How to fine-tune Llama 2


How to fine-tune Llama 2

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Meta is raising the bar in the realm of AI with the introduction of its latest version of Llama, an open-source language model. It’s newest release, Llama 2, offers a significant upgrade, presenting users with a comprehensive toolset to fuel innovation and broaden the boundaries of their digital creations. Let’s delve into the remarkable features of Llama 2 and explore how to fine-tune this state-of-the-art model.

Open source AI

Primarily pretrained using an extensive range of publicly available online resources, Llama 2 is distinguished by its incredible prowess and enhanced capabilities. Llama-2-chat, the fine-tuned model, is a product of integrating publicly accessible instructional data and more than a million human annotations. This meticulous approach has ensured that Llama 2 models have a context length twice that of Llama 1, with an impressive training base of 2 trillion tokens.

The ability of Llama 2 to outshine other open source language models on numerous external benchmarks, including coding, reasoning, proficiency, and knowledge tests, is testament to its high-level performance.

Downloading Llama 2

Training the Llama-2-chat model is an intricate process, powered by the merger of several technological strategies. Initially, Llama 2 uses publicly available online data for pretraining, followed by supervised fine-tuning to create an initial version of Llama-2-chat. The model then undergoes iterative refinement through Reinforcement Learning from Human Feedback (RLHF), employing 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 Weights, README (User Guide), Responsible Use Guide, License, Acceptable Use Policy and Model Card.

Fine tuning Llama 2

One of the major attractions of Llama 2 is its potential for fine-tuning. A comprehensive tutorial is available, which guides users on how to fine-tune the Llama 2 model using Quantized Low-Rank Approximation (QLoRA) and subsequently upload the model to the Hugging Face model hub.

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For instance, the tutorial embedded below illustrates this process using a French dataset, thereby enabling the Llama 2 model to generate French text. This involves fine-tuning the model using French quotes, a process inspired by a Hugging Face tutorial, which reduces the model’s precision and memory requirements through QLoRA.

In this example tutorial fine-tuning the Llama 2 model requires Google Colab, an advantageous tool allowing less than 15GB of memory usage due to the quantized model. It also involves using four primary libraries: Accelerate, PiFT, Transformers, and Datasets. Additionally, Weights & Biases are employed for 4-bit quantization and monitoring the training process.

The dataset, available on the Hugging Face model hub, contains prompts and responses formatted for training the model.  Throughout the training process, it is critical to monitor for convergence, anticipating that the training loss will decrease over time. Upon completion 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 model hub and upload the model for future use.

Fine-tuning the Llama 2 model expands its capabilities, enabling it to handle a variety of tasks more effectively. It empowers individuals, creators, researchers, and businesses to experiment, innovate, and scale their ideas responsibly. Whether you’re a novice or a seasoned professional in the field, taking the time to learn how to fine-tune Llama 2 will surely enhance your AI applications and bring your ideas to life.

For more information on the latest open source AI to be released by Meta jump over to the official product page for more information and download links.

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