Model Overview

kykim0/Llama-2-7b-ultrachat200k-2e is a 7 billion parameter language model based on the Llama-2-hf architecture. It has been fine-tuned by kykim0 using the HuggingFaceH4/ultrachat_200k dataset, which is designed to enhance conversational and instruction-following capabilities. The model achieved a validation loss of 0.9258 during training.

Training Details

The fine-tuning process involved specific hyperparameters:

• Learning Rate: 2e-05
• Batch Size: 8 (train), 16 (eval)
• Gradient Accumulation: 16 steps, leading to a total effective batch size of 512
• Optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08
• Epochs: 2.0
• Scheduler: Cosine learning rate scheduler

This configuration was executed across 4 GPUs, utilizing Transformers 4.36.2 and Pytorch 2.1.2.

Potential Use Cases

Given its fine-tuning on a conversational dataset, this model is likely suitable for:

• Chatbot development
• Instruction-following tasks
• General text generation
• Prototyping applications requiring a Llama-2 base