open-llms VS open_llama

Everyone knows about ChatGPT by now, but did you know there are other models like "Mistral" or "Falcon" - you can view a full list of open-source models here or on huggingface.

This is the most well-maintained list of commercially usable open LLMs: https://github.com/eugeneyan/open-llms

MPT, OpenLLaMA, and Falcon are probably the most generally useful.

For code, Replit Code (specifically replit-code-instruct-glaive) and StarCoder (WizardCoder-15B) are the current top open models and both can be used commercially.

There's also MPT, which has a 7B, and Falcon, with a 7B and 40B although they have not had the inference tuning in community projects that the llamas have had. This is a good repo for reviewing what's available atm: https://github.com/eugeneyan/open-llms

If I was going to do the same I would at least build off of something, check out https://github.com/eugeneyan/open-llms, you should at least have a decent understanding of artificial neural networks (ANNs) and this link is pretty good on the basic concepts you need inc classification and learning types, good luck friend.

This list might help: https://github.com/eugeneyan/open-llms

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].