The Claim
A version of this claim appears frequently in AI development communities: that Hermes Agent, because it runs locally, somehow learns from your data over time and gets better at your specific use cases.
What Hermes Actually Does
Hermes Agent is an inference framework. You load a pre-trained model — a llama.cpp model, an Ollama model, or another compatible format — and Hermes executes that model locally against your prompts and tool calls.
The model weights do not change during inference. Hermes is running the math of an existing model on your hardware. It is not modifying that model.
What 'Local Execution' Actually Means
Local execution means the computation happens on your hardware. Your data does not travel to an external API. This is a meaningful privacy and security benefit.
Local execution does not mean local training. Training changes model weights. Inference uses existing weights. Hermes does inference.
Where the Confusion Comes From
Two sources:
1. Agent memory systems: Hermes supports memory tools — agents can write context to local storage and retrieve it later. This is application-level persistence, not model training. The memory content is injected into context windows, not baked into weights.
2. Fine-tuning marketing: Some AI products do offer local or private fine-tuning. Hermes is not one of them. When you see 'gets smarter over time', that typically refers to fine-tuning pipelines, which are complex, expensive, and explicitly not what Hermes does.
What to Do If You Actually Want Local Fine-Tuning
Local fine-tuning is possible but requires significant infrastructure: GPU memory (typically 24GB+ VRAM for meaningful models), a fine-tuning pipeline, and data preparation work. Tools like Axolotl, Unsloth, and the LLaMA Factory handle the training side. This is a fundamentally different project from running inference with Hermes.