Model Overview

This model, llama_2_sky_safe_o1_llama_3_8B_default_4000_500_full, is a fine-tuned iteration of the meta-llama/Llama-2-7b-chat-hf base model. It features 7 billion parameters and was trained with a context length of 4096 tokens. The fine-tuning process utilized a specific "generator dataset," resulting in a reported validation loss of 0.6327.

Training Details

The model underwent training for 1 epoch with a learning rate of 2e-05, using an Adam optimizer with betas=(0.9, 0.999) and epsilon=1e-08. The training setup involved a total batch size of 32 across 4 devices, employing a cosine learning rate scheduler with a 0.1 warmup ratio. Intermediate training results show a decreasing loss, indicating effective learning from the fine-tuning data.

Key Characteristics

• Base Model: Llama-2-7b-chat-hf
• Parameter Count: 7 billion
• Context Length: 4096 tokens
• Fine-tuning Objective: Optimized on a specific generator dataset.
• Performance Metric: Achieved a validation loss of 0.6327.

Potential Use Cases

This model is best suited for applications that align with the characteristics of its fine-tuning dataset. Developers should consider its Llama-2 lineage and specialized training for tasks where a refined version of Llama-2-7b-chat-hf is beneficial, particularly if the use case mirrors the "generator dataset" it was trained on.