OpenAI may be the more well-known name when it comes to commercial generative AI, but Meta has successfully clawed out a place through open sourcing powerful large language models. Meta revealed its largest generative AI model yet, Llama 3, on April 18, which outperforms GPT04 on some standard AI benchmark tests.
Llama 3 is an LLM created by Meta. It can be used to create generative AI, including chatbots that can respond in natural language to a wide variety of queries. The use cases Llama 3 has been evaluated on include brainstorming ideas, creative writing, coding, summarizing documents and responding to questions in the voice of a specific persona or character.
The full Llama 3 model comes in four variants:
Llama 3’s generative AI capabilities can be used in a browser, through AI features in Meta’s Facebook, Instagram, WhatsApp and Messenger. The model itself can be downloaded from Meta or from major enterprise cloud platforms.
Llama 3 was released on April 18 on Google Cloud Vertex AI, IBM’s watsonx.ai and other large LLM hosting platforms. AWS followed, adding Llama 3 to Amazon Bedrock on April 23. As of April 29, Llama 3 is available on the following platforms:
Hardware platforms from AMD, AWS, Dell, Intel, NVIDIA and Qualcomm support Llama 3.
Llama 3 is open source, as Meta’s other LLMs have been. Creating open source models has been a valuable differentiator for Meta.
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There is some debate over how much of a large language model’s code or weights need to be publicly available to count as open source. But as far as business purposes go, Meta offers a more open look at Llama 3 than its competitors do for their LLMs.
Llama 3 is free as long as it is used under the terms of the license. The model can be downloaded directly from Meta or used within the various cloud hosting services listed above, although those services may have fees associated with them.
Llama 3 is not multimodal, which means it is not capable of understanding data from different modalities such as video, audio or text. Meta plans to make Llama 3 multimodal in the near future.
To make Llama 3 more capable than Llama 2, Meta added a new tokenizer to encode language much more efficiently. Meta souped Llama 3 up with grouped query attention, a method of improving the efficiency of model inference. The Llama 3 training set is seven times the size of the training set used for Llama 2, Meta said, including four times as much code. Meta applied new efficiencies to Llama 3’s pretraining and instruction fine-tuning.
Since Llama 3 is designed as an open model, Meta added guardrails with developers in mind. A new guardrail is Code Shield, which is intended to catch insecure code the model might produce.
Meta plans to:
Meta is working on a 400B parameter model, which may help shape the next generation of Llama 3. In early testing, Llama 3 400B with instruction tuning scored 86.1 on the MMLU knowledge assessment (an AI benchmark test), according to Meta, making it competitive with GPT-4. Llama 400B would be Meta’s largest LLM thus far.
Llama 3 competes directly with GPT-4 and GPT-3.5, Google’s Gemini and Gemma, Mistral AI’s Mistral 7B, Perplexity AI and other LLMs for either individual or commercial use to build generative AI chatbots and other tools. About a week after Llama 3 was revealed, Snowflake debuted its own open enterprise AI with comparable capabilities, called Snowflake Arctic.
The increasing performance requirements of LLMs like Llama 3 are contributing to an arms race of AI-enabled PCs that can run models at least partially on-device. Meanwhile, generative AI companies may face increased scrutiny over heavy compute needs, which could contribute to worsening climate change.
Llama 3 outperforms OpenAI’s GPT-4 on HumanEval, which is a standard benchmark that compares the AI model’s ability to generate code with code written by humans. Llama 3 70B scored 81.7, compared to GPT-4’s score of 67.
However, GPT-4 out-performed Llama 3 on the knowledge assessment MMLU with a score of 86.4 to Llama 3 70B’s 79.5. Llama 3’s performance on more tests can be found on Meta’s blog post.
