Sounds nice except that these are 1 very small scale model, 1 reranker, and 1 embedding model that are far from frontier LLM level. And they're not open sourced.
As much as I agree with the message, this reads like marketing copy trying to make a big deal out of a tiny model being hosted privately.
Just talk to them as if they were already your friend. Most of what you talk about with friends isn't just mutual interests and you start conversations with them all the time.
This blog post describes the basic work of a research engineer and nothing more. The amount of surprise the author has seems to suggest they haven't really worked in ML for very long.
Honestly? This is the best its ever been. Getting stuff to run before huggingface and uv and docker containers with cuda was way worse. Even with full open-source, go try to run a 3+ years old model and codebase. The field just moves very fast.
The paper you're talking about is "Deal or No Deal? End-to-End Learning for Negotiation Dialogues" and it was just AIs drifting away from English. The crazy news article was from Forbes with the title "AI invents its own language so Facebook had to shut it down!" before they changed it after backlash.
> the generation of 281,128 augmented examples, from which 1,000 were
held out as a benchmark test set.
This model is trained on a custom dataset of 280k examples then tested on 1k very similar examples from the same dataset. Of course it is specialized to outperform general models on this specific task in this specific domain with this specific json format for output.
This is a reasonable hobby project and interesting approach to synthetic data generation but not impressive research.
At minimum you should test your model on other benchmarks that have similar tasks e.g. docbench
As much as I agree with the message, this reads like marketing copy trying to make a big deal out of a tiny model being hosted privately.