Axolotl vs Vertex AI Gemini tuning

Axolotl

Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU.

Vertex AI Gemini tuning

Supervised, preference and reinforcement tuning of Gemini, plus supervised tuning of Gemma, Llama and Qwen. No weight export. The tuned model exists only as a Google Cloud endpoint.

Axolotl

1. Set AXOLOTL_DO_NOT_TRACK=1 before any command, or training waits 10 seconds and sends usage events to PostHog
2. Run axolotl agent-docs and axolotl config-schema --field <name> before writing a config; both work offline from the installed package
3. Install torch first, then uv pip install --no-build-isolation axolotl[deepspeed], on Python 3.12 or later with PyTorch 2.13 or later
4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys
5. Resume an interrupted run with axolotl train config.yml --resume-from-checkpoint <path>, then axolotl merge-lora and axolotl export only when shipping

Vertex AI Gemini tuning

1. Use client.tunings.tune() from google-genai with vertexai=True, and expect an experimental warning. Tuning isn't available on the Gemini Developer API
2. Add .md.txt to any docs.cloud.google.com URL to read the page as Markdown
3. Tune Gemini 3.5 Flash or 3.1 Flash-Lite. The 2.5 models retire on 2026-10-20
4. List jobs with a filter before re-sending a create after a timeout. There's no request ID to deduplicate it
5. Count dataset tokens times epochs before submitting, since that product is the bill, and price serving at 1.5x base for Gemini 3 tunes