localGPT VS LLMsPracticalGuide

I think local LLMs are great for tinkerers, and with quantization can run on most modern PCs. I am not comfortable sending over my personal data over to OpenAI/Anthropic, so I've been playing around with https://github.com/PromtEngineer/localGPT/, GPT4All, etc. which keep the data all local.

Sliding window chunking, RAG, etc. seem more sophisticated than the other document LLM tools, so I would love to try this out if you ever add the ability to run LLMs locally!

localGPT can parse PDF into embeddings, see <https://github.com/PromtEngineer/localGPT>.

This is going so fast that it feels like a new thing pops up every day. LocalGPT seems to have gotten a lot of traction though: https://github.com/PromtEngineer/localGPT

i am currently trying to use localGPT (https://github.com/PromtEngineer/localGPT) for a project and i encountered a problem.

I tinker a lot with electronics. I have put datasheets for components, documentation for development boards, documentation for software libraries, etc into a database with localGPT.

There are a bunch of other methods to improve quality and performance like tree-of-thought-llm, connecting a LLM to a database or have it review its own output.

Check out local gpt on git hub. I tried but it had slow response for me. Other developers are fine. https://github.com/PromtEngineer/localGPT

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.

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

The Practical Guides for Large Language Models (https://github.com/Mooler0410/LLMsPracticalGuide)