monradach/Llama-3.2-3B-Instruct-HeadQA
The monradach/Llama-3.2-3B-Instruct-HeadQA is a 3.2 billion parameter instruction-tuned Llama model, developed by monradach and finetuned from unsloth/Llama-3.2-3B-Instruct. It was trained using Unsloth and Huggingface's TRL library, specifically optimized on the alesi12/head_qa_v2 dataset. This model is designed for question-answering tasks, particularly those related to the HeadQA dataset, leveraging efficient training methods for faster development.
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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_v2dataset, 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.