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name: core-training description: "Plan and validate code-first Unsloth Core model loading, LoRA/full finetuning setup, trainer/data preparation, and backend troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Core Training

Use this sub-skill when a user wants a code-first Unsloth Core training plan, import/backend sanity check, model loading choice, LoRA or full-finetuning setup, trainer wiring, chat-template/data preparation, or a tiny configuration/data validation pass.

Route First

• For unsloth command-line orchestration, generated CLI configs, or Studio-free command runs, read ../cli-workflows/SKILL.md.
• For saving adapters, merged checkpoints, GGUF/16-bit export, or Hub upload after training, read ../model-export/SKILL.md.
• For Studio web UI training or Studio backend/API behavior, read ../studio-runtime/SKILL.md.
• Stay here for Python API planning with FastLanguageModel, FastModel, FastVisionModel, FastTextModel, FastSentenceTransformer, UnslothTrainer, chat templates, raw text helpers, and safe preflight checks.

Read Or Run

• Read references/api-reference.md to choose the correct Fast* loader, quantization flags, LoRA targets, trainer classes, and chat/data utility APIs.
• Read references/workflows.md for code-first QLoRA, full-finetuning, vision, sentence-transformer, raw-text, and planning-only workflow skeletons.
• Read references/data-formats.md before shaping JSONL chat, ShareGPT-style, raw text, CSV/JSON, vision, or config files for training.
• Read references/troubleshooting.md when imports, optional dependencies, CUDA/MLX routing, tokenizer mappings, data fields, quantization, downloads, or VRAM choices fail.
• Run scripts/inspect_unsloth_core.py --help to inspect installed Unsloth Core signatures, package/backend availability, and import errors without loading a model.
• Run scripts/validate_training_config.py --help to validate a YAML/JSON training config and optional tiny dataset sample before writing runnable training code.

Safe Defaults

• Import Unsloth before transformers, trl, or peft so Unsloth can patch training paths before those libraries initialize.
• Treat load_in_4bit=True as the QLoRA default for GPU memory savings; set all quantization flags false for full finetuning.
• Use is_bfloat16_supported() or is_bf16_supported() to pick bf16=True versus fp16=True in trainer arguments.
• Validate dataset field names and message roles before calling standardize_sharegpt, get_chat_template, or SFTTrainer.
• Do not run model downloads, training, native notebooks, or expensive examples during planning; generate code/configs and run only safe helper checks first.