vicgalle/distilled-self-critique
distilled Self-Critique refines the outputs of a LLM with only synthetic data
This helps AI developers and researchers refine the outputs of large language models (LLMs) to better align with specific safety, sentiment, or privacy goals. It takes an existing LLM and synthetic training data, then produces a fine-tuned LLM that generates responses more reliably following desired criteria, like avoiding harmful content or maintaining a positive tone. This is for those building or deploying LLM-powered applications.
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Use this if you need to improve the reliability and control over an LLM's output for specific behaviors (e.g., safety, sentiment) without requiring extensive human-annotated feedback data.
Not ideal if you are looking for a general-purpose LLM alignment solution that doesn't involve fine-tuning or if your primary concern isn't related to output refinement via synthetic data.
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Jupyter Notebook
License
MIT
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Last pushed
Apr 11, 2024
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