CharlesLi/llama_2_sky_safe_o1_4o_reflect_1000_1000_full

Model Overview

This model, llama_2_sky_safe_o1_4o_reflect_1000_1000_full, is a fine-tuned version of Meta's Llama-2-7b-chat-hf with 7 billion parameters and a 4096-token context length. It has been specifically trained by CharlesLi on a "generator dataset," indicating a specialization in tasks related to content generation or specific data patterns present in its training data.

Key Characteristics

• Base Model: Meta's Llama-2-7b-chat-hf.
• Parameter Count: 7 billion parameters.
• Context Length: 4096 tokens.
• Fine-tuning Focus: Trained on a dedicated "generator dataset."
• Performance Metric: Achieved a loss of 0.7917 on its evaluation set, suggesting a good fit for the data it was trained on.

Training Details

The model was trained using the following hyperparameters:

• Learning Rate: 2e-05
• Batch Sizes: train_batch_size of 4, eval_batch_size of 4, with gradient_accumulation_steps of 2, resulting in a total_train_batch_size of 32.
• Optimizer: Adam with standard betas and epsilon.
• Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio.
• Epochs: Trained for 1 epoch.

Potential Use Cases

Given its fine-tuning on a "generator dataset," this model is likely suitable for applications requiring:

• Specialized text generation based on patterns learned from its training data.
• Tasks where the specific characteristics of the "generator dataset" are relevant.

Further details on specific intended uses and limitations would require more information about the "generator dataset" and its contents.