Search Results
LLaMA: Open and Efficient Foundation Language Models
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Ranked #3 on
Few-Shot Learning
on MedConceptsQA
VideoGLUE: Video General Understanding Evaluation of Foundation Models
We evaluate the video understanding capabilities of existing foundation models (FMs) using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring an FM for downstream tasks.
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
To this end, We build a system called \textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
CoCa: Contrastive Captioners are Image-Text Foundation Models
We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively.
Ranked #1 on
Image Classification
on ImageNet
(Top 1 Accuracy metric)
Can Foundation Models Wrangle Your Data?
Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning.
Ranked #11 on
Entity Resolution
on Amazon-Google
Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration
The proposed method is evaluated on multiple speech restoration tasks, including speech denoising, bandwidth extension, codec artifact removal, and target speaker extraction.
Making Large Language Models A Better Foundation For Dense Retrieval
LLaRA consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively.
Cosmos World Foundation Model Platform for Physical AI
In this paper, we present the Cosmos World Foundation Model Platform to help developers build customized world models for their Physical AI setups.
Code Llama: Open Foundation Models for Code
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
Ranked #37 on
Code Generation
on MBPP
(using extra training data)
