aligner/aligner-7b-v1.0
Aligner/aligner-7b-v1.0 is a 7 billion parameter model-agnostic plug-and-play module, trained based on Llama2-Base using a residual correction strategy. It is designed to work with both open-source and API-based models, primarily focusing on enhancing helpfulness and harmlessness. This model serves as a module to improve alignment, rather than a standalone generative language model.
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Overview
Aligner/aligner-7b-v1.0 is a 7 billion parameter model developed as a plug-and-play module, rather than a traditional standalone large language model. It is built upon the Llama2-Base architecture and utilizes a unique residual correction strategy during its training. This approach allows Aligner to be integrated with various existing open-source and API-based models.
Key Capabilities
- Model-Agnostic Integration: Designed to work as an add-on module for diverse LLMs.
- Alignment Enhancement: Focuses on improving the helpfulness and harmlessness of responses from underlying models.
- Residual Correction Strategy: Employs a specific training methodology to achieve its alignment goals.
Good For
- Developers looking to enhance the safety and utility of their existing LLM deployments.
- Integrating alignment capabilities into models without retraining the entire base model.
- Research into modular alignment techniques for large language models.
Limitations
- This model is intended as a module and not a primary generative model.
- It is released under a non-commercial license.
More aligner modules with different parameter counts (7B, 13B, 70B) and trained on larger datasets (20K, 30K, 40K, 50K) are anticipated for future release.