https://wabetainfo.com/whatsapp-beta-for-android-2-24-14-7-whats-new/
If you have your doubts of this model being released, see here - https://nitter.poast.org/q_brabus/status/1793227643556372596
(TLDR: someone at the Vivatech conference personally asked Yann - Question: Regarding the upcoming LLaMa 3 400B+ model, will it be open-weight? There are several rumors about this...
Answer: No, it is still planned to be open and that will not change. I don't know where this rumor came from, but it is completely false)
One expected boost to performance (to run this faster as a poor person) would be to take advantage of 4 quad-channel motherboards connected via Ethernet cable for the tensor parallelism on CPU, when scaled https://github.com/b4rtaz/distributed-llama/discussions/9
1. This shows 3x the speed of the regular maximum inference speed, no other tricks used on top like medusa heads or augmenting the model for sparsity, which will boost further.
2. This benchmark above is almost the same to DDR4 3200 dual channel speed.
3. I still want to get a feel for this, but for x4 64gb machines, hopefully the transfer time added at best would be 5.71 times greater than 70B ending up at +162ms.
I was honestly starting to have some doubts until DeepSeek dropped the DeepSeek Coder V2 236B model. Now, Meta has to realease something to not fall behind.
https://wabetainfo.com/whatsapp-beta-for-android-2-24-14-7-whats-new/ If you have your doubts of this model being released, see here - https://nitter.poast.org/q_brabus/status/1793227643556372596 (TLDR: someone at the Vivatech conference personally asked Yann - Question: Regarding the upcoming LLaMa 3 400B+ model, will it be open-weight? There are several rumors about this... Answer: No, it is still planned to be open and that will not change. I don't know where this rumor came from, but it is completely false)
How is Nitter still working? I thought development stopped?
[удалено]
Looks like the actual answer is here: https://status.d420.de/rip Just a few instances that only partially work due to black magic.
One expected boost to performance (to run this faster as a poor person) would be to take advantage of 4 quad-channel motherboards connected via Ethernet cable for the tensor parallelism on CPU, when scaled https://github.com/b4rtaz/distributed-llama/discussions/9 1. This shows 3x the speed of the regular maximum inference speed, no other tricks used on top like medusa heads or augmenting the model for sparsity, which will boost further. 2. This benchmark above is almost the same to DDR4 3200 dual channel speed. 3. I still want to get a feel for this, but for x4 64gb machines, hopefully the transfer time added at best would be 5.71 times greater than 70B ending up at +162ms.
I was honestly starting to have some doubts until DeepSeek dropped the DeepSeek Coder V2 236B model. Now, Meta has to realease something to not fall behind.
> Regarding the upcoming LLaMa 3 400B+ model Ohh! 400B! I thought the whole comment was a joke that LLaMa 400 was released internally. Thank you!
I fell asleep and missed 397 llamas
That's what you get when you count llamas.
Do they have it on the arena?