Dolphin 3.0 R1 Mistral 24B Overview

Dolphin 3.0 R1 Mistral 24B is an advanced instruct-tuned model, part of the Dolphin 3.0 Collection, developed by Eric Hartford, Ben Gitter, BlouseJury, and Cognitive Computations. This 24 billion parameter model is engineered to be a versatile, general-purpose local AI, supporting a wide array of applications including coding, mathematical problem-solving, agentic workflows, function calling, and general conversational use cases. It leverages a 32768 token context length, making it suitable for complex tasks requiring extensive context.

Key Capabilities & Differentiators

• Enhanced Reasoning: The R1 version has undergone 3 epochs of training on 800k reasoning traces from the Dolphin-R1 dataset, significantly boosting its general-purpose reasoning abilities.
• User Control & Steerability: Unlike proprietary models, Dolphin emphasizes user control over system prompts and alignment, allowing developers to define ethics and guidelines without external imposition. This ensures data privacy and application-specific customization.
• General Purpose: Designed to function as a comprehensive reasoning instruct model, akin to the capabilities found in leading commercial models like ChatGPT, Claude, and Gemini, but with the advantage of local deployment and user-defined control.
• Optimized for Low Temperature: Experimental observations suggest optimal performance with a low temperature setting (0.05 to 0.1), which helps prevent issues like second-guessing or self-correction.

Training & Data

The model's development benefited from various open-source datasets, including those from OpenCoder-LLM, Microsoft (orca-agentinstruct, orca-math-word-problems), NousResearch (hermes-function-calling), AI-MO (NuminaMath), allenai (tulu-3-sft-mixture), and HuggingFaceTB (smoltalk). The training process also utilized an excellent reward model from RLHFlow for dataset filtering and leveraged Deepseek-V3 for data augmentation.