Eliminate Token Spend
Companies of all sizes are coming into larger and larger token spends, all while their reliance on foundational models is growing. GiiLD will help you run local models and greatly reduce, or eliminate entirely, your dependence on costly tokens.
Four Step Process:
01. Embrace Open Source
Many open source models perform better than foundational models against baseline tests. Furthermore, they offer something the others don’t: open weights. Because they models are open, they can be optimized using GiiLD’s brain surgery methods.
If you are using foundational models for general purposes, many agents will function well against a quality open source model. However, if you have refined your own models, then…
02. Use Your Existing Models as Baselines
GiiLD will use your data to train/refine an open source model until it matches the desired performance of your “baseline” foundational models.
03. Optimize Your New Open Model
Open source models are “open.” This means that we can use our optimization techniques to do brain surgery. Using our methods, we can:
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Improve accuracy above your current baselines
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Prune the model down in size (typically by 50%)
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Remove unwanted data (without retraining)
04. Run Locally & Eliminate Token Spend
Your new open sourced model will now be outperforming the foundational model. And it will be sized so that it can run locally. This allows you to “cut the cord” with the giant, token-toll-booth models.