Using Llama 3.1 8B Instruct on Cloudflare Workers AI

Quick Start

1. 1
   Create an account at Cloudflare Workers AI and generate an API key.
2. 2
   Use the Cloudflare Workers AI SDK or REST API to call @cf/meta/llama-3.1-8b-instruct-fp8 — see the documentation for request format.

Code Examples

About Cloudflare Workers AI

Cloudflare Workers AI is a serverless GPU inference platform enabling developers to run machine learning models on Cloudflare's global edge network. It supports diverse AI tasks including text generation, image classification, automatic speech recognition, and real-time language translation. The platform provides pay-per-use pricing and access to a curated library of open-source models from Hugging Face, enabling rapid deployment without complex infrastructure management. Key features include low-latency edge computing, streaming responses for large language models, context length customization, and the AI Gateway for monitoring, caching, and cost optimization.

Cloudflare is a leading connectivity cloud company that provides a comprehensive suite of cloud-native products and developer tools to enhance web performance, security, and reliability. Their services include content delivery network (CDN), DDoS mitigation, DNS services, and zero trust security solutions. While Cloudflare doesn't primarily market itself as an AI platform, they have incorporated AI and machine learning technologies into various aspects of their services to improve performance and security, including threat detection capabilities, content delivery optimization, and intelligent routing decisions across their global network.

Pricing on Cloudflare Workers AI

Capabilities

About Llama 3.1 8B Instruct

The Llama 3.1 8B Instruct model, released on July 23, 2024, is a multilingual large language model with 8 billion parameters, optimized for instruction-following tasks. It features an enhanced transformer architecture, supporting languages like English, German, French, and others. The model excels in dialogue applications, having been fine-tuned using supervised fine-tuning and reinforcement learning with human feedback. Trained on approximately 15 trillion tokens with a December 2023 data cutoff, it outperforms many existing open-source and closed chat models in various benchmarks. Ideal for commercial and research applications such as conversational agents and content generation, the model can be accessed on Hugging Face .