Overview

This model, Llama-3.1-8B_suffix_phrase, is a fine-tuned variant of the meta-llama/Llama-3.1-8B-Instruct base model. It features 8 billion parameters and a substantial context length of 32,768 tokens, suggesting its capability to handle complex and lengthy textual interactions.

Training Details

The model was trained using specific hyperparameters, including a learning rate of 2e-05, a total batch size of 16 across 4 GPUs, and an Adam optimizer. The training process involved 5 epochs with a cosine learning rate scheduler and a warmup ratio of 0.1. This configuration indicates a focused fine-tuning approach aimed at adapting the base Llama-3.1-8B-Instruct model for particular applications.

Key Characteristics

• Base Model: Fine-tuned from meta-llama/Llama-3.1-8B-Instruct.
• Parameter Count: 8 billion parameters.
• Context Length: 32,768 tokens.
• Training Framework: Utilized Transformers 4.43.3, Pytorch 2.3.1, Datasets 2.20.0, and Tokenizers 0.19.1.

Intended Use Cases

While specific intended uses and limitations are not detailed in the provided information, the model's name, _suffix_phrase, suggests a specialization in tasks involving the manipulation, generation, or detection of specific phrase patterns or suffixes. This could be relevant for research in areas like backdoor attacks, linguistic analysis, or controlled text generation where precise phrase-level control is required.