An In-Depth Exploration: Open vs Closed Source LLMs, Unpacking Llama 2’s Unique Features, Mastering the Art of Prompt Engineering, and Designing Robust Solutions with FastAPI, Celery, Redis, and Docker
In an unexpected move, Meta open-sourced their Large Language Model (LLM), Llama 2, a few days ago in a decision that could reshape the current landscape of AI development. It offers an alternative to the main companies in the space such as OpenAI and Google that decided to maintain tight control over their AI models, limiting accessibility and restricting broader innovation. Hopefully, Meta’s decision will spark a collective response from the open-source community, counteracting the trend of restricting access to the advances in the field. Llama 2’s new license even goes further and allows commercial use, granting developers and businesses opportunities to leverage the model within existing and new products.
The Llama2 family consists of pre-trained and fine-tuned LLMs, including Llama2 and Llama2-Chat, scaling up to 70B parameters. These models have proven to perform better than open-source models on various benchmarks . They also hold their ground against some closed-source models, offering a much-needed boost to open-source AI development .
If you follow the Open LLM leaderboard from HuggingFace , you can see that Meta’s Llama 2 holds a strong third-place position. After the LLama 2 announcement, Stability AI released FreeWilly1 and FreeWilly2 . FreeWilly1 is a fine-tuned version of Llama, and FreeWilly2 of Llama 2. Stability AI shared that they fine-tuned both models on an Orca-style Dataset. The Orca dataset is a large, structured collection of augmented data designed to fine-tune LLMs, where each entry consists of a question and a corresponding response from GPT-4 or GPT-3.5. Why are we not using the FreeWilly2 model? Unfortunately, while Llama 2 allows commercial use, FreeWilly2 can only be used for research purposes, governed by the Non-Commercial Creative Commons license…