cross-posted from: https://lemm.ee/post/61282397

Open sourcing this project I made in just a weekend, planning to continue this in my free time, with synthetic data gen and some more modifications, anyone is welcome to chip in, I'm not an expert in ML. The inference is live here using tensorflow.js. The model is just 1.92 Megabytes!

cross-posted from: https://lemmy.world/post/27657674

Hello!

I did a map generator(it's pixel art and the largest are 300x200 pixels) some time ago and decided to generate 3 types of map sizes and 1500 maps for each size to train a model to practice and I thought to do that dataset open source.

Is that really something that people want/appreciate or not really? I'm a bit lost on how to proceed and what license to use. Does it make sense to use an MIT License? Or which one do you recommend?

thanks!

Hi all,

I've been experimenting with building and deploying ML and LLM projects for a while now, and honestly, it’s been a journey.

Training the models always felt more straightforward, but deploying them smoothly into production turned out to be a whole new beast.

I had a really good conversation with Dean Pleban (CEO @ DAGsHub), who shared some great practical insights based on his own experience helping teams go from experiments to real-world production.

Sharing here what he shared with me, and what I experienced myself -

Data matters way more than I thought. Initially, I focused a lot on model architectures and less on the quality of my data pipelines. Production performance heavily depends on robust data handling—things like proper data versioning, monitoring, and governance can save you a lot of headaches. This becomes way more important when your toy-project becomes a collaborative project with others.

LLMs need their own rules. Working with large language models introduced challenges I wasn't fully prepared for—like hallucinations, biases, and the resource demands. Dean suggested frameworks like RAES (Robustness, Alignment, Efficiency, Safety) to help tackle these issues, and it’s something I’m actively trying out now. He also mentioned "LLM as a judge" which seems to be a concept that is getting a lot of attention recently.

Some practical tips Dean shared with me:

Save chain of thought output (the output text in reasoning models) - you never know when you might need it. This sometimes require using the verbos parameter.

Log experiments thoroughly (parameters, hyper-parameters, models used, data-versioning...).

Start with a Jupyter notebook, but move to production-grade tooling (all tools mentioned in the guide bellow 👇🏻)

To help myself (and hopefully others) visualize and internalize these lessons, I created an interactive guide that breaks down how successful ML/LLM projects are structured. If you're curious, you can explore it here:

https://www.readyforagents.com/resources/llm-projects-structure

I'd genuinely appreciate hearing about your experiences too—what’s your favorite MLOps tools? I think that up until today dataset versioning and especially versioning LLM experiments (data, model, prompt, parameters..) is still not really fully solved.

Declaration

We, the undersigned members of the Open Source community, assert that Open Source is defined solely by the Open Source Definition (OSD) version 1.9.

Any amendments or new definitions shall only be recognized if declared by clear community consensus through a transparent process to be determined.

When training a transformer on positionally encoded embeddings, should the tgt output embeddings also be positionally encoded? If so, wouldn't the predicted/decoded embeddings also be positionally encoded?

Someone (Dreamertist on reddit) got tired of depending on Huggingface for downloading models and proposes a torrent tracker to share more efficiently these huge blobs.

It just started, only a few models uploaded yet, but I think it is worth that we all put our local stash online there. Making a new torrent is super easy (one missing step though: when "re-downloading" the model you need to save it in the directory where it already exists. This way it will "resume" at 100% completion and switch to seeding mode)

Imagine AI giving offsprings...

Hey guys,

I have been experimenting with self-supervised visual learning a bit. Until now I have only ever used U-Nets and related architectures.

No matter what specific task, images or other parameters I changed I always encountered these stains on my output-images (here marked with green), although sometimes more, sometimes less.

Now I wondered if anybody could tell me where they came from and how I could prevent them?

In the attached picture the input (left) and target (right) are the same, so that I can be sure these stains do not come from a badly designed learning task, yet they still appear (output is the middle image).

Thanks in advance and all the best :D

Edit: added line breaks

Copilot sounds amazing on paper. The free (to 365 subs) version on the web is just Chat GPT4, so that's familiar enough. The integration with 365 applications is really what grabs me. Stuff like tossing it 10 spreadsheets and asking it to analyze and compare the data, having a virtual assistant to remind me of upcoming actionables, and summarizing a meeting when I zone out - it all sounds really handy.

I met with Microsoft last week and they're down for giving me a 90 day trial if I want to take it for a spin. Any thoughts or suggestions? I ideally want to determine if this will improve productivity for my end users enough to be worth the insane cost of $30/user/mo.

Hi all,

I think around 1 or 2 years ago, I stumbled upon a personal blog of an asian woman (I think) working at OpenAI. She had numerous extensive fascinating blog posts on a black themed blog, going into the technical details of embeddings of language models and such.

I can no longer find that blog and have no other information to go by. Would anyone possibly know which blog I'm referring to? It would be very much appreciated.