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After the 7-step LoRA training run finishes and the in-memory inference assertion passes, the smoke test now exports the trained model in three formats, drops the in-memory model + trainer to reclaim memory, and reloads each export from disk to re-run the "<<HELLO!!>> My name is " inference assertion. Each reload is expected to still complete with "Unsloth" -- catching round-trip regressions where the saved weights silently corrupt or fail to load. Formats exercised: - LoRA adapter via model.save_pretrained_merged(save_method="lora"). Reloaded with FastMLXModel.from_pretrained on the adapter dir; the loader auto-detects adapter_config.json and pulls down the base model. - Merged 16-bit via model.save_pretrained_merged(save_method= "merged_16bit"). Fuses LoRA into the base, dequantizes to fp16, saves an HF-compatible safetensors directory. Reload via FastMLXModel.from_pretrained on the saved dir. - GGUF via model.save_pretrained_gguf(quantization_method= "not_quantized"). Builds llama.cpp via cmake on the runner with GGML_METAL=ON (only the llama-cli, llama-quantize, and llama-gguf-split targets), then runs the produced bf16 GGUF through llama-cli with a fixed seed and asserts "Unsloth" in stdout. GGUF infra failures (cmake / build / convert) are surfaced as RuntimeError so we notice -- if Mac CI starts hitting build flakes the assertion can be softened. Workflow timeout bumped 15 -> 25 min to budget for the llama.cpp cmake build (~5-7 min on the macos-14 standard runner). |
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