This video was sponsored by and produced on behalf of Crusoe.
Full fine-tuning a 7B model can push your VRAM requirement past 80GB. LoRA brings that down to 20-30GB by training a lightweight adapter instead of touching the base weights. And with Serverless Fine-Tuning in Crusoe Intelligence Foundry, you skip the cluster provisioning entirely.
In this video, Caleb walks through the full model lifecycle — what fine-tuning actually is, how LoRA works under the hood, when to reach for prompting vs. context vs. fine-tuning, and how to go from a JSONL training file to a deployed API endpoint in Crusoe Intelligence Foundry.
Key concepts:
– LoRA: low-rank adaptation; trains a compact adapter, freezes base weights
– JSONL / Parquet: training data formats; implicit learning from examples vs. explicit prompt instructions
Serverless Fine-Tuning: no instance provisioning, no VRAM math, no Kubernetes setup
– Crusoe Intelligence Foundry: where you run the job, view checkpoints, and deploy the model
Try Serverless Fine-Tuning: https://console.crusoecloud.com/request-foundry
Crusoe Intelligence Foundry docs: https://docs.crusoecloud.com/managed-inference
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