{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"LLaMA Factory provides a unified framework for fine-tuning 100+ large language models through CLI and web UI, supporting multiple training methods and model architectures with minimal setup.","skillfed_tags":["llm-training","model-adaptation","parameter-efficient-tuning"],"use_cases":["Fine-tune a Qwen or Llama model on domain-specific instruction data using LoRA to reduce memory footprint","Adapt a vision-language model like LLaVA for custom image understanding tasks via the web UI","Run reward modeling and DPO training on a base model to align outputs with human preferences","Quantize and fine-tune a large model with QLoRA to fit on consumer GPUs","Deploy a fine-tuned model with OpenAI-compatible API endpoints using vLLM integration"],"what_it_does":"LLaMA Factory is a unified framework for fine-tuning large language models, designed to reduce the friction of adapting pre-trained models to specific tasks or domains. It abstracts away boilerplate training code and provides both command-line and web UI entry points, letting users configure training runs through YAML or the Gradio interface without writing custom training loops. The package bundles support for multiple training paradigms\u2014supervised fine-tuning, reward modeling, reinforcement learning methods like DPO and PPO, and quantization-aware training\u2014across a broad range of model families.\n\nThe package depends on a substantial ecosystem: PyTorch, Hugging Face Transformers, PEFT for parameter-efficient tuning, and supporting libraries for data handling, optimization, and inference. It integrates with experiment tracking tools (Weights & Biases, MLflow, SwanLab), model hubs (Hugging Face, ModelScope), and inference servers (vLLM, SGLang). The framework targets researchers and practitioners who want to customize LLMs without managing low-level training infrastructure, and it is actively maintained with support for newly released model architectures.","worth_installing":"Yes, if you need to fine-tune LLMs without writing training code. The framework is actively maintained, has no known vulnerabilities, and is used by major organizations (Amazon, NVIDIA, Aliyun). The Apache-2.0 license is permissive. Install friction is low, but you must have Python 3.11+, GPU resources (or significant CPU), and be comfortable with the 31-dependency stack. Start with the free Colab notebook or PAI-DSW trial to evaluate before committing to local setup."},"id":"llamafactory","links":{"html":"https://skillfed.io/packages/llamafactory","md":"https://skillfed.io/packages/llamafactory.md","pypi":"https://pypi.org/project/llamafactory/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-30","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"llamafactory","python_support":"supports_current","summary":"Unified Efficient Fine-Tuning of 100+ LLMs"},"popularity":{"monthly_downloads":101638,"position":12922,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.5"}