Model Overview

This model, llama_2_cot_simplest_alpaca_5_full, is a 7 billion parameter language model derived from Meta's Llama-2-7b-chat-hf. It has undergone fine-tuning on a specific generator dataset, aiming to enhance its performance in conversational and instruction-following scenarios.

Key Characteristics

• Base Model: Fine-tuned from meta-llama/Llama-2-7b-chat-hf.
• Parameter Count: 7 billion parameters.
• Context Length: Supports a context window of 4096 tokens.
• Training Objective: Optimized through fine-tuning on a generator dataset, indicated by an evaluation loss of 0.7572.

Training Details

The model was trained with a learning rate of 2e-05, a batch size of 4 (total train batch size of 32 across 4 GPUs), and utilized the Adam optimizer. The training process involved 1 epoch with a cosine learning rate scheduler.

Potential Use Cases

• Conversational AI: Suitable for chatbots and interactive agents.
• Instruction Following: Can be applied to tasks requiring the model to adhere to specific instructions or prompts.
• Text Generation: Capable of generating coherent and contextually relevant text based on input.