NLUHOPOE/Mistral-test-case-2
NLUHOPOE/Mistral-test-case-2 is a 7 billion parameter Large Language Model developed by Juhwan Lee. Based on the Mistral-7B-v0.1 architecture, it features Grouped-Query Attention, Sliding-Window Attention, and a Byte-fallback BPE tokenizer. This model is specifically fine-tuned for data ordering tasks, utilizing a random sample of the Open-Orca dataset.
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Model Overview
NLUHOPOE/Mistral-test-case-2 is a 7 billion parameter Large Language Model developed by Juhwan Lee. It is built upon the Mistral-7B-v0.1 architecture, which incorporates advanced features such as Grouped-Query Attention and Sliding-Window Attention for efficient processing. The model also utilizes a Byte-fallback BPE tokenizer.
Key Capabilities
- Data Ordering: The model has been specifically fine-tuned for data ordering tasks.
- Mistral Architecture: Leverages the robust and efficient Mistral-7B-v0.1 base.
Training Details
The model was fine-tuned using a random sample of the Open-Orca dataset, specifically on 100,000 data points, to optimize its performance for data ordering. Further details can be found on the developer's GitHub.
License
This model is released under the Apache License 2.0.