Eilliar/llama-2-7b-test
Eilliar/llama-2-7b-test is a 7 billion parameter Llama 2 model, fine-tuned by Eilliar. This model serves as a test for understanding the fine-tuning and upload process on platforms like Hugging Face. It was fine-tuned using the mlabonne/guanaco-llama2-1k dataset, primarily for experimental purposes rather than specific production applications.
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Overview
Eilliar/llama-2-7b-test is a 7 billion parameter language model based on the Llama 2 architecture. This model was created by Eilliar as an experimental project to explore the process of fine-tuning and uploading models to platforms like Hugging Face. It utilizes a context length of 4096 tokens.
Key Characteristics
- Architecture: Llama 2 base model.
- Parameter Count: 7 billion parameters.
- Fine-tuning Dataset: The model was fine-tuned on the mlabonne/guanaco-llama2-1k dataset.
- Purpose: Primarily developed for learning and testing the fine-tuning and model upload workflow.
Intended Use
This model is best suited for:
- Educational purposes: Developers looking to understand the practical steps involved in fine-tuning and deploying large language models.
- Experimental use: Testing different configurations or workflows related to Llama 2 fine-tuning.
It is important to note that this model is a test artifact and not intended for production-grade applications requiring robust performance or specific task capabilities.