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Run your own AI (but private) 121gamers 1 Views • 1 year ago

Run your own AI with VMware: https://ntck.co/vmware

Unlock the power of Private AI on your own device with NetworkChuck! Discover how to easily set up your own AI model, similar to ChatGPT, but entirely offline and private, right on your computer. Learn how this technology can revolutionize your job, enhance privacy, and even survive a zombie apocalypse. Plus, dive into the world of fine-tuning AI with VMware and Nvidia, making it possible to tailor AI to your specific needs. Whether you're a tech enthusiast or a professional looking to leverage AI in your work, this video is packed with insights and practical steps to harness the future of technology.

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VIDEO STUFF
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Ollama: https://ollama.com/
PrivateGPT: https://docs.privategpt.dev/ov....erview/welcome/intro
PrivateGPT on WSL2 with GPU: https://medium.com/@docteur_rs..../installing-privateg

**Sponsored by VMWare by Broadcom

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- Setting up Private AI on your computer
- Offline AI models like ChatGPT
- Enhancing job performance with Private AI
- VMware and Nvidia AI solutions
- Fine-tuning AI models for specific needs
- Running AI without internet
- Privacy concerns with AI technologies
- Surviving a zombie apocalypse with AI
- VMware Private AI Foundation
- Nvidia AI enterprise tools
- Connecting knowledge bases to Private GPT
- Retrieval Augmented Generation (RAG) with AI
- Installing WSL for AI projects
- Running LLMs on personal devices
- VMware deep learning VMs
- Customizing AI with VMware and Nvidia
- Private GPT project setup
- Leveraging GPUs for AI processing
- Consulting databases with AI for accurate answers
- VMware's role in private AI development
- Intel and IBM partnerships with VMware for AI
- Running local private AI in companies
- NetworkChuck's guide to private AI
- Future of technology with private and fine-tuned AI

**00:00** - Introduction to Private AI and Setup Guide
**00:56** - VMware's Role in Private AI
**01:50** - Understanding AI Models and Exploring Hugging Face
**02:54** - Training and Power of AI Models
**04:24** - Installing Ollama for Local AI Models
**05:24** - Setting Up Windows Subsystem for Linux (WSL) for AI
**06:53** - Running Your First Local AI Model
**07:23** - Enhancing AI with GPUs for Faster Responses
**08:02** - Fun with AI: Zombie Apocalypse Survival Tips
**08:28** - Switching AI Models for Different Responses
**09:04** - Fine-Tuning AI with Your Own Data
**10:50** - VMware's Approach to Fine-Tuning AI Models
**12:53** - The Data Scientist's Workflow with VMware and NVIDIA
**15:23** - VMware's Partnerships for Diverse AI Solutions
**16:26** - Setting Up Your Own Private GPT with RAG
**18:08** - Bonus: Running Private GPT with Your Knowledge Base
**20:55** - The Future of Private AI and VMware's Solution
**21:28** - Quiz Announcement for Viewers

#vmware #privategpt #AI

How to Build an LLM from Scratch | An Overview 121gamers 1 Views • 1 year ago

Want to learn more? I’m launching a 6-week live BootCamp for AI Builders.
👉 Learn more: https://maven.com/s/course/13437a45a7
Save 50% at checkout with the code FOUNDER50

This is the 6th video in a series on using large language models (LLMs) in practice. Here, I review key aspects of developing a foundation LLM based on the development of models such as GPT-3, Llama, Falcon, and beyond.

More Resources:
▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos Read more: https://towardsdatascience.com..../how-to-build-an-llm

[1] BloombergGPT: https://arxiv.org/pdf/2303.17564.pdf
[2] Llama 2: https://ai.meta.com/research/p....ublications/llama-2-
[3] LLM Energy Costs: https://www.statista.com/stati....stics/1384401/energy
[4] arXiv:2005.14165 [cs.CL]
[5] Falcon 180b Blog: https://huggingface.co/blog/falcon-180b
[6] arXiv:2101.00027 [cs.CL]
[7] Alpaca Repo: https://github.com/gururise/AlpacaDataCleaned
[8] arXiv:2303.18223 [cs.CL]
[9] arXiv:2112.11446 [cs.CL]
[10] arXiv:1508.07909 [cs.CL]
[11] SentencePience: https://github.com/google/sent....encepiece/tree/maste
[12] Tokenizers Doc: https://huggingface.co/docs/tokenizers/quicktour
[13] arXiv:1706.03762 [cs.CL]
[14] Andrej Karpathy Lecture: https://www.youtube.com/watch?v=kCc8FmEb1nY&t=5307s
[15] Hugging Face NLP Course: https://huggingface.co/learn/n....lp-course/chapter1/7
[16] arXiv:1810.04805 [cs.CL]
[17] arXiv:1910.13461 [cs.CL]
[18] arXiv:1603.05027 [cs.CV]
[19] arXiv:1607.06450 [stat.ML]
[20] arXiv:1803.02155 [cs.CL]
[21] arXiv:2203.15556 [cs.CL]
[22] Trained with Mixed Precision Nvidia: https://docs.nvidia.com/deeple....arning/performance/m
[23] DeepSpeed Doc: https://www.deepspeed.ai/training/
[24] https://paperswithcode.com/method/weight-decay
[25] https://towardsdatascience.com..../what-is-gradient-cl
[26] arXiv:2001.08361 [cs.LG]
[27] arXiv:1803.05457 [cs.AI]
[28] arXiv:1905.07830 [cs.CL]
[29] arXiv:2009.03300 [cs.CY]
[30] arXiv:2109.07958 [cs.CL]
[31] https://huggingface.co/blog/ev....aluating-mmlu-leader
[32] https://www.cs.toronto.edu/~hi....nton/absps/JMLRdropo

--
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Intro - 0:00
How much does it cost? - 1:30
4 Key Steps - 3:55
Step 1: Data Curation - 4:19
1.1: Data Sources - 5:31
1.2: Data Diversity - 7:45
1.3: Data Preparation - 9:06
Step 2: Model Architecture (Transformers) - 13:17
2.1: 3 Types of Transformers - 15:13
2.2: Other Design Choices - 18:27
2.3: How big do I make it? - 22:45
Step 3: Training at Scale - 24:20
3.1: Training Stability - 26:52
3.2: Hyperparameters - 28:06
Step 4: Evaluation - 29:14
4.1: Multiple-choice Tasks - 30:22
4.2: Open-ended Tasks - 32:59
What's next? - 34:31