juhwanlee/gemma-7B-alpaca-case-1-2
The juhwanlee/gemma-7B-alpaca-case-1-2 is an 8.5 billion parameter large language model developed by Juhwan Lee. Based on the Mistral-7B-v0.1 architecture, it incorporates Grouped-Query Attention and Sliding-Window Attention. This model is specifically fine-tuned for data ordering tasks, utilizing a subset of the Open-Orca dataset. Its design focuses on efficient processing for structured data arrangement.
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Model Overview
The juhwanlee/gemma-7B-alpaca-case-1-2 is an 8.5 billion parameter Large Language Model developed by Juhwan Lee. It is built upon the Mistral-7B-v0.1 architecture, which includes advanced features like Grouped-Query Attention and Sliding-Window Attention for efficient processing. The model also uses a Byte-fallback BPE tokenizer.
Key Capabilities
- Data Ordering: This model has been specifically fine-tuned for tasks involving data ordering.
- Architectural Efficiency: Leverages Mistral-7B's efficient transformer architecture.
Training Details
The model was fine-tuned using a random sample of 100,000 entries from the Open-Orca dataset, specifically targeting data ordering applications.
Good For
- Developers working on applications requiring structured data arrangement or reordering.
- Experimentation with fine-tuned models based on the Mistral architecture for specific task optimization.
License
This model is released under the Apache License 2.0.