mlops-course
LLMsPracticalGuide
| mlops-course | LLMsPracticalGuide | |
|---|---|---|
| 20 | 11 | |
| 2,741 | 8,561 | |
| - | - | |
| 2.1 | 4.5 | |
| 9 months ago | 13 days ago | |
| Jupyter Notebook | ||
| MIT License | - |
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mlops-course
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Ask HN: Daily practices for building AI/ML skills?
coming from a similar context, i believe going top down might be the way to go.
up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.
i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)
i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.
- [Q] Any good resources for MLOps?
- Open-Source Machine Learning for Software Engineers Course
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Open-source MLOps Fundamentals Course 🚀
Find all the lessons here → https://madewithml.com/MLOps course repo → https://github.com/GokuMohandas/mlops-courseMade With ML repo → https://github.com/GokuMohandas/Made-With-ML
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What are examples of well-organized data science project that I can see on Github?
- https://github.com/GokuMohandas/mlops-course (code for MLOps course)
- Made With ML – develop, deploy and maintain production machine learning
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Where can I learn more about the engineering part of the role?
Haven’t done it but have heard good reviews - https://github.com/GokuMohandas/mlops-course
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Path to ML from a backend engineering role
If MLOps, read https://github.com/GokuMohandas/mlops-course 😎
- What skills should I focus on to improve as a MLE?
- MadeWithML – A practical approach to learning machine learning
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?
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
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.
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
Awesome-LLM - Awesome-LLM: a curated list of Large Language Model
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
machine-learning-interview - Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
chatdocs - Chat with your documents offline using AI.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
fastai - The fastai deep learning library
open_llama - OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset