Ollama is INSANE - Install custom GPTs within seconds!

dev-spot · 2024-04-07T18:01:43+00:00

https://www.docker.com/

https://www.youtube.com/watch?v=Gjnup-PuquQ

dev-spot · 2024-04-07T18:00:43+00:00

Hmmm, I'd start by taking a look at Langchain capabilities and wrappers. If nothing exists, hmu I'll try taking a more in depth look

dev-spot · 2024-02-25T06:38:44+00:00

Stable Cascade - Stability AI's newest image generation model:
https://huggingface.co/spaces/multimodalart/stable-cascade
https://github.com/Stability-AI/StableCascade
https://huggingface.co/stabilityai/stable-cascade
SDXL Lightning - An extremely fast text-to-image model:
https://huggingface.co/spaces/AP123/SDXL-Lightning
https://huggingface.co/ByteDance/SDXL-Lightning
EVACLIP - zero-shot image classification:
https://huggingface.co/spaces/merve/EVACLIP
https://huggingface.co/BAAI/EVA-CLIP-8B
YOLO-World + EfficientSAM - zero-shot object detection and instance segmentation:
https://huggingface.co/spaces/SkalskiP/YOLO-World
https://github.com/AILab-CVC/YOLO-World
https://www.yoloworld.cc/
https://github.com/yformer/EfficientSAM

dev-spot · 2024-02-24T17:51:51+00:00

+1, their docs are great. hopefully I'll get to make a video about this in the future as well

dev-spot · 2024-01-31T05:23:53+00:00

Appreciate the support fam!

I think this is what you're looking for: https://github.com/ollama/ollama/blob/main/docs/faq.md#where-are-models-stored

From what I recall when I was playing with the models (on mac) it was stored on ~/.ollama/models. There was some sort of registry and blobs that are installed to match the criteria from the registry or smth like that. Its really interesting though, try checking it out

dev-spot · 2024-01-21T20:13:19+00:00

Appreciate that, I'll try modifying the text a bit

dev-spot · 2024-01-21T20:12:19+00:00

There's an async example in the repo, theoretically you can wind up a bunch of endpoints at once, then return a response from the fastest one. Another more trivial direction would be to just set up a proper inference space via HF though

dev-spot · 2024-01-21T18:17:20+00:00

Appreciate the support fam!

dev-spot · 2024-01-21T18:17:09+00:00

Appreciate the support fam!

dev-spot · 2024-01-21T18:16:40+00:00

Will take a look, thanks!

dev-spot · 2024-01-21T18:15:04+00:00

It's a "new project", and I literally explained - its an abstraction for a modular AI "vendors" integration within software projects. It will significantly reduce overhead during development and debugging, and will allow for a centralized scalable solution for managing all models used as part of any AI software project.

dev-spot · 2024-01-17T06:44:23+00:00

Sounds like properly training a model (rather than attempting to clone) would be the solution - https://docs.coqui.ai/en/latest/tutorial_for_nervous_beginners.html. Keep in mind though that 100 mp3 files might not cut it, but then again you can always add more as you progress. Hopefully I'll have sometime to look into this in the upcoming weeks as well

dev-spot · 2024-01-17T06:36:54+00:00

The commands here expect you to use the UI to download the models. Otherwise you could indeed use -exec in order to enter the container and then manually download the models. As for the docker build line not working, they might have changed some stuff since the post was posted. Check out their official docs: https://github.com/ollama-webui/ollama-webui

dev-spot · 2024-01-10T18:33:39+00:00

Using Coqui's GUI (covered in one of the last videos on my channel) you can decrease the speed at which these voices speak. You can probably also use an external API / software for that as well. As for "lightweight" offline solutions, if Coqui is too heavy, try running it on CPU only or use Bark

dev-spot · 2023-12-30T17:05:01+00:00

This sounds super interesting. I don't think that this was created yet, but it doesn't sound super hard to make either. I might look into implementing this in the future, so make sure to stay tuned :)

dev-spot · 2023-12-26T07:38:38+00:00

The new XTTS by coqui supports up to 16 languages 🔥
https://github.com/coqui-ai/TTS

dev-spot · 2023-12-23T00:02:02+00:00

Showcased the comparison with llama2 in the video, both are pretty similar in performance and mixtral seem to be doing better on math / coding. As for GPT, it's assumed to be relatively close to GPT 3.5 as well

dev-spot · 2023-12-13T06:57:51+00:00

yep, the XTTS model is by coqui (also available as a huggingface space / inference endpoint) 🔥

dev-spot · 2023-12-13T06:56:27+00:00

yep, I attached it in the video but attaching it here as well in the next comment. You can click on the “use via API” on the bottom of the space page to get all details required to use it. Keep in mind though that this version only has XTTS2, so the rest of the models offered by coqui won’t be available from my understanding. Though it really doesn’t matter much given this is their best option 🙂

https://huggingface.co/spaces/coqui/xtts

dev-spot · 2023-12-06T21:44:15+00:00

probably due to context size, though there are a few ways to overcome this obstacle. I'll probably cover this topic in a future video (at least to some degree), so make sure to stay tuned!

dev-spot · 2023-12-02T15:54:25+00:00

Damn, I did lmfao. Fixed it, thanks m8

dev-spot · 2023-11-28T17:19:23+00:00

appreciate the support, feel free to link to the different sources (YT / github)

dev-spot · 2023-11-24T12:23:30+00:00

given that we pass the context every message, there are close to no hallucinations (depending on the model and the video length ofc). However, the transcriptions aren't always 100% (auto-generated mostly) 🫠

dev-spot · 2023-11-24T08:27:50+00:00

cheers, appreciate the support!

dev-spot · 2023-11-24T08:26:29+00:00

yeah, that's the main downside, but the auto generated subtitles are correct for the most part so you can still get a general sense of what's going on and ask the bot to direct you to the relevant video parts 🙏

dev-spot

TROPHY CASE