parth-1/metaguard-policy-agent-v1
The parth-1/metaguard-policy-agent-v1 is an 8 billion parameter Llama 3.1 instruction-tuned causal language model developed by parth-1. This model was fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process. It is designed for general language understanding and generation tasks, leveraging the Llama 3.1 architecture for efficient performance.
Loading preview...
Model Overview
The parth-1/metaguard-policy-agent-v1 is an 8 billion parameter instruction-tuned language model based on the Llama 3.1 architecture. Developed by parth-1, this model was fine-tuned using the Unsloth library in conjunction with Huggingface's TRL library. A key characteristic of its development is the reported 2x faster training time achieved through the use of Unsloth.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/llama-3.1-8b-instruct. - Parameter Count: 8 billion parameters.
- Training Efficiency: Utilizes Unsloth for accelerated training, claiming a 2x speed improvement.
- Context Length: Supports an 8192 token context window.
Intended Use Cases
This model is suitable for a variety of natural language processing tasks that benefit from an instruction-tuned Llama 3.1 base. Its efficient training process suggests it could be a good candidate for applications requiring a capable 8B parameter model with a focus on performance and resource optimization during development.