stonebraker

4 ms·

1.
Keeping multimodal parsing free for all
3 points by stonebraker 1y ago|0 comments
2.
by stonebraker 2y ago
Nice! This should be helpful. https://github.com/oidlabs-com/Lexoid?tab=readme-ov-file#ben...
3.
by stonebraker 2y ago
True, but the cost for other models will continue to go down as well.
4.
by stonebraker 2y ago
recently, started using rye. Convenient for sure.
5.
by stonebraker 3y ago
Blog: https://medium.com/the-story-within/state-of-text-to-sql-dc3...
6.
LLM2SQL New Repo(github.com)
1 points by stonebraker 3y ago|1 comments
7.
by stonebraker 3y ago
Might also be worth scanning, Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies https://arxiv.org/abs/2308.03188
8.
by stonebraker 3y ago
I guess for self-evaluation and generation, we might want to choose a model that's performant for the job. This means that if the 70B is fine-tuned, that is probably the judge + augmentor vs a generic model. Also, I think the paper sho
9.
by stonebraker 3y ago
Thanks
10.
by stonebraker 3y ago
So you would rather connect to a databricks lake from a privately hosted jupyterhub if that's an option?
11.
Do folks often use Jupyter notebook outside of Databricks?
1 points by stonebraker 3y ago|5 comments
12.
by stonebraker 3y ago
Interesting
13.
What are different use-cases where text 2 SQL generation could be helpful?
1 points by stonebraker 3y ago|2 comments
14.
by stonebraker 4y ago
Has anyone compared performance between LLaMa.cpp vs Alpaca.cpp?
15.
by stonebraker 9y ago
this is awesome. great work :)
16.
Ask HN: Do we have an opinion between vegeta vs. JMeter?
1 points by stonebraker 9y ago|0 comments
17.
by stonebraker 10y ago
Great intro on the topic using pymc package
18.
Summarizing all big data projects(blog.andreamostosi.name)
3 points by stonebraker 11y ago|0 comments