Mu
Multi node training of Llama 70B without crying
Multi node training of Llama 70B without crying
Share cardActual performance
1points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →64%64% predicted probability of success on BetaList, based on ML models trained on real launch data.
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
37%37% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
To
TorchSubmit – Painless multi-node training with PyTorch (no SLURM/K8s)45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
TorchSubmit – Painless multi-node training with PyTorch (no SLURM/K8s)
I
I reproduced Code Llama fill-in-the-middle code completion training72%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I reproduced Code Llama fill-in-the-middle code completion training
Se
Setting up a multi-node database in Elixir with Amnesia67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Setting up a multi-node database in Elixir with Amnesia
Ll
Llama or Alpaca?78%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Llama or Alpaca?
Ll
Llama 3.2 Interpretability with Sparse Autoencoders74%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Llama 3.2 Interpretability with Sparse Autoencoders
no
node-scrunch42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
node-scrunch
Mo
MongoSync for Backbone/Node42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
MongoSync for Backbone/Node
No
Node Knockout 2013 entries28%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Node Knockout 2013 entries
no
node-mongoose-fixtures42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
node-mongoose-fixtures
As
Assert.env: one fell swoop loading and asserting settings in node24%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Assert.env: one fell swoop loading and asserting settings in node