hiyouga/LlamaFactory vs keras-team/keras
Compare hiyouga/LlamaFactory and keras-team/keras using the current verified snapshot: positioning, license, deployment, use cases, limitations, and original sources.
hiyouga/LlamaFactory
LlamaFactory is a unified framework for fine-tuning large language models and vision-language models through a zero-code CLI and Web UI. It supports a wide range of training algorithms, quantization methods, and inference backends.
- License
- Apache-2.0
- Deployment
- Refer to project documentation
- Use cases
- AI engineers, developers, and researchers who need a unified framework for zero-code model customization and deployment via CLI or Web UI. · Users who need to run parameter-efficient fine-tuning on limited hardware, such as 4-bit QLoRA for a 7B model on 6GB VRAM.
- Updated
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keras-team/keras
A high-level deep learning framework that provides a unified API for building, training, and running models across JAX, TensorFlow, PyTorch, and OpenVINO backends. It is installable via standard Python package managers and designed to scale from local development to datacenter-scale training.
- License
- Apache-2.0
- Deployment
- Refer to project documentation
- Use cases
- Developers, AI engineers, and researchers who need to build, train, and run deep learning models across JAX, TensorFlow, PyTorch, and OpenVINO backends without framework lock-in. · Users who want to scale model training from a laptop to large clusters of GPUs or TPUs.
- Updated
- 2026-07-17T05:50:57Z
How to choose
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