HN user
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
Love this level of detail, thanks for sharing!
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.
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:
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!
How big are your datasets? Working on an Open Source git-lfs replacement called "oxen" if you are interested.
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!
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!
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.
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!
Hey all,
If you haven't seen the Oxen project yet, we have been building an open source unstructured data version control tool.
We were inspired by the idea of making large machine learning datasets living & breathing assets that people can collaborate on, rather than the static ones of the past. Lately we have been working hard on optimizing the underlying Merkle Trees and data structures with in Oxen.ai and just released v0.19.4 which provides a bunch of performance upgrades and stability to the internal APIs.
To put it all to the test, we decided to benchmark the tool on the 1 million+ images in the classic ImageNet dataset.
The TLDR is Oxen.ai is faster than raw uploads to S3, 13x faster than git-lfs, and 5x faster than DVC. The full breakdown can be found here.
https://docs.oxen.ai/features/performance
If you are in the ML/AI community, or rust aficionados, would love to get your feedback on both the tool and the codebase. We would love some community contribution when it comes to different storage backends and integrations into other data tools.
Every Friday we pick a paper for our Paper Club and discuss it, here's the recap of yesterday's session on Mixtral 8x7B if anyone is interested!
https://blog.oxen.ai/arxiv-dives-mixture-of-experts-moe-with...
Yes it is! We meet every Friday at 10am PST and pick an Arxiv Paper to go over as a group.
Feel free to join here: https://lu.ma/oxenbookclub
We went over it in our Friday paper club before the holidays which helped me gain an intuition.
https://blog.oxen.ai/mamba-linear-time-sequence-modeling-wit...
I'm still not convinced on Mamba's performance on Natural Language tasks, but maybe it's just because they haven't trained a large enough model on enough data yet.
I put together a reading list for Andrej Karpathy's intro to LLMs that would be helpful for all of the latest LLM and multi-modal architectures:
https://blog.oxen.ai/reading-list-for-andrej-karpathys-intro...
Have been studying the Mamba architecture all week and put together my notes here:
https://blog.oxen.ai/mamba-linear-time-sequence-modeling-wit...
I hadn't found a very satisfying explanation of the paper yet, and still had some questions at the end, but hopefully this can give people a good jumping off point for their understanding!
Hey all, I ran some experiments benchmarking fine-tuning ViT, ResNet50, and CLIP on a Facial Emotion Recognition dataset. I had read the original papers the past few weeks, but wanted to do some practical hands on use of the models themselves.
https://blog.oxen.ai/practical-ml-dive-how-to-customize-a-vi...
~ TLDR ~ ViT works the best in this small experiment, with minimal code. The experiment was classifying 7 different facial emotions such as "happy", "sad", "angry", etc...
Model Accuracy
* ViT - 69% * ResNet50 64% * Zero-Shot CLIP - 53%
Was honestly most impressed with CLIP's ability for zero-shot transfer, even though it had the worst accuracy. The ability to give it a freeform list of prompts or labels and it will automatically classify into the subset without training feels like the future of prototyping products and models, then once you define your use case go with something more performant like a ViT.
Anyways, I had fun writing the code and running the experiments, so thought I would share!
We've been working on a data version control system called "oxen" optimized for large unstructured datasets that we are seeing more and more with the advent of many of the generative AI techniques.
Many of these datasets have many many images, videos, audio files, text as well as structured tabular datasets that git or git-lfs just falls flat on.
Would love anyone to kick the tires on it and let us know what you think:
https://github.com/Oxen-AI/oxen-release
The commands are mirrored after git so it is easy to learn, but optimized under the hood for larger datasets.
Hey all, we had a lively group discussion today on the 2021 CLIP paper from OpenAI.
Every Friday we've been going over the fundamentals of a lot of the state of the art techniques used in Machine Learning today. Hoping to learn a little each week, and spot patterns we can apply to our own work. I feel like there's always a little nugget of information I didn't fully understand before reading the paper, so have been finding it helpful.
Though it is not groundbreaking research as of this week, I think it's nice to take a step back and review the fundamentals as well as keeping up with the latest and greatest.
Posted the notes and video recap are here if anyone finds it helpful:
https://blog.oxen.ai/arxiv-dives-zero-shot-image-classificat...
Also would love to have anyone join us live on Fridays or suggest papers! We've got a pretty consistent and fun group of 400+ engineers and researchers popping in and out.
I've spent a decent amount of time indexing and formatting a lot of machine learning datasets that include images, audio, video, and text and wanted to propose a simple format that might help us standardize a format for the data with a little more structure. Wouldn't say it is ground breaking, but I feel like could be a good practice! Let me know what you think.
https://blog.oxen.ai/suds-a-guide-to-structuring-unstructure...
Thanks for all the good work and all the pointers! Awesome stuff. Let me know if you would want to join us live on a Friday and go over some of your newer work or any recent papers you find interesting. Feel free to reach out at hello@oxen.ai if so :)
We have a reading club every Friday where we go over the fundamentals of a lot of the state of the art techniques used in Machine Learning today. Last week we dove into the "Vision Transformers" Paper from 2021 where the Google Brain team benchmarked training large scale transformers against ResNets.
Though it is not groundbreaking research as of this week, I think with the pace of AI it is important to dive deep into past work and what others have tried! It's nice to take a step back and learn the fundamentals as well as keeping up with the latest and greatest.
Posted the notes and recap here if anyone finds it helpful:
https://blog.oxen.ai/arxiv-dives-vision-transformers-vit/
Also would love to have anyone join us live on Fridays! We've got a pretty consistent and fun group of 300+ engineers and researchers showing up.
Oops, correct link here: https://blog.oxen.ai/reading-list-for-andrej-karpathys-intro...
I loved Andrej's talk about in his "Busy person's intro to Large Language Models" video, so I decided to create a reading list to dive in deeper to a lot of the topics. I feel like he did a great job of describing the state of the art for anyone from an ML Researcher to any engineer who is interested in learning more.
The full talk can be found here: https://youtu.be/ zjkBMFhNj_g?si=fPvPyOVmV-FCTFEx
Here's the reading list: https://blog.oxen.ai/reading-list-for-andrej-karpathys-intro... video/
Let me know if you have any other papers you would add!
I agree and like to think of it as machine inference vs machine learning:
We have been working on an open source tool called "Oxen" that aims to tackle this problem! Would love for you to kick the tires and see if it works for your use case. We have a free version of the CLI, python library, and server on github, and a free hosted version you can kick around at Oxen.ai.
Website: https://oxen.ai
Dev Docs: https://docs.oxen.ai
GitHub: https://github.com/Oxen-AI/oxen-release
Feel free to reach out on the repo issues if you run into anything!
Super cool! Would love to see an integration with Oxen and their data version control https://github.com/Oxen-AI/oxen-release
We have been working on a data version control tool called Oxen that is tackling many of your needs. Feel free to check it out here:
https://github.com/Oxen-AI/oxen-release#-oxen
Going down your list of requirements, Oxen has:
* Data versioning, similar paradigm to git, but built from the ground up for large ML datasets
* Inexpensive storage, comparable pricing to s3
* Branching/Merging for maintaining production training data sets
* Metadata storage and query capabilities, works with many structured data types. Have APIs for querying.
* User interface for less tech savy people, building out a hub at https://www.oxen.ai to enable this.
* Being able to define datasets that are a subset of the whole collected data (is this a similar requirement to querying?)
* Data ingestion pipeline - engineers would have to hook into APIs or CLI tools right now.
Feel free to check it out and leave any feedback on the GitHub repo!
Will add a local network benchmark as well! Many reasons to upload your data to the cloud...but agree that there are use cases where you might just want to version on your local network.
Oxen dev here - let me add some benchmarks for DVC backed by an S3 bucket. I did it awhile back and we were still faster, but agree it's a good benchmark to have.
Fundamentally even adding and committing data locally is slower, even before the push. But I agree the remote matters too.