use_wandb: False dataset: name: 'dataset' records_path: null initial_dataset: '' label_schema: ["Yes", "No"] max_samples: 10 semantic_sampling: False # Change to True in case you don't have M1. Currently there is an issue with faiss and M1 annotator: method : 'argilla' config: api_url: 'https://kenken999-arglira.hf.space' api_key: '12345678' workspace: 'team' time_interval: 5 predictor: method : 'llm' config: llm: type: 'OpenAI' name: 'llama3-70b-8192' # async_params: # retry_interval: 10 # max_retries: 2 model_kwargs: {"seed": 220} num_workers: 5 prompt: 'prompts/predictor_completion/prediction.prompt' mini_batch_size: 1 #change to >1 if you want to include multiple samples in the one prompt mode: 'prediction' meta_prompts: folder: 'prompts/meta_prompts_classification' num_err_prompt: 1 # Number of error examples per sample in the prompt generation num_err_samples: 2 # Number of error examples per sample in the sample generation history_length: 4 # Number of sample in the meta-prompt history num_generated_samples: 10 # Number of generated samples at each iteration num_initialize_samples: 10 # Number of generated samples at iteration 0, in zero-shot case samples_generation_batch: 10 # Number of samples generated in one call to the LLM num_workers: 5 #Number of parallel workers warmup: 4 # Number of warmup steps eval: function_name: 'accuracy' num_large_errors: 4 num_boundary_predictions : 0 error_threshold: 0.5 llm: type: 'OpenAI' name: 'llama3-70b-8192' temperature: 0.8 stop_criteria: max_usage: 2 #In $ in case of OpenAI models, otherwise number of tokens patience: 10 # Number of patience steps min_delta: 0.01 # Delta for the improvement definition