Cgml VS text-generation-webui

> Meant no offense

None taken.

> computervison project in c#

Yeah, for CV applications nuget.org is indeed not particularly great. Very few people are using C# for these things, people typically choose something else like Python and OpenCV.

BTW, same applies to ML libraries, most folks are using Python/Torch/CUDA stack. For that hobby project https://github.com/Const-me/Cgml/ I had to re-implement the entire tech stack in C#/C++/HLSL.

> If there is a future with this idea, its gotta be just shipping the LLM with game right?

That might be a nice application for this library of mine: https://github.com/Const-me/Cgml/

That’s an open source Mistral ML model implementation which runs on GPUs (all of them, not just nVidia), takes 4.5GB on disk, uses under 6GB of VRAM, and optimized for interactive single-user use case. Probably fast enough for that application.

You wouldn’t want in-game dialogues with the original model though. Game developers would need to finetune, retrain and/or do something else with these weights and/or my implementation.

If you just want to run Mistral on Windows, you could try my port: https://github.com/Const-me/Cgml/tree/master/Mistral/Mistral...

The setup is relatively easy: install .NET runtime, download 4.5 GB model file from BitTorrent, unpack a small ZIP file and run the EXE.

Speaking about random sampling during inference, most ML models are doing it rather inefficiently.

Here’s a better way: https://github.com/Const-me/Cgml/blob/master/Readme.md#rando...

My HLSL is easily portable to CUDA, which has `__syncthreads` and `atomicInc` intrinsics.

I did a few times with Direct3D 11 compute shaders. Here’s an open-source example: https://github.com/Const-me/Cgml

Pretty sure Vulkan gonna work equally well, at the very least there’s an open source DXVK project which implements D3D11 on top of Vulkan.

Here’s an example of a custom 4 bits/weight codec for ML weights:

https://github.com/Const-me/Cgml/blob/master/Readme.md#bcml1...

llama.cpp does it slightly differently but still, AFAIK their quantized data formats are conceptually similar to my codec.

> the API was high-friction due to the shader language, and the glue between shader and CPU

Direct3D 11 compute shaders share these things with Vulkan, yet D3D11 is relatively easy to use. For example, see that library which implements ML-targeted compute shaders for C# with minimal friction: https://github.com/Const-me/Cgml The backend implemented in C++ is rather simple, just binds resources and dispatches these shaders.

I think the main usability issue with Vulkan is API design. Vulkan was only designed with AAA game engines in mind. The developers of these game engines have borderline unlimited budgets, and their requirements are very different from ordinary folks who want to leverage GPU hardware.

Minor update https://github.com/Const-me/Cgml/releases/tag/1.1a Can’t edit that comment anymore, too late.

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?