# Autoregressive Model

XGLM-564M is a multilingual autoregressive language model with 564 million parameters, trained on a balanced corpus of 30 languages totaling 500 billion subwords.

Liquid is an autoregressive generation paradigm that achieves seamless fusion of visual understanding and generation by tokenizing images into discrete codes and learning these code embeddings alongside text tokens in a shared feature space.

Lumina-mGPT is a family of multimodal autoregressive models excelling in various vision and language tasks, particularly in generating flexible and realistic images from text descriptions.

Lumina-mGPT is a family of multimodal autoregressive models, excelling in generating flexible and realistic images from text descriptions and capable of performing various vision and language tasks.

Lumina-mGPT is a family of multimodal autoregressive models, excelling in generating flexible and realistic images from text descriptions, and capable of performing various vision and language tasks.

Lumina-mGPT is a series of multimodal autoregressive models, excelling in generating flexible and realistic images from text descriptions.

Code Llama is a series of code generation and comprehension models released by Meta, including pre-trained and fine-tuned versions with parameters ranging from 7B to 34B. This model is the 7B-parameter instruction fine-tuned version, specifically optimized for code assistant scenarios.

Code Llama is a series of code generation and understanding models from Meta with parameter scales ranging from 7B to 34B. This version is the 7B base model.

Code Llama is a 7-billion-parameter Python-specific code generation model launched by Meta, optimized based on the Llama 2 architecture, focusing on Python code synthesis and comprehension tasks

CodeGen2 is a series of autoregressive language models for program synthesis, featuring infill capability and supporting multiple programming languages.

CodeGen is a series of autoregressive language models for program synthesis, pre-trained on Python programming language datasets.

A fine-tuned version of EleutherAI's GPT-Neo 1.3B model, specialized in adventure-style text generation.

XGLM-2.9B is a multilingual autoregressive language model with 2.9 billion parameters, trained on a diverse and balanced corpus of 500 billion subword tokens across multiple languages.

XGLM-1.7B is a multilingual autoregressive language model with 1.7 billion parameters, trained on a diverse and balanced corpus of 500 billion subword tokens.

XGLM-4.5B is a multilingual autoregressive language model with 4.5 billion parameters, trained on a balanced corpus of 134 languages.