Dolphin 3.0 R1 Mistral 24B GGUF
| Property | Value |
|---|---|
| Base Model | Mistral 24B |
| Quantization Options | 28 variants (6.55GB - 94.30GB) |
| Author | Eric Hartford / bartowski (GGUF) |
| Model URL | HuggingFace/cognitivecomputations |
What is cognitivecomputations_Dolphin3.0-R1-Mistral-24B-GGUF?
This is a specialized GGUF conversion of the Dolphin 3.0 model, based on Mistral 24B architecture. It's specifically designed for reasoning and first-principles analysis, offering multiple quantization options to balance performance and resource requirements. The model implements a unique prompt format and specialized system prompt for enhanced reasoning capabilities.
Implementation Details
The model features 28 different quantization variants using llama.cpp's imatrix quantization method. These range from full F32 weights (94.30GB) to highly compressed IQ2_XXS (6.55GB) versions, each optimized for different hardware configurations and use cases.
- Specialized reasoning system prompt format
- Support for rich Markdown formatting and emoji
- Multiple quantization options for various hardware configurations
- Optimized versions for ARM and AVX CPU inference
Core Capabilities
- Advanced reasoning and first-principles analysis
- Structured response format with think/reasoning blocks
- Detailed, scientific-minded explanations
- Flexible deployment options across different hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
The model combines Mistral 24B's capabilities with specialized reasoning abilities and a unique prompt format that enforces structured thinking. It offers an exceptionally wide range of quantization options, making it accessible across various hardware configurations.
Q: What are the recommended use cases?
This model excels in scenarios requiring deep reasoning, analysis, and structured thinking. It's particularly well-suited for complex problem-solving, scientific analysis, and detailed explanations. Users can choose from multiple quantization options based on their hardware capabilities, from high-quality Q6_K_L for optimal performance to lighter versions for resource-constrained environments.
