build LLMs faster, cheaper, and with less data
WHAT WE DO:
stabilize identifies optimal subsets of large training datasets that give equivalent performance, which allows our clients to build LLMs faster, cheaper, and with less data
01
Why Subset?
More Data, More Perfomance. Problems.
LLMs are trained using a lot of data which takes a lot of time, parallel compute and expense. This in turn leads to bottlenecks that limit explorations, slow feature development and add recurring compute expense. stabilize Subsetting addresses these issues by creating subsets of your train sets, which lead to equivalent models as training on all the data.
All Data
Training
Model
RANDOM
All Data
Random Subsetted Data
Training
Model
stabilize
All Data
Stabilize Subsetted Data
Training
Model
02
Why use Stabilize optimized subsets?
Faster, Cheaper, Less Data
01 Reduce Parallel Compute
Small performant train sets lower the need for parallel compute
02 Reduce Cost
Lower compute needs directly imply lower costs
03 More Iteration
Efficient compute usage enables exploration of more ideas
04 Faster Time to Delivering Features
Faster training means more features delivered faster
03
How it works
Give us your dataset and we’ll identify the optimal subset of data that creates equivalent models
Plug and Play.
No code change required for training. Seamlessly integrate with your existing data pipelines and tooling.
Usable and optimal for any model architecture
Use Stabilize subsets to optimally train any neural network architecture
Not dependent on training trajectory
No additional compute or logic required during training
Subset once, use again.
Once you have your subset, use again and again, for any use case.
Cloud agnostic.
Interoperable and enterprise proven
Use Cases
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