What it is
Fine-tuning is the bridge between a generic foundation model and the operator’s actual product. Axolotl (Wing Lian) is the YAML-driven post-training framework most labs reach for. LLaMA-Factory is the all-in-one Chinese-ecosystem fine-tuner with a polished WebUI. Unsloth makes LoRA fine-tuning faster and cheaper on consumer GPUs. torchtune (PyTorch) is Meta’s official lightweight option. TRL (Hugging Face) provides the underlying RLHF/DPO trainers. Together they are where the operator’s training data, base model choice, and tuning recipe live.
What goes wrong
A fine-tuning host is a workstation with the operator’s training corpus on local disk, their Hugging Face token in the environment, their base model weights downloaded to a local cache, and their output adapter weights in a results directory. LLaMA-Factory’s WebUI ships without auth on first boot; Axolotl jobs running under tmux/screen leave the dataset filename visible in the process list of any reachable monitoring endpoint. The exposure is the operator’s entire training strategy: what data, what base model, what hyperparameters, what they’re trying to teach the model to do.
How we test
We probe LLaMA-Factory’s WebUI for the version banner and the recent-jobs endpoint, Axolotl’s prometheus metrics for active runs, and any job scheduler integration that surfaces the dataset path. Job names and dataset filenames tell the story without our needing to read training data.