modal-examples VS FlexGen

We made a small app for SDXL Lightning, running your own Python code on GPUs. It generates images in real time.

https://potatoes.ai/

We know there was a fal.ai post yesterday, and that got a lot of interest, but we also made this demo yesterday and didn't share — just wanted to mention it as an alternative option for people who like running their own code and custom models instead of using a prebuilt API provider.

The backend code is open-source too and you can deploy it yourself: https://github.com/modal-labs/modal-examples/blob/main/06_gpu_and_ml/stable_diffusion/stable_diffusion_xl_lightning.py

The startup I work at is relatively pretty good at documentation engineering. We have written code to test the code snippets in docstrings (https://github.com/modal-labs/pytest-markdown-docs) and we have written code to do synthetic monitoring testing of the examples in our examples repo (https://github.com/modal-labs/modal-examples). We are also diligent about putting using Python's warnings library to handle API deprecation, and treat deprecation warnings as errors internally, ensuring our own code samples and examples are most up-to-date.

You can get it running with one Python script on Modal.com :)

https://github.com/modal-labs/modal-examples/blob/main/06_gp...

This demo lets you choose the podcast, and is open-source: https://modal-labs--whisper-pod-transcriber-fastapi-app.moda...

https://github.com/modal-labs/modal-examples/tree/main/06_gp...

Transcribes 1hr of audio in roughly 1min, using parallelisation across CPUs.

This demo is open-source: https://github.com/modal-labs/modal-examples/tree/main/06_gp....

https://modal-labs--whisper-pod-transcriber-fastapi-app.moda...

It's become so easy to stick together ML models, often without training most or all of them yourself.

*video demo:* https://youtu.be/mQsMuM8d4Qc

*cloud platform:* https://modal.com

*code*: https://github.com/modal-labs/modal-examples/tree/main/06_gp...

Transcription - OpenAI just released Whisper. Check out what it can do with podcasts

Here's the source code.

LLaMA, Pythia, RWKV, Flan-T5 (self-hosted), FlexGen

> With no real knowledge of LLM and only recently started to understand what LLM terms mean, such as 'model, inference, LLM model, intruction set, fine tuning' whatelse do you think is required to make a took like yours?

This was mee a few weeks ago. I got interested in all this when FlexGen (https://github.com/FMInference/FlexGen) was announced, which allowed to run inference using OPT model on consumer hardware. I'm an avid user of Stable Diffusion, and I wanted to see if I can have an SD equivalent of ChatGPT.

Not understanding the details of hyperparameters or terminology, I basically asked ChatGPT to explain to me what these things are:

   Explain to someone who is a software engineer with limited knowledge of ML terms or linear algebra, what is "feed forward" and "self-attention" in the context of ML and large language models. Provide examples when possible.