The development and evolution of language models have been a significant area of interest in the field of artificial intelligence. One such AI model that has garnered attention is Llama 2, an updated version of the original Llama model. Meta the development team behind Llama 2 has made significant strides in improving the model’s capabilities, with a focus on open-source tooling and community feedback. This guide provides more details on how Meta created Llama 2 delves into the development, features, and potential applications of Llama 2, providing an in-depth look at the advancements in large language models. Thanks to a presentation by Angela Fan a research scientist at Meta AI Research Paris who focuses on machine translation.
Llama 2 was developed with the feedback and encouragement from the community. The team behind the model has been transparent about the development process, emphasizing the importance of open-source tools. This approach has allowed for a more collaborative and inclusive development process, fostering a sense of community around the project.
How Meta developed Llama 2
The architecture of Llama 2 is similar to the original, using a standard Transformer-based architecture. However, the new model comes in three different parameter sizes: 7 billion, 13 billion, and 70 billion parameters. The 70 billion parameter model offers the highest quality, but the 7 billion parameter model is the fastest and smallest, making it popular for practical applications. This flexibility in parameter sizes allows for a more tailored approach to different use cases.
The pre-training data set for Llama 2 uses two trillion tokens of text found on the internet, predominantly in English, compared to 1.4 trillion in Llama 1. This increase in data set size has allowed for a more comprehensive and diverse range of language patterns and structures to be incorporated into the model. The context length in Llama 2 has also been expanded to around 4,000 tokens, up from 2,000 in Llama 1, enhancing the model’s ability to handle longer and more complex conversations.
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Training Llama 2
The training process for Llama 2 involves three core steps: pre-training, fine-tuning to make it a chat model, and a human feedback loop to produce different reward models for helpfulness and harmlessness. The team found that high-quality data set annotation was crucial for achieving high-quality supervised fine-tuning examples. They also used rejection sampling and proximal policy optimization techniques for reinforcement learning with human feedback. This iterative improvement process showed a linear improvement in both safety and helpfulness metrics, indicating that it’s possible to improve both aspects simultaneously.
The team behind Llama 2 also conducted both automatic and human evaluations, with around 4,000 different prompts evaluated for helpfulness and 2,000 for harmlessness. However, they acknowledged that human evaluation can be subjective, especially when there are many possible valuable responses to a prompt. They also highlighted that the distribution of prompts used for evaluation can heavily affect the quality of the evaluation, as people care about a wide variety of topics.
AI models
Llama 2 has been introduced as a competitive model that performs significantly better than open-source models like Falcon or Llama 1, and is quite competitive with models like GPT 3.5 or Palm. The team also discussed the concept of “temporal perception”, where the model is given a cut-off date for its knowledge and is then asked questions about events after that date. This feature allows the model to provide more accurate and contextually relevant responses.
Despite the advancements made with Llama 2, the team acknowledges that there are still many open questions to be resolved in the field. These include issues around the hallucination behavior of models, the need for models to be more factual and precise, and questions about scalability and the types of data used. They also discussed the use of Llama 2 as a judge in evaluating the performance of other models, and the challenges of using the model to evaluate itself.
Fine tuning
The team also mentioned that they have not released their supervised fine-tuning dataset, and that the model’s access to APIs is simulated rather than real. They noted that the model’s tool usage is not particularly robust and that more work needs to be done in this area. However, they also discussed the potential use of language models as writing assistants, suggesting that the fine-tuning strategy and data domain should be adjusted depending on the intended use of the model.
Llama 2 represents a significant step forward in the development of large language models. Its improved capabilities, coupled with the team’s commitment to open-source tooling and community feedback, make it a promising tool for a variety of applications. However, as with any technology, it is important to continue refining and improving the model, addressing the challenges and open questions that remain. The future of large language models like Llama 2 is bright, and it will be exciting to see how they continue to evolve and shape the field of artificial intelligence.
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