SAVIS (Sentence-level Attention VISualization) is a Python library for visualizing inter-sentence attention in large language models (LLMs). This tool enhances the interpretability of LLMs by providing an intuitive visualization of how attention is distributed across sentences in generated text.

• Inter-sentence attention calculation
• Interactive visualization of attention patterns
• Support for various LLMs through Hugging Face's transformers library

pip install savis

from savis import TextGenerator, ISA, ISAVisualization

# Initialize the text generator with your chosen model
generator = TextGenerator("Model name")

# Generate text and get attention data
input_text = "Your input prompt here"
generated_text, attentions, tokenizer, input_ids, outputs = generator.generate_text(input_text)

# Calculate inter-sentence attention
isa = ISA(outputs.sequences[0], attentions, tokenizer)

# Visualize the attention patterns
vis = ISAVisualization(isa.sentence_attention, isa.sentences)
vis.visualize_sentence_attention()

1. TextGenerator: Interfaces with the LLM to generate text and extract attention information.
2. Attention: Manages the underlying LLM and provides methods for obtaining attention data from the model.
3. ISA (Inter-Sentence Attention): Processes raw attention data to compute attention between sentences.
4. ISAVisualization: Creates interactive visualizations of the computed inter-sentence attention.

These components work together to provide a comprehensive pipeline from text generation to attention visualization:

• TextGenerator uses the LLM to generate text based on input prompts.
• Attention handles the interaction with the LLM, extracting detailed attention information.
• ISA takes the raw attention data and computes meaningful inter-sentence attention scores.
• ISAVisualization takes these scores and creates interactive visualizations.

SAVIS is released under the MIT License. See the LICENSE file for more details.