open_llama VS gpt4all

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].

I don’t know if Ollama can do this but https://gpt4all.io/ can.

Gpt4all is a local desktop app with a Python API that can be trained on your documents: https://gpt4all.io/

The readme page is cryptic. What does 'mature' mean in this context? What is the sample text a continuation of?

Hving a gif the thing in use would be great, similar to the gpt4all readme page. (https://github.com/nomic-ai/gpt4all)

Check https://github.com/nomic-ai/gpt4all instead.

"I ran performance tests on two systems, here's the results of system 1, and heres the results of system 2. Summarize the results, and build a markdown table containing x,y,z rows."

"extract the reusable functions out of this bash script"

"write me a cfssl command to generate a intermediate CA"

"What is the regex for _____"

"Here are my accomplishments over the last 6 months, summarize them into a 1 page performance report."

etc etc etc

If you're not using GPT4 or some LLM as part of your daily flow you're working too hard.

Get GPT4All (https://gpt4all.io), log into OpenAI, drop $20 on your account, get a API key, and start using GPT4.

offline versija, nors ir ne tokia pažengus - https://github.com/nomic-ai/gpt4all ; https://gpt4all.io/index.html

Take a look at https://gpt4all.io, their docs are pretty awesome

I run one. I built an iMessage-like frontend to it using plain JS and a Python websocket backend. I mostly just use it for curiosity and playing with different prompts. I only have 16GB of RAM to dedicate to it, so I use an 8B parameter model which is enough for fun and chitchat, but I don't find it good enough to replace ChatGPT.

https://github.com/nomic-ai/gpt4all