Using Llama 3 8B Instruct on Cloudflare Workers AI
Implementation guide · Llama 3 · AI at Meta
Quick Start
- 1
- 2Use the Cloudflare Workers AI SDK or REST API to call
llama3-8b-instruct— 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 8B Instruct
The Llama 3 8B Instruct model, released on April 18, 2024, is Meta's latest instruction-following language model with 8 billion parameters. It utilizes an auto-regressive transformer architecture with Grouped-Query Attention for improved scalability. Trained on over 15 trillion tokens and fine-tuned with 10 million human-annotated examples, it excels in dialogue and conversational tasks. The model outperforms its predecessors on industry benchmarks, scoring 68.4 on MMLU (5-shot). Designed for commercial and research applications, it prioritizes safety and helpfulness, making it suitable for chatbots, virtual assistants, and other interactive AI applications. For more details, visit the Hugging Face page [1].