course22p2
LLMsPracticalGuide
| course22p2 | LLMsPracticalGuide | |
|---|---|---|
| 6 | 11 | |
| 431 | 8,561 | |
| 2.6% | - | |
| 0.0 | 4.5 | |
| 11 days ago | 16 days ago | |
| Jupyter Notebook | ||
| Apache License 2.0 | - |
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course22p2
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Ask HN: Daily practices for building AI/ML skills?
Practical Deep Learning for Coders: https://course.fast.ai/Lessons/part2.html
- Stanford A.I. Courses
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A quick visual guide to what's actually happening when you generate an image with Stable Diffusion
To me the most important bit is that the diffusion loop turns a noisy latent into an image, does that iteratively, and uses "guidance" in the form of a prompt/controlnet image/etc to do it. The scheduler part, I felt, was needlessly complex for this short explainer, so I hand-wave it away. IF someone wants to dive in deeper, much deeper, they can go through the same thing I'm doing, which is this: https://course.fast.ai/Lessons/part2.html
- Practical Deep Learning for Coders - Part 2 overview
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Courses for an AI beginner
They also recently released a course for more experienced students where they teach you to implement the Stable Diffusion algorithm from scratch.
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From Deep Learning Foundations to Stable Diffusion (Part 2)
The full transcripts are available here in plain text form:
https://github.com/fastai/course22p2/tree/master/summaries
LLMsPracticalGuide
- Ask HN: Daily practices for building AI/ML skills?
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XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens
Here are some high level answers:
"7B" refers to the number of parameters or weights for a model. For a specific model, the versions with more parameters take more compute power to train and perform better.
A foundational model is the part of a ML model that is "pretrained" on a massive data set (and usually is the bulk of the compute cost). This is usually considered the "raw" model after which it is fine-tuned for specific tasks (turned into a chatbot).
"8K length" refers to the Context Window length (in tokens). This is basically an LLM's short term memory - you can think of it as its attention span and what it can generate reasonable output for.
"1.5T tokens" refers to the size of the corpus of the training set.
In general Wikipedia (or I suppose ChatGPT 4/Bing Chat with Web Browsing) is a decent enough place to start reading/asking basic questions. I'd recommend starting here: https://en.wikipedia.org/wiki/Large_language_model and finding the related concepts.
For those going deeper, there are lot of general resources lists like https://github.com/Hannibal046/Awesome-LLM or https://github.com/Mooler0410/LLMsPracticalGuide or one I like, https://sebastianraschka.com/blog/2023/llm-reading-list.html (there are a bajillion of these and you'll find more once you get a grasp on the terms you want to surf for). Almost everything is published on arXiv, and most is fairly readable even as a layman.
For non-ML programmers looking to get up to speed, I feel like Karpathy's Zero to Hero/nanoGPT or Jay Mody's picoGPT https://jaykmody.com/blog/gpt-from-scratch/ are alternative/maybe a better way to understand the basic concepts on a practical level.
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Need help finding local LLM
checked e.g.: - https://medium.com/geekculture/list-of-open-sourced-fine-tuned-large-language-models-llm-8d95a2e0dc76 - https://github.com/Mooler0410/LLMsPracticalGuide - https://www.reddit.com/r/LocalLLaMA/comments/12r552r/creating_an_ai_agent_with_vicuna_7b_and_langchain/ - https://www.youtube.com/watch?v=9ISVjh8mdlA
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1-Jun-2023
The Practical Guides for Large Language Models (https://github.com/Mooler0410/LLMsPracticalGuide)
- [D] LLM Evolutionare Tree from "The Practical Guides for Large Language Models"
- Comprehensive Table of LLM Usage Restrictions
- Check out this Comprehensive and Practical Guide for Practitioners Working with Large Language Models
- The Practical Guides for Large Language Models
- Practical Guide for LLMs
What are some alternatives?
developer - the first library to let you embed a developer agent in your own app!
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.
simpleaichat - Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
Awesome-LLM - Awesome-LLM: a curated list of Large Language Model
playground - Play with neural networks!
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
latentblending - Create butter-smooth transitions between prompts, powered by stable diffusion
chatdocs - Chat with your documents offline using AI.
machine-learning-specialization-andrew-ng - A collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
stylegan2-projecting-images - Projecting images to latent space with StyleGAN2.
open_llama - OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset