How to refine Llama 2 Meta's ChatGPT open source AI alternative

Meta is raising the bar in AI with the introduction of its latest version of Llama, an open source language model. Its latest release, Llama 2, offers a significant upgrade, giving users a comprehensive toolset to fuel innovation and expand the boundaries of their digital creations. Let's dive into the standout features of Llama 2 and explore how to fine-tune this state-of-the-art model.

Mostly preformed using a wide range of publicly available online resources, Llama 2 stands out for its incredible prowess and enhanced abilities. Llama-2-chat, the refined model, is a product of the integration of publicly available educational data and over one million human annotations. This meticulous approach resulted in Llama 2 models having twice the context length of Llama 1, with an impressive training base of 2,000 billion tokens.

Llama 2's ability to eclipse other open-source language models on many external criteria, including coding, reasoning, skill, and knowledge tests, is testament to its top-tier performance.

Llama-2-cat model training is a complex process, powered by the fusion of several technological strategies. Initially, Llama 2 uses publicly available online data for pre-training, followed by supervised fine-tuning to create an initial version of Llama-2-chat. The model is then iteratively refined through reinforcement learning from human feedback (RLHF), using techniques such as rejection sampling and proximal policy optimization (PPO).

When you download a Llama 2 model, your package will include the following: model code, model weight, README file (user guide), responsible use guide, license, acceptable use policy, and model card.

One of Llama 2's main attractions is its fine-tuning potential. A comprehensive tutorial is available which walks users through how to fine-tune the Llama 2 model using Quantized Low Rank Approximation (QLoRA) and then upload the model to the Hugging Face model hub.

For example, the embedded tutorial below demonstrates this process using a French dataset, allowing the Llama 2 model to generate French text. This involves refining the model using French quotes, a process inspired by a Hugging Face tutorial, which reduces model accuracy and memory requirements using QLoRA.

In this example tutorial, fine-tuning the Llama 2 model requires Google Colab, an advantageous tool that allows less than 15 GB of memory usage thanks to the quantized model. It also involves the use of four main libraries: Accelerate, PiFT, Transformers and Datasets. Additionally, weights and biases are used for 4-bit quantization and monitoring of the training process.

Watch this video on YouTube.

The dataset, available on the Hugging Face model hub, contains prompts and formatted responses for model training. Throughout the training process, it is essential to monitor convergence, anticipating that the loss of training will decrease over time. At the end of the training, the model can be saved and used for text generation. In the video above, also learn how to authenticate the notebook with the Hugging Face template hub and download the template for future use.

The fine tuning of the Llama 2 model expands its capabilities, allowing it to handle a variety of tasks more efficiently. It allows individuals, creators, researchers and companies to experiment, innovate and develop their ideas responsibly. Whether you're new to the game or a seasoned pro, taking the time to learn how to polish Llama 2 will surely improve your...