kartoolOz

It's very hyper-parameter dependent, and in my testing didn't provide comparable performance to maxsim.

"कर्मण्येवाधिकारस्ते मा फलेषु कदाचन | मा कर्मफलहेतुर्भूर्मा ते सङ्गोऽस्त्वकर्मणि" - Bhagvad gita, chapter 2, verse 47.

You have a right to perform your prescribed duties, but you are not entitled to the fruits of your actions. Never consider yourself to be the cause of the results of your activities, nor be attached to inaction.

The Info extraction and Question Answering metrics are far worse than transformers though.

They also say that in the blog "However, both Based and Mamba still underperform the strongest Transformer baseline, sometimes by large margins. This is consistent with our “no free lunch” observation above"

Alphacode 2 technical paper claims to solve 43% of problems (77 problems from 12 codeforce competitions) performing 85%ile on all human participants.

Caveat is deep in the technical paper,

1) generates 1m candidates from N different prior models (fine tuned on previous codeforces)

2) throws away 95%ile candidates (doesn't fit the test case + no compile)

3) groups semantically similar candidates

4) scores candidates from each group (Based on another scoring model, probably latency + descriptions etc)

5) Picks top 10

Makes 10 submissions and finally gets the score ..

sure this is how humans solve problems ... totally awed by AGI /s

Hi, Thanks for Open sourcing the code! I was trying to reuse the code especially the dynamic quantization per channel (int8 on gpu) but couldn't get it to work, i also checked out torchao package but it looks like it has dependency on the nightly channel and SAM's dynamic implementation with triton has other issues, is there any clean implementation of int8 dynamic post-training quantization that you can point too ?

Would see great improvments in retrieval accuracy by finetuning e5-base-v2 or the newer leaders on mteb benchmark.

Vespa.ai is pretty crazy too, a bit unkown we run a huge vespa cluster serving 1k+ queries with <100ms latency ...

Hindu - is someone who practices Hinduism and identifies as such, its not a language, that's Hindi.

It's also rudimentary to think in binary that you can either be a programmer or a manager. Progressing in my career i am finding this duality to be incredibly constricting, I excel best at programming, machine learning & working with business, product teams in finding areas of highest impact and then executing with smallish teams but i am increasingly finding that this doesn't fit into most corporate structures and i find resistance in landing the right role.

ML engineer with 10+ years experience, took voluntary break in Sep, started interviewing in Nov, interviewed at 6 companies to land 2 offers.

Most companies have been having hacker-rank pre-screen coding, Hiring Manager Technical screen, System Design, Product round, Behavioral round, ML specific round.

While it's not been hard to land interviews, the process is taking a long time and it looks like people are looking for previous experience in the specific stack they work on. Offers are also lower from a year ago.

Any idea how does it perform compared too Cassandra, Scylla other cloud offering wr.t maintainence, scaling, throughput, cost effectiveness ?