Axolotl vs Tinker

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.

Tinker

Full control of the training loop with the GPUs abstracted away, plus recipes for SFT, DPO, RL and distillation. LoRA only; no full-parameter training.

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

Tinker

1. Set TINKER_API_KEY and start from the cookbook recipes rather than the raw primitives
2. Read the 'Avoid Client-Side Timeouts and Retries' guide before wrapping sampling calls in your own retries; the SDK already retries sampling with stable request IDs
3. Save intermediate checkpoints with a TTL between 1 hour and 10 years; storage bills at $0.10 a GB-month until they expire
4. Read models.json for current prices before a run; sampling tokens cost more than training tokens on the open models
5. Check the model deprecations page before pinning a base model; 18 were retired on 2026-06-12