Direct Access LLMs
Overview
Datasaur offers Direct Access LLMs, a new feature that allows users to instantly access and call the most popular Large Language Models (LLMs) within the platform. This feature eliminates the need for complex API key setup and multi-cloud configurations. Additionally, users can cut wait lines and immediately access the latest state of the art models.
Supported Providers
Datasaur's Direct Access LLM feature currently supports Azure, OpenAI, Amazon Bedrock and Google Vertex, each offering a unique set of LLM models. Below, we'll delve into the details of each provider and the models they offer.
Azure OpenAI
With Azure OpenAI, users can utilize the following models:
gpt-4o: A highly advanced model boasting an expansive knowledge base for richer and more comprehensive responses.
gpt-4-32k: A variant of the gpt-4 model, with greater capacity to handle longer inputs.
gpt-4 turbo: A high-performance model optimized for speed and efficiency.
gpt-4: A powerful model offering advanced language understanding and generation capabilities.
gpt-35-turbo-16k: A variant of the gpt-35-turbo model, with greater capacity to handle longer inputs.
gpt-35-turbo: A fast and efficient model ideal for applications requiring rapid response times.
OpenAI
With OpenAI, users can utilize the following model:
gpt-4o-mini: A streamlined and efficient version of the advanced gpt-4o model, designed to deliver rich responses while requiring less computational power.
gpt-4o: A highly advanced model boasting an expansive knowledge base for richer and more comprehensive responses.
gpt-4 turbo: A high-performance model optimized for speed and efficiency.
gpt-4: A powerful model offering advanced language understanding and generation capabilities.
gpt-35-turbo-16k: A variant of the gpt-35-turbo model, with greater capacity to handle longer inputs.
gpt-35-turbo: A fast and efficient model ideal for applications requiring rapid response times.
Amazon Bedrock
With Amazon Bedrock, Datasaur is able to provide several Open Source Models, such as:
Claude 3.5 Sonnet: An enhanced version of Claude 3 Sonnet, with updated knowledge and improved reasoning capabilities.
Claude 3 Sonnet: A more verbose Claude model, offering deeper analysis and extended conversations.
Claude 3 Opus: The most comprehensive Claude model, providing in-depth expertise across a wide range of subjects.
Claude 3 Haiku: A concise and efficient AI assistant, perfect for brief, focused interactions.
Claude 2.1: An updated version of Claude 2.0, featuring refinements in language understanding and generation.
Claude 2.0: An upgraded Claude model with expanded knowledge and improved conversational abilities.
Claude Instant: A rapid-response AI assistant for quick, concise interactions.
Meta Llama 3 70b Instruct: A variant of the Meta Llama 3 8b Instruct model, with increased capacity and performance.
Meta Llama 3 8b Instruct: A newer model optimized for instruction-following tasks, offering high accuracy and reliability.
Meta Llama 2 Chat 70B: A variant of the Meta Llama 2 Chat 13B model, with increased capacity and performance.
Meta Llama 2 Chat 13B: A highly advanced model designed for conversational AI applications.
Mistral Large: A more expansive version of Mistral, offering deeper knowledge and more nuanced interactions.
Mixtral 8x7B Instruct: An advanced instruction-following model combining multiple expert systems for enhanced performance.
Mistral 7B Instruct: A compact yet powerful model designed for following instructions with precision.
Mistral Small: A nimble AI assistant optimized for quick responses and everyday tasks.
Command R+: The most advanced Command model, featuring superior problem-solving and creative abilities.
Command R: An enhanced version of Command, with improved reasoning and analytical skills.
Command: A versatile AI assistant balancing speed and capability for various applications.
Command Light: A streamlined AI model for efficient, straightforward task completion.
Amazon Titan Text Premier: Amazon's most advanced text AI, offering sophisticated language understanding and generation.
Amazon Titan Text Express: A mid-range AI assistant balancing efficiency and capability for various text-based applications.
Amazon Titan Text Lite: A lightweight AI model for basic text processing and generation tasks.
Vertex AI
With Vertex AI, users can utilize the following models:
Gemini 1.5 Pro: A high-performance model offering advanced language understanding and generation capabilities.
Gemini 1.5 Flash: A variant of the Gemini 1.0 Pro model, optimized for speed and efficiency.
Gemini 1.0 Pro: A highly advanced model offering exceptional language understanding and generation capabilities.
Getting Started with Direct Access LLM
To get started with Direct Access LLM, simply navigate to the Datasaur LLM Labs platform and create a new LLM Playground.
Once the LLM Playground is created, you just have to choose your desired provider by clicking the models button.
Application configuration
You can configure your application with your desired settings. Several parameters that you can adjust based on your needs are:
Temperature: This parameter controls the randomness of the generated text. A higher temperature will result in more diverse and creative responses, while a lower temperature will produce more focused and predictable responses.
Top P: This parameter, also known as nucleus sampling, controls the cumulative probability threshold for token selection. A higher Top P value will result in more focused and relevant responses, while a lower Top P value will allow for more diverse and unexpected responses.
Maximum length: This parameter sets the maximum number of tokens that will be generated in the response. A higher maximum length will allow for longer and more detailed responses, while a lower maximum length will result in shorter and more concise responses.
Similarity score: This parameter controls how closely the generated text matches the original prompt in terms of content and style. A higher similarity score will result in responses that more closely align with the prompt, while a lower similarity score will allow for more divergent responses.
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