Miscellaneous code made available for purposes of education, reproducibility, and transparency
| Example | Description | |
|---|---|---|
| 1 | few_shot_prompt_selection | Notebook showing how to clean few-shot examples pool to improve prompt template for OpenAI LLM. |
| 2 | generate_llm_response | Notebook showing how to generate LLM responses for customer service requests using Llama 2 and OpenAI's API. |
| 3 | fine_tuning_data_curation | Notebook showing how to use Cleanlab TLM and Cleanlab Studio to detect bad data in instruction tuning LLM datasets. |
| 4 | gpt4-rag-logprobs | Notebook showing how to obtain logprobs from a GPT-4 based RAG system. |
| 5 | fine_tuning_mistral_beavertails | Analyze human annotated AI-safety-related labels (like toxicity) using Cleanlab Studio, and thus generate safer responses from LLMs. |
| 6 | Evaluating_Toxicity_Datasets_Large_Language_Models | Notebook on analyzing toxicity annotations in the Jigsaw dataset using Cleanlab Studio. |
| 7 | time_series_automl | Notebook showing how to model time series data in a tabular format and use AutoML with Cleanlab Studio to improve out-of-sample accuracy. |