DigitalPixie/attention-guard-v2-brain-f16
DigitalPixie/attention-guard-v2-brain-f16 is a 0.5 billion parameter Qwen2.5-based causal language model developed by DigitalPixie. Finetuned from unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit, this model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Model Overview
DigitalPixie/attention-guard-v2-brain-f16 is a 0.5 billion parameter instruction-tuned language model based on the Qwen2.5 architecture. Developed by DigitalPixie, this model was finetuned from unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based, a causal language model.
- Parameter Count: 0.5 billion parameters, making it a compact and efficient model.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- Context Length: Supports a context window of 32768 tokens.
Use Cases
This model is suitable for general instruction-following tasks where a smaller, efficiently trained model is beneficial. Its compact size and optimized training make it a good candidate for applications requiring faster inference or deployment on resource-constrained environments, while still providing solid performance for common language understanding and generation tasks.