From 88da8c077af4fae1b65951615ea5b55455796803 Mon Sep 17 00:00:00 2001 From: "Carlos D. Escobar-Valbuena" Date: Mon, 18 Mar 2024 16:17:43 -0500 Subject: [PATCH] Updated docstring for run.py --- run.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/run.py b/run.py index f1e157a..9a06d15 100644 --- a/run.py +++ b/run.py @@ -22,6 +22,27 @@ CKPT_PATH = "./checkpoints/" def main(): + """ + Initializes and runs a text generation model using predefined model configurations and inference settings. + + This function sets up a language model with specific configurations, including model architecture details + (e.g., embedding sizes, number of layers, attention heads, and MoE settings) and text generation settings + (e.g., vocabulary size, token identifiers). It initializes an inference runner with the model, checkpoint + path, tokenizer, and mesh configuration. The inference runner is then used to generate text based on a + given prompt and output the result. + + The process involves: + - Creating a `LanguageModelConfig` instance with specified model parameters, including transformer + configurations and quantization settings for weights. + - Initializing an `InferenceRunner` with the model configuration, batch size per device, checkpoint path, + and other relevant settings. + - Calling the `initialize` method on the inference runner to prepare the model and tokenizer for inference. + - Generating text based on a provided prompt using the `sample_from_model` function, which internally + manages the sampling process through the inference runner. + + Output: + - Prints the generated text continuation for a prompt to the standard output. + """ grok_1_model = LanguageModelConfig( vocab_size=128 * 1024, pad_token=0,