Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested. The implementation of this function is mod

Generates a standardized message to prompt the model with an input and a 'response' field.

For compatibility with base checkpoints.

For compatibility with base checkpoints.

Return default precision that is supported by the hardware: either `bf16` or `16`. Args: training: `-mixed` or `-true` version of the pre

Reset all the weights, even including pretrained ones.

An extension of the `torch.nn.functional.conv1d` function with a logic specific to grouped queries. If the number of heads is equal to the nu

Function for getting a batch of data Args: fabric: Lightning Fabric object data: Data longest_seq_ix: Index of th

This function gets a batch of data. Args: fabric (Fabric): Fabric object. data (List[Dict]): Data to get a batch from.

This function is derived from the original file provided by the LIT team: Args: model: The model to use. input_pos: Tensor o

Parse entities from a record in the data. Args: - record: A dictionary representing a single record in the data. - key: The key

Processes a single sample. Each sample in the dataset consists of: - input: A string containing adverse event description (email) - outpu

This function saves the adapter v2 checkpoint. Args: fabric (Fabric): Fabric object. model (torch.nn.Module): The model to s

Function for saving the LoRA checkpoint Args: fabric: Lightning Fabric object model: GPT model file_path: Path to th

Function for validating the model on the entity extraction task using LoRA Args: fabric: Lightning Fabric object model: GPT

Finetune the adapter v2 model on the entity extraction task. This function validates the model. Args: fabric (Fabric): Fabric object

Properly pad weight updates with zeros. If, based on `self.enable_lora`, we want to fine-tune queries and values, but not keys, then

Enhanced Transformer with Rotary Position Embedding. Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/

Calculate precision and recall from the data. Args: - data: A list of dictionaries, each containing 'output' and 'prediction'. Retu

Create a dataset of Adverse Event Reports for the Drugs using the OpenAI Chat Completions API. Args: folder_path (str): Path to the

Create a prompt for the OpenAI API using the drug name and the drug report. Args: drug_name (str): Name of the drug drug_rep

Downloads the raw json data file and saves it in the given destination.

Extracts the section with the given ID from the HTML content. Args: soup (BeautifulSoup): BeautifulSoup object containing the HTML

Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested. The implementation of this function is mod

Generate Adverse Event Reports for the Drug using the OpenAI API. Args: prompt (str): Prompt for the OpenAI API Returns:

This function is used to generate predictions using the fine-tuned adapter models. It loads the model and generates and prints a sample predi

This function is used to generate predictions using the fine-tuned LoRA models. It loads the model and generates and prints a sample predicti

This function is used to generate predictions using the fine-tuned adapter models. It loads the model and generates and prints a sample predi

Generate train and test data from the JSON files in the given folder path. Args: folder_path (str): Path to the folder containing JS

Function for getting the longest sequence length Args: data: Data Returns: longest_seq_length: Longest sequence length

This function gets the longest sequence length. Args: data (List[Dict]): Data to get the longest sequence length from. Returns:

Sets requires_grad=False for all non-adapter weights

Freeze all modules except LoRA's and depending on 'bias' value unfreezes bias weights. Args: model: model with LoRA layers bias:

Merges the LoRA weights into the full-rank weights (W = W + delta_W).

Merge LoRA weights into the full-rank weights to speed up inference.

This function is derived from the original file provided by the LIT team: Args: probs: Tensor of shape (..., N) containing probabili

This function is derived from the original file provided by the LIT team: Args: logits: Tensor of shape (..., N) containing logits f

Function for training the model on the entity extraction task using LoRA Args: fabric: Lightning Fabric object model: GPT mo

Finetune the adapter v2 model on the entity extraction task. This function trains the model. Args: fabric (Fabric): Fabric object.