model-training-expert

To configure LoRA adapters for a 7B LLM training workflow, provide an end-to-end training objective to initiate a full setup. This enforces modular backbone choices and hot-swapping without reloading base models.

Synthetic data evolution improves model training through workflows spanning draft generation to verification, correction, and dataset expansion. It leverages teacher-model-based failure analysis to recursively generate and refine training data.

Setting up a Hierarchical MoE for domain-specific specialization involves configuring adapter strategies using LoRA. This approach enables hot-swapping specialized modules without reloading the base model.

Yes, you can run agentic evaluation workflows in a sandboxed environment using Mesen2 emulation. This leverages Agahnim and HAFS, providing multi-modal ingestion guidance for technical papers to support tooling and experiments.

Yes, the model training workflow supports deployment on Vast.ai. You can initiate a full setup for a 7B LLM using LoRA adapters directly on the platform to accelerate research pipelines.

The best way to evaluate AI models using agentic workflows is through AgE leveraging Agahnim and HAFS. This provides sandboxed emulation and multi-modal ingestion guidance for technical papers to validate tooling.