abeiler/WordProb: A Fine-Tuned Llama 2 Model

This model, developed by abeiler, is a fine-tuned iteration of the meta-llama/Llama-2-7b-hf base model. It leverages the robust Llama 2 architecture, featuring 7 billion parameters, to offer specialized capabilities.

Key Characteristics

• Base Model: Built upon the widely recognized and powerful Llama-2-7b-hf.
• Fine-Tuning: The model has undergone a fine-tuning process, indicating optimization for particular tasks or domains, though the specific dataset and intended uses are not detailed in the provided information.
• Training Hyperparameters: Training was conducted with a learning rate of 0.0001, a train batch size of 4, and an Adam optimizer, over 1 epoch.

Intended Uses

While specific intended uses are not explicitly defined, as a fine-tuned Llama 2 variant, it is generally suitable for tasks where the base Llama 2 model performs well, with potential enhancements in areas targeted by its fine-tuning. Users should evaluate its performance for their specific applications.

Limitations

Detailed information regarding the specific dataset used for fine-tuning, as well as comprehensive intended uses and limitations, is currently not available. Users are advised to conduct thorough testing for their specific use cases.