Model Overview

The andrewprayle/llama-2-7b-miniguanaco is a 7 billion parameter language model, representing a fine-tuned iteration of the Llama 2 architecture. Specifically, it is built upon the NousResearch/llama-2-7b-chat-hf base model.

Key Characteristics

• Base Model: Fine-tuned from NousResearch/llama-2-7b-chat-hf.
• Training Data: Utilizes the mlabonne/guanaco-llama2-1k dataset for fine-tuning.
• Context Length: Inherits a context length of 4096 tokens from its Llama 2 base.
• Development Purpose: Primarily developed as a personal learning project by Andrew Prayle to understand model fine-tuning.

Intended Use Cases

This model is currently intended for internal exploration within CochraneCF to investigate:

• Automating aspects of medical literature searching.
• Understanding the practical application of large language models in a research context.

Limitations

As a fine-tuned version of Llama 2 7B, it is expected to share similar risks, biases, and limitations as its foundational model. Users are advised to be aware of these inherent characteristics, which are still under exploration by the developer.