hiyouga/LlamaFactory vs recommenders-team/recommenders
Compare hiyouga/LlamaFactory and recommenders-team/recommenders 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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recommenders-team/recommenders
A repository providing examples, reusable Python utilities, and workflow guidance for building, evaluating, and operationalizing classical and deep learning recommendation systems. It supports researchers and developers through stages from data preparation to model deployment.
- License
- MIT
- Deployment
- Refer to project documentation
- Use cases
- Learning & Education
- Updated
- 2026-07-17T23:00:37Z
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