Using Starling LM 7B Beta on Cloudflare Workers AI
Implementation guide · Starling · Nexusflow
Quick Start
- 1
- 2Use the Cloudflare Workers AI SDK or REST API to call
starling-lm-7b-beta— 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
No model capability flags are currently sourced.
About Starling LM 7B Beta
Starling LM 7B Beta is an open-source large language model crafted by Nexusflow, leveraging a 7-billion parameter transformer architecture tailored for conversational AI. Fine-tuned with Reinforcement Learning from AI Feedback (RLAIF), it aims to enhance helpfulness and minimize harm. Built on the foundation of the Openchat-3.5-0106 and Mistral-7B-v0.1 models, it utilizes the berkeley-nest/Nectar ranking dataset, Nexusflow/Starling-RM-34B reward model, and Proximal Policy Optimization (PPO) strategy. Achieving an improved MT Bench score of 8.12, its capabilities span engaging conversations, informative responses, and tasks like content and code generation. While it shows strong performance among 7B models, verbose outputs and strict adherence to a provided chat template are notable considerations. Licensed under Apache-2.0 with restrictions against competing with OpenAI, it continues to offer robust functionality within its calibrated framework.