stackexchange_movies

Introduction

The STACKEXCHANGE_MOVIES model is a fine-tuned version of the meta-llama/Meta-Llama-3.1-8B model. It is specifically trained on the mlfoundations-dev/stackexchange_movies dataset. The model is intended for text generation tasks and is part of the Hugging Face ecosystem.

Architecture

This model is based on the LLaMA architecture, a large-scale language model known for its capabilities in text generation. It operates using the transformers library and utilizes advanced features such as safetensors for efficient data handling.

Training

Training Procedure

The model was trained using the following hyperparameters:

• Learning Rate: 5e-06
• Train Batch Size: 8
• Eval Batch Size: 8
• Seed: 42
• Distributed Type: Multi-GPU
• Num Devices: 8
• Gradient Accumulation Steps: 8
• Total Train Batch Size: 512
• Total Eval Batch Size: 64
• Optimizer: AdamW with betas (0.9, 0.999) and epsilon 1e-08
• LR Scheduler Type: Constant
• Num Epochs: 3.0

Training Results

The model achieved a final validation loss of 1.0959, showing progressive improvement over training epochs.

Guide: Running Locally

To run the model locally, follow these steps:

1. Setup Environment: Ensure that you have Python and the required packages installed. Use a virtual environment for isolation.
2. Install Dependencies:

   pip install transformers==4.46.1 torch==2.3.0 datasets==3.1.0 tokenizers==0.20.3
   

3. Download Model: Access and download the model from Hugging Face's model hub.
4. Run Inference: Use the model for text generation tasks by integrating it into your application.

For enhanced performance, consider using cloud GPUs, such as those offered by AWS, Google Cloud, or Azure.

License

The model is licensed under llama3.1. Ensure compliance with this license when using the model for your applications.