wehub-resource-sync
119 lines
6.2 KiB
Markdown
119 lines
6.2 KiB
Markdown
---
|
|
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 © 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>
|
|
|
|
|
|
---
|
|
<ul class="list-style-none">
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://discord.gg/MhZn5Nc39h"><span><i class="fa-brands fa-discord"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://www.youtube.com/@llmware"><span><i class="fa-brands fa-youtube"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://huggingface.co/llmware"><span> <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" alt="Hugging Face" class="hugging-face-logo"/> </span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://www.linkedin.com/company/aibloks/"><span><i class="fa-brands fa-linkedin"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://twitter.com/AiBloks"><span><i class="fa-brands fa-square-x-twitter"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://www.instagram.com/aibloks/"><span><i class="fa-brands fa-instagram"></i></span></a>
|
|
</li>
|
|
</ul>
|
|
---
|
|
|