awesome-ml VS text-generation-webui

I do something like that for open source:

https://github.com/underlines/awesome-ml

But it lost a bit of traction lately.

It needs re-work for the categories, or better, a tagging system, because these products and libraries can sit in more than one space.

Plus it either needs massive collaboration, or some form of automation (with an LLM and indexer), as I can't keep up with it.

This aera is barely new. Look at how old some of the projects are:

https://github.com/underlines/awesome-ml/blob/master/audio-a...

The thing that changes is the complexity to run it. I was training my wife's voice and my voice for fun and needed 15min of audio and trained on my 3080 for 40 minutes.

Now it's 2 Minutes.

Thanks for your clarifications. I added it to my awesome list:

https://github.com/underlines/awesome-marketing-datascience/...

RAG is very difficult to do right. I am experimenting with various RAG projects from [1]. The main problems are:

- Chunking can interfer with context boundaries

- Content vectors can differ vastly from question vectors, for this you have to use hypothetical embeddings (they generate artificial questions and store them)

- Instead of saving just one embedding per text-chuck you should store various (text chunk, hypothetical embedding questions, meta data)

- RAG will miserably fail with requests like "summarize the whole document"

- to my knowledge, openAI embeddings aren't performing well, use a embedding that is optimized for question answering or information retrieval and supports multi language. Also look into instructor embeddings: https://github.com/embeddings-benchmark/mteb

1 https://github.com/underlines/awesome-marketing-datascience/...

I do the same and with currently with 700+ github stars people seem to like it, but it's still curated/manual, because the hf search API is so limited and I don't have the time to create a scraper.

Added to the list

I keep mine fairly up to date as well, almost daily: https://github.com/underlines/awesome-marketing-datascience/blob/master/README.md

Disclaimer: I am curating LLM-tools on github [1]

A few thoughts:

* allow for custom endpoint URLs, this way people can use open source LLMs with a fake openAI API backend like basaran[2] or llama-api-server[3]

* look into better embedding methods for info-retrieval like InstructorEmbeddings or Document Summary Index

* Don't use a single embedding per content item, use multiple to increase retrieval quality

1 https://github.com/underlines/awesome-marketing-datascience/...

2 https://github.com/hyperonym/basaran

3 https://github.com/iaalm/llama-api-server

Oobabooga isn't a wrapper for llama.cpp, but it can act as such. A usual Oobabooga installation on windows will use a GPTQ wheel (binary) compiled for cuda/windows, or alternatively use llama.cpp's API and act as a GUI. On Linux you had the choice to use the triton or cuda branch for GPTQ, but I don't know if that is still the case. You can also go the route to use virtualized and hardware accelerated WSL2 Ubuntu on Windows and use anything similar to linux. See my guide

Some of the tools offer a path to doing tool use (fetching URLs and doing things with them) or RAG (searching your documents). I think Oobabooga https://github.com/oobabooga/text-generation-webui offers the latter through plugins.

Our tool, https://github.com/transformerlab/transformerlab-app also supports the latter (document search) using local llms.

You can use webui https://github.com/oobabooga/text-generation-webui

Once you get a version up and running I make a copy before I update it as several times updates have broken my working version and caused headaches.

a decent explanation of parameters outside of reading archive papers: https://github.com/oobabooga/text-generation-webui/wiki/03-%...

a news ai website:

The other answers are recommending paths which give you #1. less control and #2. projects with smaller eco-systems.

If you want a truly general purpose front-end for LLMs, the only good solution right now is oobabooga: https://github.com/oobabooga/text-generation-webui

All other alternatives have only small fractions of the features that oobabooga supports. All other alternatives only support a fraction of the LLM backends that oobabooga supports, etc.

The example waifu in text-generation-webui is good enough for me.

https://github.com/oobabooga/text-generation-webui/blob/main...

> Downloading text-generation-webui takes a minute, let's you use any model and get going.

What you're missing here is you're already in this area deep enough to know what ooogoababagababa text-generation-webui is. Let's back out to the "average Windows desktop user" level. Assuming they even know how to find it:

1) Go to https://github.com/oobabooga/text-generation-webui?tab=readm...

2) See a bunch of instructions opening a terminal window and running random batch/powershell scripts. Powershell, etc will likely prompt you with a scary warning. Then you start wondering who ooobabagagagaba is...

3) Assuming you get this far (many users won't even get to step 1) you're greeted with a web interface[0] FILLED to the brim with technical jargon and extremely overwhelming options just to get a model loaded, which is another mind warp because you get to try to select between a bunch of random models with no clear meaning and non-sensical/joke sounding names from someone called "TheBloke". Ok...

Let's say you somehow braved this gauntlet and get this far now you get to chat with it. Ok, what about my local documents? text-generation-webui itself has nothing for that. Repeat this process over the 10 random open source projects from a bunch of names you've never heard of in an attempt to accomplish that.

This is "I saw this thing from Nvidia explode all over media, twitter, youtube, etc. I downloaded it from Nvidia, double-clicked, pointed it at a folder with documents, and it works".

That's the difference and it's very significant.

[0] - https://raw.githubusercontent.com/oobabooga/screenshots/main...

Maybe a copout answer, but setting up a local LLM on my development machine has been invaluable. I use Deep Seek Coder 6.7 [0] and Oobabooga's UI [1]. It helps me solve simple problems and find bugs, while still leaving the larger architecture decisions to me.

[0] https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instr...

[1] https://github.com/oobabooga/text-generation-webui

You can download it and run it with [this](https://github.com/oobabooga/text-generation-webui). There's an API mode that you could leverage from your VS Code extension.

Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).

For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]

[1] https://github.com/oobabooga/text-generation-webui/issues/53...

[2] https://github.com/langroid/langroid/blob/main/langroid/lang...

Related question - I assume ollama auto detects and applies the right chat formatting template for a model?