---

name: model-loading-and-export description: "Use for LlamaFactory model/tokenizer/processor loading, model patches, LoRA adapter loading/merge/export, export-time GPTQ quantization, on-the-fly quantization backends, value-head export, checkpoint utilities, and hub/cache/revision/trust settings." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Loading and Export

Use this sub-skill when an agent needs to load, patch, merge, quantize, export, or diagnose LlamaFactory v0 model artifacts for source behavior 0.9.6.dev0.

Route Here For

• model_name_or_path, adapter_name_or_path, tokenizer/processor loading, trust_remote_code, cache_dir, model_revision, and hub tokens.
• LoRA/OFT adapter load, resume, merge, export, create_new_adapter, pissa_init, pissa_convert, value-head/RM checkpoints, and additional_target interactions.
• llamafactory-cli export CONFIG.yaml or lmf export CONFIG.yaml, including adapter merge, full-model export, Ollama Modelfile, tokenizer/processor saving, and hub push settings.
• Quantization choices for PTQ models, QLoRA/on-the-fly training or inference, and export-time GPTQ quantization.
• Model patches and utilities such as vocab resize, RoPE/attention/kv-cache/kernel switches, MoD, Unsloth, Liger, KTransformers, and checkpoint conversion/init planning.

Boundaries

• Route training launch mechanics, dataset sampling, optimizers, distributed training, CLI overrides, and experiment tracking to training-and-configs.
• Route dataset registration, prompt templates, multimodal data schemas, and tokenizer-template fixes to data-and-templates unless the failure is directly in model/tokenizer/processor loading.
• Route chat/API/Web UI/vLLM/SGLang serving behavior to inference-and-serving unless the issue is model loading, adapter, or quantization setup.
• Treat source checkpoint conversion and initialization utilities as reference-only plans unless the user explicitly authorizes large CPU/GPU and disk operations.

Start With

1. Identify the operation: load, train-time adapter setup, export/merge, export quantization, or checkpoint utility.
2. Read references/model-loading.md for loader arguments, tokenizer/processor behavior, patches, adapters, and value-head concerns.
3. Read references/export-and-quantization.md before editing export YAML or choosing a quantization backend.
4. Read references/checkpoint-utilities.md before recommending PiSSA/LoftQ/LLaMA-Pro/DCP/other checkpoint conversions.
5. Read references/troubleshooting.md when errors mention tokenizer/processor loading, remote code, hub access, quantized vocab resize, adapter merge, optional dependencies, or hub mirrors.

Bundled Helper

• scripts/check_export_config.py statically checks YAML/JSON export configs for common LlamaFactory pitfalls without importing LlamaFactory, Transformers, PyTorch, or downloading models.

Safety

• Never run model downloads, adapter merges, export quantization, checkpoint conversion, or hub pushes without explicit user approval and a concrete output path.
• Do not put secrets in config files; prefer environment variables for hub tokens and mirror controls.
• Warn that quantized model merge/export operations are constrained: adapters must be merged before export quantization, and adapters cannot be merged into an already quantized model.