dolphin-2.5-mixtral-8x7b-GGUF

Maintained By
TheBloke

Dolphin 2.5 Mixtral 8x7B GGUF

PropertyValue
Parameter Count46.7B
Model TypeMixtral
LicenseApache 2.0
Context Length32K tokens

What is dolphin-2.5-mixtral-8x7b-GGUF?

Dolphin 2.5 Mixtral is an advanced language model based on the Mixtral-8x7B architecture, specifically optimized for coding tasks and general-purpose applications. It represents a significant evolution in the Dolphin series, incorporating multiple expert models and extensive training on diverse datasets including coding, instruction-following, and general knowledge.

Implementation Details

The model is available in various GGUF quantization formats, from 2-bit to 8-bit precision, allowing users to balance between model size and performance. The model utilizes a mixture of experts architecture with 8 expert models, trained using qLoRA and Axolotl over 1.5 epochs on 4 A100 GPUs.

  • Multiple quantization options (Q2_K to Q8_0) for different hardware capabilities
  • ChatML prompt format for consistent interaction
  • Extensive training on 8 specialized datasets
  • 32K context window support

Core Capabilities

  • Advanced coding assistance and generation
  • Highly compliant instruction following
  • Extended context understanding
  • Efficient resource utilization through GGUF format
  • Balanced performance across general and specialized tasks

Frequently Asked Questions

Q: What makes this model unique?

The model combines Mixtral's powerful architecture with specialized training on coding and instruction-following datasets, making it particularly effective for development tasks while maintaining strong general-purpose capabilities. The GGUF format enables efficient deployment across different hardware configurations.

Q: What are the recommended use cases?

This model excels in software development tasks, technical writing, and general assistance scenarios. It's particularly well-suited for users who need both coding expertise and general knowledge handling, with flexible deployment options through various quantization levels.

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