jisukim8873/falcon-7B-case-5
The jisukim8873/falcon-7B-case-5 model is a 7 billion parameter large language model developed by Jisu Kim. Based on the Falcon-7B architecture, it features Grouped-Query Attention and Sliding-Window Attention. This model has been fine-tuned specifically for data ordering tasks, utilizing a randomly sampled subset of the Open-Orca dataset. Its primary application is in testing data ordering methodologies.
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Model Overview
The jisukim8873/falcon-7B-case-5 is a 7 billion parameter Large Language Model (LLM) developed by Jisu Kim. It is built upon the Falcon-7B architecture, which incorporates advanced features such as Grouped-Query Attention and Sliding-Window Attention for efficient processing. The model uses a Byte-fallback BPE tokenizer.
Key Capabilities
- Data Ordering: This model is specifically fine-tuned for data ordering tasks.
- Falcon-7B Base: Leverages the robust architecture of Falcon-7B, known for its efficiency.
- Fine-tuned Dataset: Training involved fine-tuning on 100,000 samples from the Open-Orca dataset, focusing on data ordering.
Use Cases
- Data Ordering Research: Ideal for experiments and testing related to data ordering methodologies.
- Benchmarking: Can be used as a base for evaluating different data ordering approaches.
License
The model is released under the Apache License 2.0, allowing for broad use and distribution.