LLama2, Meta's open-source language model, has made waves in the AI community with its impressive capabilities and cost-effective deployment options. This model, an evolution of its predecessor, Llama, not only matches the performance of state-of-the-art models but also challenges giants like GPT-3.5. Released with various parameter sizes and an open-source license, LLama2 is a game-changer, allowing researchers and companies to harness its power without the burden of API costs. In this guide, we'll explore how to deploy LLama2 on platforms like AWS SageMaker and HuggingFace, discuss associated costs, and delve into innovative techniques and tools like quantization, LoRA and Ollama for local use and fine-tuning, all backed by Python code examples.
Earlier this year, Meta announced their own LLM, following the path of their major competitors, Google and OpenAI. However, they quickly set themselves apart by releasing their model as open source. While it initially proved competitive with many state-of-the-art models, it soon became evident that it lagged behind GPT-3.5. As a consequence of offering it under a free license, variations of the model emerged within a few weeks of the release, such as Vicuna, an open-source chatbot fine-tuned from LLama.
Later in the year, in July, Meta introduced Llama-2. It retained the same architecture but incorporated 40% more data for training the foundational models. They released it in three sizes, with 7 billion, 13 billion, and 70 billion parameters. Similar to the first version, Llama-2 was also open source. However, this time, it included a fine-tuned model for dialog, known as LLaMA-2 Chat. This was a significant development for the industry, as the capabilities of the largest Llama-2 model are on par with GPT-3.5. This makes it the largest open-source LLM to date, providing researchers and companies with the opportunity to experiment with such a powerful tool at no cost per token or for the use of any API, only incurring the costs of their own infrastructure.
Key points of Llama2
In discussions about LLama2, several key points are worth mentioning, we have written separated articles for each one of them, explaining these concepts in more detail:
- How to deploy LLama2 on cloud platforms: LLama2, can be deployed using various options such as AWS SageMaker and HuggingFace. Deploying the model requires multiple GPUs, which can be expensive. AWS SageMaker allows easy deployment through its studio environment, while HuggingFace requires access and a valid payment method. It is important to consider the costs of infrastructure for running and deploying the model. Compliance is also crucial, as users are responsible for maintaining infrastructure, access, privacy, and security. Overall, deploying LLama2 offers flexibility but requires careful consideration of costs and comparison to the GPT-3 API. Link to the article.
- How to use LLama2 locally with Python and techniques like quantization and LoRA: The release of the Llama2 model as an open-source project has allowed for experimentation and improvements. Techniques like Quantization and LoRA have re-emerged, reducing hardware requirements for using and training the model. Ollama, a tool that leverages these techniques, enables running large language models locally. Quantized Llama2 models are available on HuggingFace, providing cost-effective alternatives for utilizing and fine-tuning the model. These advancements make it possible to use Llama2 for various language tasks without relying solely on expensive hosting services or extensive hardware resources, only a bunch of Python libraries. Link to the article.
- The process of fine-tuning LLama2 with Python and a single GPU: Fine-tuning involves further training a pre-trained model on a specific task or dataset, leveraging the valuable features and representations learned during pre-training. Fine-tuning offers advantages such as faster training and improved performance on specific tasks. However, it's important to consider the limitations and challenges associated with fine-tuning. The processing requirements, especially for large language models, can also be significant, although techniques like quantization and LoRA can help reduce these requirements, making possible to fine-tune LLama2 with a single GPU and some Python libraries. It is possible to use the fine-tuning process to enable the model to answer questions about a specific company (foe example, Rootstrap) without needing additional context in the prompt, and to make it refuse to answer any other question non related to the company. Link to the article.
These topics provide insights into the practical applications and capabilities of LLama2, making it a versatile and powerful language model.
In conclusion, LLama2, Meta's open-source language model, has emerged as a powerful and cost-effective tool for the AI community. Its availability in various parameter sizes and open-source licensing makes it a game-changer, allowing researchers and companies to harness its capabilities without the burden of API costs. LLama2 challenges giants like GPT-3.5 and offers exciting opportunities for innovation.
While LLama2 represents a significant advancement, GPT-3 remains the preferred choice for some due to the convenience and affordability of its API. However, LLama2 offers unique advantages when used locally, thanks to techniques like quantization and LoRA, making it an excellent option for various language tasks. This, in turn, has led to the development of tools like Ollama, simplifying local use.
Fine-tuning LLama2 opens up new possibilities for specialized tasks and reduces the need for extensive context in prompts. This versatility and cost-effective approach make LLama2 a valuable addition to the AI landscape, complementing other established models like GPT-3.