MLX Export

Use posttrainllm export-mlx when a posttrainllm-trained artifact needs to leave the posttrainllm binary and be loaded from Python MLX, MLX-Swift, or another Mac-local tool.

Full distilled or trained checkpoints:

posttrainllm export-mlx path/to/model.tinygpt --out exported-model
python exported-model/mlx_load.py exported-model

Fine-tuned adapters:

posttrainllm export-mlx path/to/adapter.lora --out exported-adapter
python exported-adapter/mlx_load.py exported-adapter

The command writes standard safetensors containers plus sidecars:

model.safetensors for full .tinygpt checkpoints.
adapters.safetensors for .lora / DoRA adapters.
config.json, adapter_config.json, tokenizer sidecars, and posttrainllm_mlx_export.json metadata.
mlx_load.py, a tiny Python MLX helper that loads the arrays and config.

posttrainllm-native byte-level checkpoints are not marked as mlx-lm compatible. Their tensors are MLX-loadable, but a caller still needs a posttrainllm-aware module class to run a forward pass. HF / MLX model directories copied through export-mlx remain mlx-lm compatible when their original architecture is supported by mlx-lm.

Specialist packages

For trained modules that should be shared or routed in an app, pair the MLX export with a specialist package under specialists/<id>/:

model_card.md for the human-facing claim and limitations.
prompt.md for the measured system/developer prompt.
eval_report.json for machine-readable scores and regressions.
tinygpt.lock.json for artifact files, sizes, checksums, base model, and compatibility.
mlx_load.py for cheap metadata validation and optional MLX loading.

The first package is specialists/qwen3-4b-file-ops-distilled: a real fused Qwen3-4B file-ops specialist stored at ~/.cache/posttrainllm/models/mt4b_fused.