license: cc-by-nc-4.0 language: -en

MistralDarwin-7b-v0.1-GGUF-8bit

Introducing MistralDarwin-7b-v0.1-GGUF-8bit, a highly efficient adaptation of the MistralDarwin-7b-v0.1 model, requiring approximately half the VRAM/RAM of the base model thanks to its 8-bit quantization. Built on the OpenHermes 2.5 architecture, this model excels in delivering Charles Darwin-themed content with enhanced speed and efficiency, ideal for educational and interactive applications.

Model Description

Developed by: phanerozoic
License: cc-by-nc-4.0
Original Model: MistralDarwin-7b-v0.1
Base Model: OpenHermes 2.5
Quantization: 8-bit

Direct Use

Optimized for virtual educational platforms, interactive exhibits, and historical narratives, offering quick and efficient Darwin-themed conversations.

Downstream Use

Ideal for applications requiring a blend of historical context and efficient response generation, such as educational tools or thematic content creation.

Out-of-Scope Use

Not designed for modern scientific research or non-Darwinian contexts. Best suited within its historical and thematic specialization.

Bias, Risks, and Limitations

While it maintains historical accuracy, limitations may arise from its quantization. Not recommended for contemporary scientific dialogue.

Recommendations

Recommended for scenarios prioritizing speed and Darwinian language. Use with caution in non-specialized areas.

Custom Stopping Strings Usage

"},"
"User:"
"You:"
""\n"
"\nUser"
"\nUser:"

These strings are designed to clearly mark the ends of responses, enhancing the structural integrity of dialogues generated by the model.

Training Data and Hyperparameters

Employs the same Darwin-themed dataset as MistralDarwin-7b-v0.1, optimized for quicker processing.

Performance Highlights

Noteworthy for its rapid output of coherent and contextually appropriate Darwinian language. Strikes a balance between linguistic sophistication and operational efficiency.

Compute Infrastructure

Tailored for settings with limited computational resources, focusing on swift processing and minimal resource usage.

Acknowledgments

Special thanks to the Mistral and OpenHermes 2.5 teams for their foundational contributions to the development of this efficient, specialized language model.