LLMs

What is Substrate?

Substrate is an AI inference platform. In particular, it excels at enabling complex multi-model workloads . At its core, Substrate is 1) a collection of cutting-edge AI models – tuned for optimum performance, and 2) a set of composable APIs for relating these models to each other. We believe having both of these components in one... See more

Since we launched ChatGPT Enterprise a few months ago, early customers have expressed the desire for even more customization that aligns with their business. GPTs answer this call by allowing you to create versions of ChatGPT for specific use cases, departments, or proprietary datasets. Early customers like Amgen, Bain, and Square are already... See more

The multiple cantilevered AI overhangs:

Compute overhang. We have much more compute than we are using. Scale can go much further.

Idea overhang. There are many obvious research ideas and combinations of ideas that haven’t been tried in earnest yet.

Capability overhang. Even if we stopped all research now, it would take ten years to digest the new... See more

𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦: it will improve your LLM performance on given use cases (e.g., coding, extracting text, etc.). Mainly, the LLM will specialize in a given task (a specialist will always beat a generalist in its domain)

𝘤𝘰𝘯𝘵𝘳𝘰𝘭: you can refine how your model should behave on specific inputs and outputs, resulting in a more robust product

𝘮𝘰𝘥𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯:... See more

Developers can now generate human-quality speech from text via the text-to-speech API. Our new TTS model offers six preset voices to choose from and two model variants, tts-1 and tts-1-hd . tts is optimized for real-time use cases and tts-1-hd is optimized for quality. Pricing starts at $0.015 per input 1,000 characters. Check out our TTS guide to... See more

First time here? Go to our setup guide

Features

• 🤖 Multiple model integrations: OpenAI, transformers, llama.cpp, exllama2, mamba


• 🖍️ Simple and powerful prompting primitives based on the Jinja templating engine


• 🚄 Multiple choices, type constraints and dynamic stopping


• ⚡ Fast regex-structured generation


• 🔥 Fast JSON generation following a JSON schema

... See more

However development time, and maintenance can offset these savings. Hiring skilled data scientists, machine learning engineers, and DevOps professionals can be expensive and time consuming. Using available resources for “reimplementing” solutions hinder innovation and lead to a lack of focus. Since You not longer work on improving your model or... See more

Document search and synthesis

Scores of organizations want to harness generative AI so employees can easily find the most relevant documents through improved search results and summaries. For example, your organization can reduce the time it takes employees to find answers to common HR- and process-related questions. Internal manuals and sites are... See more

The next-generation command line.

The source of truth for your team’s secrets, scripts, and SSH credentials.