Awesome-LLM
LLMsPracticalGuide
Awesome-LLM | LLMsPracticalGuide | |
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10 | 11 | |
14,654 | 8,662 | |
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8.6 | 4.5 | |
8 days ago | 24 days ago | |
Creative Commons Zero v1.0 Universal | - |
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Awesome-LLM
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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.
- Couple of questions about a.i that can be run locally
- How to dive deeper into LLMs?
- [Hiring] Developer to build AI-powered chatbots with open source LLMs
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Creating a Wiki for all things Local LLM. What do you want to know?
Check out this repo, there should be some useful things worth noting https://github.com/Hannibal046/Awesome-LLM
- Large Language Model (LLM) Resources
- Curated list for LLMs: papers, training frameworks, tools to deploy, public APIs
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Performance of GPT-4 vs PaLM 2
First this is a pretty good starting point as a resource for learning about and finding open source models and the overall public history of progress of LLMs.
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FreedomGPT: AI with no censorship
This seems fishy as fuck. First red flag is a fishy installer instead of any huggingface link for the model. Upon further search I found this: https://desuarchive.org/g/thread/92686632/#92692092 There are posts in its own sub, r slash freedomgpt, raising concerns, and many new accounts with low karma replying to them(I don't think I can link other subs here, check them yourself), 100% some botting/astroturfing going on. Not touching this. Even in the best case scenario that this is legit with no funny business, this is supposed to be based on llama, which is substantially different tiny model(hence why it can be run on your computer at all). This is no Chatgpt equivalent eitherway. I would recommend getting something more reputable from github if you are interested in running LLMs yourself.
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Ask HN: Foundational Papers in AI
https://github.com/Hannibal046/Awesome-LLM has a curated list of LLM specific resources.
Not the creator, just happened upon it when researching LLMs today.
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?
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
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.
FreedomGPT - This codebase is for a React and Electron-based app that executes the FreedomGPT LLM locally (offline and private) on Mac and Windows using a chat-based interface
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
LLMZoo - ⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡
chatdocs - Chat with your documents offline using AI.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
dalai - The simplest way to run LLaMA on your local machine
open_llama - OpenLLaMA, a permissively licensed open source reproduction of Meta AI’s LLaMA 7B trained on the RedPajama dataset
langchain - 🦜🔗 Build context-aware reasoning applications
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]