IBM Research Uses Arcee MergeKit in Granite 4.0 Model Development
MergeKit helped evaluate merged checkpoints and select top performers, reflecting a broader enterprise shift to open models & reproducible tooling.
Today, we have made two important datasets publicly available: 1. Agent Data: This dataset was instrumental in training Arcee-Agent. It contains Salesforce-xlam, agent-flan, and a custom version of Glaive-FC2 with 20k extended samples that call for the model to do tool use sequentially within the same response, along with Magpie-Pro
Today, we have made two important datasets publicly available:
These releases align with our commitment to transparency and collaborative advancement in AI research. By making these datasets accessible, we aim to facilitate further developments.
Researchers and developers interested in exploring these datasets can access them here.
We encourage the community to utilize these resources responsibly and look forward to seeing the innovative applications and insights that may arise from this data.