llamafactory

# LlamaFactory Fine-Tuning LlamaFactory fine-tunes open-weight LLMs (LoRA/QLoRA and full-parameter; SFT, DPO and more) through the `llamafactory-cli` command driven by YAML configs. ## Before you start If the user's message only invokes this skill (e.g. "use llamafactory skill") without a concrete request, ask the user what they want to fine-tune. Do not run any command until the goal is clear. Confirm before training: - GPU memory (`nvidia-smi`) — it bounds the model size and method; LoRA needs far less than full fine-tuning. - The base model: a Hugging Face id or a local path. - The dataset: where it lives and which format it is in. - The goal: SFT with LoRA is the usual starting point. ## Install ```bash git clone --depth 1 https://github.com/hiyouga/LlamaFactory.git cd LlamaFactory pip install -e . pip install -r requirements/metrics.txt # optional: evaluation metrics ``` ## Data Register every dataset in `data/dataset_info.json`; the alpaca and sharegpt formats are supported. A minimal local entry: ```json "my_dataset": { "file_name": "my_dataset.json" } ``` alpaca rows carry `instruction` / `input` / `output`; sharegpt rows carry a `conversations` list. Put the data file under `data/` next to the registry. ## Train Training is driven by a YAML config. Start from the shipped example `examples/train_lora/qwen3_lora_sft.yaml`, or save a minimal config as `my_sft.yaml`, e.g. for [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B): ```yaml model_name_or...