montanalow

5 ms·

1.
Plea deal with accused 9/11 plotters revoked(bbc.com)
2 points by montanalow 2y ago|0 comments
2.
by montanalow 2y ago
Thanks for the feedback. I've updated that paragraph to clarify the product placement is for Llama. I don't really think Meta needs our help to much...
3.
by montanalow 2y ago
If you want to see all the SQL functions and tables Korvus depends on, check out the pgml extension. https://postgresml.org/docs/open-source/pgml/
4.
by montanalow 2y ago
Yep, one of our other projects, pgcat is exactly to help make the horizontal scaling as easy as possible. https://github.com/postgresml/pgcat
5.
by montanalow 2y ago
Most managers and organizations miss the fact that engineers are often motivated by solving puzzles in ways like this, because it's fun. If you want to accomplish big challenges, quickly, make it fun for the engineer. Working long hour
6.
Serverless LLMs are dead; Long live Serverless LLMs(postgresml.org)
1 points by montanalow 2y ago|0 comments
7.
by montanalow 2y ago
You can do all of that in a single SQL query, with pgml.embed() and then pgml.train() a custom reranker with xgboost, to pgml.predict() the conversion score of a search result based on click-through-rate, or other objective. If you'd l
8.
LLMs are commoditized; data is the differentiator(postgresml.org)
1 points by montanalow 2y ago|0 comments
9.
by montanalow 3y ago
This is an SDK built to interact with PostgresML, which provides ML & AI _inside_ a Postgres database. Clients in this case don't perform inference, rather the server does. You could run the open source server locally, or connect t
10.
by montanalow 3y ago
If anyone would like a free PostgresML T-shirt, we just did our first run. Feel free to email me with your shipping info and size. It'd also be nice to get to know you a bit if your email address isn't obvious.
11.
Show HN: PostgresML – Run open-source ML/LLM models in a Postgres extension(postgresml.org)
2 points by montanalow 3y ago|0 comments
12.
How-to improve search results with machine learning(postgresml.org)
1 points by montanalow 3y ago|0 comments
13.
Pgml-chat: A CLI for deploying low-latency knowledge-based chatbots(postgresml.org)
5 points by montanalow 3y ago|0 comments
14.
LLM based pipelines with PostgresML and DBT(postgresml.org)
4 points by montanalow 3y ago|0 comments
15.
by montanalow 3y ago
Quantization allows PostgresML to fit larger models in less RAM. These algorithms perform inference significantly faster on NVIDIA, Apple and Intel hardware. Half-precision floating point and quantized optimizations are now available for yo
16.
PostgresML Adds GPTQ and GGML Quantized LLM Support for HuggingFace Transformers(postgresml.org)
4 points by montanalow 3y ago|1 comments
17.
MindsDB vs. PostgresML(postgresml.org)
4 points by montanalow 3y ago|0 comments
18.
Python SDK for PostgresML with scalable LLM embedding memory and text generation(postgresml.org)
3 points by montanalow 3y ago|1 comments
19.
by montanalow 3y ago
We've been working on a Python SDK[1] for PostgresML to make it easier for application developers to get the performance and scalability benefits of integrated memory for LLMs, by combining embedding generation, vector recall and LLM t
20.
by montanalow 3y ago
Full disclaimer, I work on PostgresML, and I'm not a MindsDB expert. They both do ML in the database, but we've been at least as focused on scalability for Postgres workloads as ML functionality, with our PgCat project. It's
21.
Personalize embedding results with application data in your database(postgresml.org)
1 points by montanalow 3y ago|1 comments
22.
by montanalow 3y ago
There is a lot of latency involved shuffling data for modern/complex ML systems in production. In our experience these costs dominate end-to-end user experienced latency, rather than actual model or ANN algorithms, which unfortunately
23.
Tuning vector recall while generating query embeddings in the database(postgresml.org)
3 points by montanalow 3y ago|0 comments
24.
by montanalow 4y ago
RAM and the Postgres shared buffers, as well OS page cache can also be important factors when there is an active working subset, e.g. the active sessions on a website might be reused hundreds of times per session, with only relatively small
25.
by montanalow 4y ago
Another suggestion: Don't build you identity around a language or platform. They come and go. Except SQL. It's been around for longer than either of us.
26.
by montanalow 4y ago
Low effort comment that didn't read the post. - Multiple formats were compared - Duckdb is not a production ready service - Pandas isn't used You seem to be trolling.
27.
by montanalow 4y ago
How are you doing online ML inference, without fetching data?
28.
by montanalow 4y ago
Stateful services are indeed more painful to manage than non stateful ones. Ignoring state (data fetch time) for ML as if the model artifact is the only important component is... not a winning strategy.
29.
by montanalow 4y ago
You get it. 1 tier is better than 2 tier. Python can't be 1 tier, unless it loads the full dataset which is not generally feasible for production online inference cases. PostgresML is 1 tier, and supports the traditional Python use cas
30.
by montanalow 4y ago
The converse is also true. We may be missing diagnosis for people born in September, who may struggle but not quite meet the definitions which often include academic performance.

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