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What is Tuning Engines?

Tuning Engines is an all-encompassing AI control and governance framework intended for teams focused on creating production intelligence that incorporates a wide range of models, agents, tools, and specialized systems. This platform brings together the entire AI lifecycle within a unified and regulated space, addressing crucial elements such as inference, model routing, fallback strategies, fine-tuning tasks, datasets, evaluations, model imports and exports, custom models, agents, MCP servers, reusable skills, guardrails, AGT YAML policies, data capture, runtime tracing, usage analytics, API management, billing, team roles, and a variety of integrations. Developers can take advantage of APIs that are compatible with OpenAI, routes that are aligned with Anthropic, as well as CLI workflows, MCP access, and smooth coding-agent integrations, supplemented by an extensive resource catalog for models, agents, tools, and skills. In addition, teams are empowered to connect different AI workflows, including Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and more, all facilitated through a single, governed platform that significantly boosts collaboration and operational efficiency. Ultimately, Tuning Engines not only streamlines the development process but also fosters a collaborative environment where diverse AI applications can thrive.

What is Llama 2?

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

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Integrations Supported

Integrations Supported

AI-FLOW
Aili
Airtrain
Alpaca
Automi
Azure Marketplace
Batteries Included
Featherless
Gopher
Groq
HelpNow Agentic AI Platform
Kiin
Literal AI
Medical LLM
Msty
Ragas
RankGPT
ReByte
Scout
Taylor AI

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

CerebrixOS

Date Founded

2025

Company Location

Canada

Company Website

www.tuningengines.com

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

ai.meta.com/llama/

Categories and Features

AI Governance

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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