andrewprayle/llama-2-7b-miniguanaco
The andrewprayle/llama-2-7b-miniguanaco model is a 7 billion parameter fine-tuned version of the Llama 2 architecture, specifically based on NousResearch/llama-2-7b-chat-hf. This model was fine-tuned using the mlabonne/guanaco-llama2-1k dataset, primarily as a learning exercise by its creator, Andrew Prayle. It is intended for internal use to explore the automation of medical literature searching within CochraneCF.
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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-llama2-1kdataset 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.