This repository provides a collection of deep learning models and utilities optimized for Cerebras hardware, targeting researchers and engineers who need to train and deploy models efficiently on Cerebras systems. It offers reference implementations, configuration files, and tools to streamline workflows, enabling faster development and deployment of advanced AI models.

How It Works

The ModelZoo leverages a comprehensive Command-Line Interface (CLI) as a single entry point for all tasks, including data preprocessing, model training, and validation. It includes optimized reference implementations and configuration files for a wide range of NLP, vision, and multimodal models like Llama, Mixtral, and DINOv2. The system supports advanced training optimizations such as custom training loops, custom model implementations, and sequence length scaling techniques like rotary position embedding (RoPE) scaling.

Quick Start & Requirements

• Installation requires following the steps in the setup guide.
• Access to Cerebras Hardware is required.
• Refer to developer documentation for comprehensive guides.

Highlighted Details

• Includes reference implementations for numerous popular models (Llama, Mixtral, DINOv2, LLaVA, etc.).
• Provides tools for checkpoint conversion (Cerebras ↔ HuggingFace) and PyTorch model porting.
• Supports advanced training optimizations like µParam (μP) scaling and RoPE scaling.
• Features a CLI for streamlined data preprocessing, model training, and validation.

Maintenance & Community

The project is maintained by Cerebras. Further community and roadmap details are not explicitly provided in the README.

Licensing & Compatibility

• License: Apache License 2.0.
• Compatibility: Designed specifically for Cerebras hardware. Commercial use is permitted under the Apache 2.0 license.

Limitations & Caveats

This ModelZoo is specifically optimized for Cerebras hardware, implying limited utility or performance on non-Cerebras systems. Access to Cerebras hardware is a prerequisite for utilizing the full functionality of the repository.