EvolvingLMMs-Lab/Otter
Otter
Otter is a framework and toolkit for the pretraining, fine-tuning, and evaluation of vision-language models. It provides a pipeline for training large language models to process high-resolution images and video frames, integrating visual encoders with textual token spaces.
The system is designed for multi-visual input processing, allowing models to interpret multiple images or video sequences within a single prompt. It supports multi-round conversation management to maintain context across interactions for detailed scene comprehension and visual reasoning.
The framework covers a full development lifecycle, including foundational pretraining, supervised fine-tuning, and visual instruction tuning. It also includes a dedicated evaluation suite to measure reasoning accuracy and performance when processing combined visual and textual data.
Features
- Training Frameworks - Provides a comprehensive framework for pretraining and fine-tuning vision-language models to process high-resolution images and video.
- Scene Comprehension - Enables detailed scene comprehension by analyzing multiple images or video sequences within a single conversation.
- Visual-Textual Alignments - Maps visual encoder embeddings into the textual token space using a learned projection layer for unified multimodal processing.
- Instruction Tuning Pipelines - Ships automated workflows to format high-resolution image and text pairs into conversational templates for supervised fine-tuning.
- Multimodal - Performs in-context instruction tuning to help models interpret high-resolution images, videos, and text using provided examples.
- Multimodal Fine-Tuning - Adapts pretrained multimodal models to specific downstream tasks to improve targeted visual and text interpretations.
- Multimodal Encoders - Integrates pretrained visual backbones with large language models to interpret spatial information as semantic tokens.
- Vision-Language Pipelines - Provides end-to-end workflows for training vision-language models that maintain context across multi-turn conversations.
- Visual Instruction Tuning - Offers a toolkit for adapting pretrained models to follow complex visual and textual instructions.
- Multimodal Pretraining - Trains models on specialized datasets to establish foundational understanding of high-resolution visual and textual inputs.
- Multi-Visual Context Processing - Interprets multiple images or video frames within a single prompt to follow instructions spanning different visual contexts.
- Multimodal Reasoning Evaluations - Measures reasoning accuracy and performance when processing combined visual and textual data.
- Multi-turn Interaction Managers - Manages stateful, multi-round interactions to maintain context for complex visual reasoning and scene comprehension.
- Cross-Modal Context Management - Provides a shared memory buffer of image tokens and text embeddings to enable reasoning across multiple visual inputs.
- In-Context Learning Engines - Implements few-shot demonstration pairs within prompts to guide model output formats without updating parameters.
- Representative Frame Sampling - Processes video content by extracting discrete representative frames to treat temporal data as a series of visual inputs.
Star history
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