guidance VS text-generation-webui

[1]: https://github.com/guidance-ai/guidance/tree/main

> The most obvious usage of this is forcing a model to output valid JSON

Isn't this something that Outlines [0], Guidance [1] and others [2] already solve much more elegantly?

0. https://github.com/outlines-dev/outlines

1. https://github.com/guidance-ai/guidance

2. https://github.com/sgl-project/sglang

Or combine it with something like llama.cpp's grammer or microsoft's guidance-ai[0] (which I prefer) which would allow adding some react-style prompting and external tools. As others have mentioned, instruct tuning would help too.

[0] https://github.com/guidance-ai/guidance

have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.

Thanks for your comment.

I did not know about "Betteridge's law of headlines", quite interesting. Thanks for sharing :)

You raise some interesting points.

1) Safety: It is true that LVMs and LLMs have unknown biases and could potentially create unsafe content. However, this is not necessarily unique to them, for example, Google had the same problem with their supervised learning model https://www.theverge.com/2018/1/12/16882408/google-racist-go.... It all depends on the original data. I believe we need systems on top of our models to ensure safety. It is also possible to restrict the output domain of our models (https://github.com/guidance-ai/guidance). Instead of allowing our LVMs to output any words, we could restrict it to only being able to answer "red, green, blue..." when giving the color of a car.

2) Cost: You are right right now LVMs are quite expensive to run. As you said are a great way to go to market faster but they cannot run on low-cost hardware for the moment. However, they could help with training those smaller models. Indeed, with see in the NLP domain that a lot of smaller models are trained on data created with GPT models. You can still distill the knowledge of your LVMs into a custom smaller model that can run on embedded devices. The advantage is that you can use your LVMs to generate data when it is scarce and use it as a fallback when your smaller device is uncertain of the answer.

3) Labelling data: I don't think labeling data is necessarily cheap. First, you have to collect the data, depending on the frequency of your events could take months of monitoring if you want to build a large-scale dataset. Lastly, not all labeling is necessarily cheap. I worked at a semiconductor company and labeled data was scarce as it required expert knowledge and could only be done by experienced employees. Indeed not all labelling can be done externally.

However, both approaches are indeed complementary and I think systems that will work the best will rely on both.

Thanks again for the thought-provoking discussion. I hope this answer some of the concerns you raised

I've had a bit of trouble getting function calling to work with cases that aren't just extracting some data from the input. The format is correct but it was harder to get the correct data if it wasn't a simple extraction.

Hopefully OpenAI and others will offer something like https://github.com/guidance-ai/guidance at some point to guarantee overall output structure.

Failed validations will retry, but from what I've seen JSONSchema + generated JSON examples are decently reliable in practice for gpt-3.5-turbo and extremely reliable on gpt-4.

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?