Awesome-LLM VS open_llama

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.

Check out this repo, there should be some useful things worth noting https://github.com/Hannibal046/Awesome-LLM

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.

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.

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.

The RedPajama dataset was adapted by the OpenLLaMA project at UC Berkeley, creating an open-source LLaMA equivalent without Meta’s restrictions. The model's later version also included data from Falcon and StarCoder. This highlights the importance of open-source models and datasets, enabling free repurposing and innovation.

OpenLLaMA is though. https://github.com/openlm-research/open_llama

All of these are surmountable problems.

We can beat OpenAI.

We can drain their moat.

#1: 🌞 Open-source Reproduction of Meta AI’s LLaMA OpenLLaMA-13B released. (trained for 1T tokens) | 0 comments #2: 🎉 #1 on HuggingFace.co's Leaderboard Model Falcon 40B is now Free (Apache 2.0 License) | 0 comments #3: 😍 Have you seen this repo? "running LLMs on consumer-grade hardware. compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!" | 0 comments

Trained OpenLLaMA models are from the OpenLM Research team in collaboration with Stability AI: https://github.com/openlm-research/open_llama

I can't use Llama or any model from the Llama family, due to license restrictions. Although now there's also the OpenLlama family of models, which have the same architecture but were trained on an open dataset (RedPajama, the same dataset the base model in my app was trained on). I'd love to pursue the direction of extended context lengths for on-device LLMs. Likely in a month or so, when I've implemented all the product feature that I currently have on my backlog.

https://github.com/openlm-research/open_llama#update-0615202...).

XGen-7B is probably the superior 7B model, it's trained on more tokens and a longer default sequence length (although both presumably can adopt SuperHOT (Position Interpolation) to extend context), but larger models still probably perform better on an absolute basis.

Compare it to openllama. It github doesn't have a single script on how to do anything.

OpenLLaMA models up to 13B parameters have now been trained on 1T tokens:

https://github.com/openlm-research/open_llama

The deployed LLM binary, orca mini, has 3 billion parameters. Orca mini is based on the OpenLLaMA project.

OpenLM Research released its 1T token version of OpenLLaMA 13B - the permissively licensed open source reproduction of Meta AI's LLaMA large language model. [Details].