Axolotl vs Unsloth

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.

Unsloth

The Apache-2.0 core runs on customer hardware and keeps model weights there. Users supply and pay for the GPU.

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

Unsloth

1. Install with uv pip install unsloth --torch-backend=auto on a CUDA machine; the desktop app is for people
2. Start from the notebook for the model family in unslothai/notebooks; it sets LoRA targets and the chat template
3. Save the LoRA adapter while iterating and merge to 16-bit or GGUF only when you ship
4. If Studio must be reachable by other agents, pass --disable-tools and keep it on 127.0.0.1 behind a tunnel
5. Pin the exact unsloth version; releases land several times a week and don't flag breaking changes