - Stochastic Sampling-Part I
• Are you prompting wrong for RAG - Stochastic Sampling-Part II- Code Experiments

🎬 Using Agents, Function Calls and SLIM models

• SLIMS Playlist
• Agent-based Complex Research Analysis
• Getting Started with SLIMs (with code)
• SLIM Models Intro
• Text2SQL Intro
• Pop up LLMWare Inference Server
• Hardest Problem in RAG - handling ‘Not Found’
• Extract Information from Earnings Releases
• Summary Function Calls
• Boolean Yes-No Function Calls
• Autogenerate Topics, Tags and NER

🎬 Using GGUF Models

• Using LM Studio Models
• Using Ollama Models
• Use any GGUF Model
• Background on GGUF Quantization & DRAGON Model Example
• Getting Started with Whisper.CPP

🎬 Core RAG Scenarios Running Locally

• RAG with BLING on your laptop
• DRAGON-7B-Models
• Use small LLMs for RAG for Contract Analysis (feat. LLMWare)
• Invoice Processing with LLMware
• Evaluate LLMs for RAG with LLMWare
• Fast Start to RAG with LLMWare Open Source Library
• Use Retrieval Augmented Generation (RAG) without a Database

🎬 Parsing, Embedding, Data Pipelines and Extraction

• Ingest PDFs at Scale
• Install and Compare Multiple Embeddings with Postgres and PGVector
• Intro to Parsing and Text Chunking

More information about the project - see main repository

About the project

llmware is © 2023-2024 by AI Bloks.

Contributing

Please first discuss any change you want to make publicly, for example on GitHub via raising an issue or starting a new discussion. You can also write an email or start a discussion on our Discrod channel. Read more about becoming a contributor in the GitHub repo.

Code of conduct

We welcome everyone into the llmware community. View our Code of Conduct in our GitHub repository.

llmware and AI Bloks

llmware is an open source project from AI Bloks - the company behind llmware. The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service. AI Bloks was founded by Namee Oberst and Darren Oberst in October 2022.

License

llmware is distributed by an Apache-2.0 license.

Thank you to the contributors of llmware!

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Table of contents

• Using Agents & Function Calls with SLIM Models
• Core RAG Scenarios Running Locally
• Using Quantized GGUF Models
• Advanced RAG
• Parsing Embedding and Data Extraction
• Voice Transcription with Whisper CPP
• Other Topics