Llama-Spark-GGUF

QuantFactory/Llama-Spark-GGUF

This is quantized version of arcee-ai/Llama-Spark created using llama.cpp

Original Model Card

<div align="center"> <img src="https://i.ibb.co/9hwFrvL/BLMs-Wkx-NQf-W-46-FZDg-ILhg.jpg" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;"> </div>

Llama-Spark is a powerful conversational AI model developed by Arcee.ai. It's built on the foundation of Llama-3.1-8B and merges the power of our Tome Dataset with Llama-3.1-8B-Instruct, resulting in a remarkable conversationalist that punches well above its 8B parameter weight class.

GGUFs available here

Model Description

Llama-Spark is our commitment to consistently delivering the best-performing conversational AI in the 6-9B parameter range. As new base models become available, we'll continue to update and improve Spark to maintain its leadership position.

This model is a successor to our original Arcee-Spark, incorporating advancements and learnings from our ongoing research and development.

Intended Uses

Llama-Spark is intended for use in conversational AI applications, such as chatbots, virtual assistants, and dialogue systems. It excels at engaging in natural and informative conversations.

Training Information

Llama-Spark is built upon the Llama-3.1-8B base model, fine-tuned using of the Tome Dataset and merged with Llama-3.1-8B-Instruct.

Evaluation Results

Please note that these scores are consistantly higher than the OpenLLM leaderboard, and should be compared to their relative performance increase not weighed against the leaderboard.

<div align="center"> <img src="https://i.ibb.co/pfSGLtB/Screenshot-2024-08-01-at-11-40-42-PM.png" alt="Arcee Spark" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;"> </div>

Acknowledgements

We extend our deepest gratitude to PrimeIntellect for being our compute sponsor for this project.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here