gschoeni
Joined 3 years ago
Karma
75
Submissions
68
Member since
Jan 20
Recent submissions
Comment · 4 months ago
one of the oxen engineers here would love to hear about anything you ran into on the os product or platform! we've grown the team a bunch and are eager to learn what your perfect vcs looks like
Comment · 6 months ago
Love this level of detail, thanks for sharing!
Comment · 1 year ago
Wanted to share some learnings we had optimizing and deploying Qwen-Image-Edit at scale to replace Nano-Banana. The goal was to generate a product catalogue of 1.2m images, which would have cost $46k with Nano-Banana or GPT-Image-Edit.
Qwen-Image-Edit being Apache 2.0 allows you to fine-tune and apply a few tricks like compilation, lightning lora and quantization to cut costs.
The base model takes ~15s to generate an image which would mean we would need 1,200,000*15/60/60=5,000 compute hours.
Compilation of the PyTorch graph + applying a lightning LoRA cut inference down to ~4s per image which resulted in ~1,333 compute hours.
I'm a big fan of open source models, so wanted to share the details in case it inspires you to own your own weights in the future.
Comment · 1 year ago
One of the maintainers of the Open Source project "Oxen" here. Our VCS scales for binary data better than git does, and was built to solve some of the problems with git-lfs and git-annex.
We've had a few requests to integrate with music production workflows, but haven't taken it on yet. If anyone wants to collaborate to integrate Oxen with their DAW or workflow let us know! Here's the project:
Comment · 1 year ago
We're working on `oxen` to solve a lot of the problems we ran into with git or git-lfs.
We have an open source CLI and server that mirrors git, but handles large files and mono repos with millions of files in a much more performant manner. Would love feedback if you want to check it out!
Comment · 1 year ago
How big are your datasets? Working on an Open Source git-lfs replacement called "oxen" if you are interested.
Comment · 1 year ago
FLUX.1-dev is one of the most widely fine-tuned models out there - but I couldn’t find a single, clean, end-to-end example that actually worked. So I wrote one. Enjoy!
Comment · 1 year ago
Over the past ~1.5 years I've been running a research paper club where we dive into interesting/foundational papers in AI/ML. So we naturally have come across a lot of the papers that lead up to DeepSeek-R1. While diving into the DeepSeek papers this week, I decided to compile a list of papers that we've already gone over or I think would be good background reading to get a bigger picture of what's going on under the hood of DeepSeek.
Grab a cup of coffee and enjoy!
Comment · 1 year ago
Right now the UI is only available through a VPC deployment. We are thinking about making the data grid / query interface embeddable or available through a library which would make it easy to self host.
Comment · 1 year ago
We're working on Oxen.ai which is an Open Source CLI and Server with Python bindings as well. Optimized for ML/AI workloads but works with any type of data and we see usage from game companies, bio, aerospace etc.
Feel free to check it out here: https://github.com/Oxen-AI/oxen-release
Or a hub you can host data on (we have public and private repos, or private VPC deployments): https://oxen.ai
The CLI mirrors git so it's easy to learn. It has some interesting build in tooling for diff-ing datasets and working on them remotely without downloading a full copy of the data as well.
Happy to answer any other questions!