juhwanlee/gemma-7B-alpaca-case-0-2
The juhwanlee/gemma-7B-alpaca-case-0-2 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, 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 for its training.
Loading preview...
Model Overview
This model, developed by Juhwan Lee, is an 8.5 billion parameter Large Language Model (LLM) built upon the Mistral-7B-v0.1 architecture. It has been specifically fine-tuned for data ordering tasks.
Key Architectural Features
The underlying Mistral-7B-v0.1 architecture includes several notable design choices:
- Grouped-Query Attention: Enhances efficiency and performance.
- Sliding-Window Attention: Optimizes context handling for longer sequences.
- Byte-fallback BPE tokenizer: Provides robust tokenization capabilities.
Training Details
The model was fine-tuned using a random sample of the Open-Orca dataset, specifically utilizing 100,000 data points for this process. This targeted fine-tuning aims to optimize its performance for specific data ordering applications.
Good For
- Data Ordering Tasks: Its primary intended use case due to specialized fine-tuning.
- Research and Experimentation: For developers interested in models fine-tuned on specific data ordering methodologies.
License
This model is released under the Apache License 2.0.