We can finally search for playlists with a giving song! A basic feature that Spotify is missing!
Readit Newsbguberfain commented on Backing up Spotify annas-archive.li/blog/bac... · Posted by u/vitplister
So they used a LLM with knowledge cut in mid 2023 to evaluate 2023? Seems like a classic leakage problem.
From paper: "testing set: January 1, 2023, to December 31, 2023"
From the Llama 2 doc: "(...) some tuning data is more recent, up to July 2023."
I think that there may be another solution for this, that is the LLM write a valid code that calls the MCP's as functions. See it like a Python script, where each MCP is mapped to a function. A simple example:
def process(param1, param2):
my_data = mcp_get_data(param1)
sorted_data = mcp_sort(my_data, by=param2)
return sorted_databguberfain commented on Windows Subsystem for Linux (WSL) github.com/microsoft/WSL... · Posted by u/xnhbx
Part of this excellent movie revolved around the months-long shutdown of the 70-meter antenna at the Deep Space Network station in Canberra, Australia. Coincidentally, the new JPL press release about Voyager 1's thrusters also details a new months-long shutdown (May 2025-Feb 2026) of that same antenna for more upgrades. It's the only antenna that can transmit to Voyager 2, which flew south of the ecliptic after its Neptune flyby. The DSN stations in Spain and California can still transmit to Voyager 1, which flew north of the ecliptic after its Saturn flyby. (Todd Barber, quoted in the The Register article and in JPL's press release, appears in the movie.)
Not available in my country :(
Glad to see that the code[1] is AGPL-3.0 licensed.
Unfortunately, it uses Miniconda, which does not allow usage in companies with more than 200 employees. I think it conflicts with AGPL license. I created a PR to fix that.
bguberfain commented on Gemma 3 Technical Report [pdf] storage.googleapis.com/de... · Posted by u/meetpateltech
Picking model sizes is not an exact science. We look for sizes that will fit quantized on different categories on devices (e.g., low-end and high-end smartphone, laptops and 16GB GPUs, and bigger GPUs/TPUs). We also want the ratio of model width to depth (number of layers) to be consistently around 90, which we found works best.
The models are trained with distillation from a bigger teacher. We train them independently, but for v3 we have unified the recipes for 4B-27B, to give you more predictably when scaling up and down to different model sizes.
Can you provide more information about this “bigger teacher” model?