---
layout: default
title: Learn
nav_order: 4
has_children: true
description: key learning resources
permalink: /learn
---
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](https://youtu.be/cViMonCAeSc?si=L6jX0sRdZAmKtRcz)  
- [Agent Automation with Web Services for Financial Research](https://youtu.be/l0jzsg1_Ik0?si=oBGtALHLplouY9x2)  
- [Voice Transcription and Automated Analysis of Greatest Speeches Dataset](https://youtu.be/5y0ez5ZBpPE?si=PAaCIqYou8nCGxYG)  
- [Are you prompting wrong for RAG - Stochastic Sampling-Part I](https://youtu.be/7oMTGhSKuNY?si=_KSjuBnqArvWzYbx)  
- [Are you prompting wrong for RAG - Stochastic Sampling-Part II- Code Experiments](https://youtu.be/iXp1tj-pPjM?si=3ZeMgipY0vJDHIMY)  

🎬 **Using Agents, Function Calls and SLIM models**  
- [SLIMS Playlist](https://youtube.com/playlist?list=PL1-dn33KwsmAHWCWK6YjZrzicQ2yR6W8T&si=TSFGqQ3ObOO5vDde)  
- [Agent-based Complex Research Analysis](https://youtu.be/y4WvwHqRR60?si=jX3KCrKcYkM95boe)  
- [Getting Started with SLIMs (with code)](https://youtu.be/aWZFrTDmMPc?si=lmo98_quo_2Hrq0C)  
- [SLIM Models Intro](https://www.youtube.com/watch?v=cQfdaTcmBpY)  
- [Text2SQL Intro](https://youtu.be/BKZ6kO2XxNo?si=tXGt63pvrp_rOlIP)  
- [Pop up LLMWare Inference Server](https://www.youtube.com/watch?v=qiEmLnSRDUA&t=20s)   
- [Hardest Problem in RAG - handling 'Not Found'](https://youtu.be/slDeF7bYuv0?si=j1nkdwdGr5sgvUtK)  
- [Extract Information from Earnings Releases](https://youtu.be/d6HFfyDk4YE?si=VmnIiWFmgBtR4DxS)   
- [Summary Function Calls](https://youtu.be/yNg_KH5cPSk?si=Yl94tp_vKA8e7eT7)  
- [Boolean Yes-No Function Calls](https://youtu.be/jZQZMMqAJXs?si=lU4YVI0H0tfc9k6e)  
- [Autogenerate Topics, Tags and NER](https://youtu.be/N6oOxuyDsC4?si=vo2Fd8VG5xTbH4SD)  

🎬 **Using GGUF Models**  
- [Using LM Studio Models](https://www.youtube.com/watch?v=h2FDjUyvsKE)  
- [Using Ollama Models](https://www.youtube.com/watch?v=qITahpVDuV0)  
- [Use any GGUF Model](https://www.youtube.com/watch?v=9wXJgld7Yow)  
- [Background on GGUF Quantization & DRAGON Model Example](https://www.youtube.com/watch?v=ZJyQIZNJ45E)  
- [Getting Started with Whisper.CPP](https://youtu.be/YG5u5AOU9MQ?si=5xQYZCILPSiR8n4s)  

🎬 **Core RAG Scenarios Running Locally**  
- [RAG with BLING on your laptop](https://www.youtube.com/watch?v=JjgqOZ2v5oU)     
- [DRAGON-7B-Models](https://www.youtube.com/watch?v=d_u7VaKu6Qk&t=37s)   
- [Use small LLMs for RAG for Contract Analysis (feat. LLMWare)](https://www.youtube.com/watch?v=8aV5p3tErP0)   
- [Invoice Processing with LLMware](https://www.youtube.com/watch?v=VHZSaBBG-Bo&t=10s)   
- [Evaluate LLMs for RAG with LLMWare](https://www.youtube.com/watch?v=s0KWqYg5Buk&t=105s)  
- [Fast Start to RAG with LLMWare Open Source Library](https://www.youtube.com/watch?v=0naqpH93eEU)  
- [Use Retrieval Augmented Generation (RAG) without a Database](https://www.youtube.com/watch?v=tAGz6yR14lw)  


🎬 **Parsing, Embedding, Data Pipelines and Extraction**  
- [Ingest PDFs at Scale](https://www.youtube.com/watch?v=O0adUfrrxi8&t=10s)  
- [Install and Compare Multiple Embeddings with Postgres and PGVector](https://www.youtube.com/watch?v=Bncvggy6m5Q)   
- [Intro to Parsing and Text Chunking](https://youtu.be/2xDefZ4oBOM?si=YZzBUjDfQ0839EVF)  


# More information about the project - [see main repository](https://www.github.com/llmware-ai/llmware.git)


# About the project

`llmware` is &copy; 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home).

## Contributing
Please first discuss any change you want to make publicly, for example on GitHub via raising an [issue](https://github.com/llmware-ai/llmware/issues) or starting a [new discussion](https://github.com/llmware-ai/llmware/discussions).
You can also write an email or start a discussion on our Discrod channel.
Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md).

## Code of conduct
We welcome everyone into the ``llmware`` community.
[View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository.

## ``llmware`` and [AI Bloks](https://www.aibloks.com/home)
``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``.
The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service.
[AI Bloks](https://www.aibloks.com/home) was founded by [Namee Oberst](https://www.linkedin.com/in/nameeoberst/) and [Darren Oberst](https://www.linkedin.com/in/darren-oberst-34a4b54/) in October 2022.

## License

`llmware` is distributed by an [Apache-2.0 license](https://www.github.com/llmware-ai/llmware/blob/main/LICENSE).

## Thank you to the contributors of ``llmware``!
<ul class="list-style-none">
{% for contributor in site.github.contributors %}
  <li class="d-inline-block mr-1">
     <a href="{{ contributor.html_url }}">
        <img src="{{ contributor.avatar_url }}" width="32" height="32" alt="{{ contributor.login }}">
    </a>
  </li>
{% endfor %}
</ul>


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