Model Overview

The monradach/Llama-3.2-3B-Instruct-HeadQA is a 3.2 billion parameter instruction-tuned language model, developed by monradach. It is based on the unsloth/Llama-3.2-3B-Instruct architecture and was finetuned using the Unsloth library in conjunction with Huggingface's TRL library, enabling a 2x faster training process.

Key Characteristics

• Base Model: Finetuned from unsloth/Llama-3.2-3B-Instruct.
• Parameter Count: 3.2 billion parameters.
• Context Length: Supports a context length of 32768 tokens.
• Training Efficiency: Utilizes Unsloth for accelerated training.
• Dataset: Specifically trained on 5,000 samples from the alesi12/head_qa_v2 dataset, with 4,000 samples for training and 1,000 for testing.

Use Cases

This model is particularly well-suited for:

• Question Answering: Optimized for tasks related to the HeadQA dataset, making it suitable for specialized QA applications.
• Efficient Deployment: Its smaller parameter count and efficient training suggest potential for faster inference and reduced resource consumption compared to larger models.

Licensing

The model is released under the Apache-2.0 license.