Llama 3.2
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Llama 3.2 was trained primarily in English and some additional languages. So, it does support other languages, but not as proficiently as English.
To achieve low latency, the larger Llama model needs to be split across multiple inference chips (GPU) with tensor parallelism. Other than that, these Llama models are compatible with many different types of hardware, including GPUs, CPUs, TPUs, NPUs, AI Accelerators, etc.
Llama 3.2 finds use cases in almost all industries thanks to its wide range of applications. It can automate patient interaction and medical care in healthcare, help analyze contracts in the legal sector, automate financial documentation in the finance sector, etc.
Llama 3.2 lets businesses control how and where the model is deployed (to ensure that it meets high-security standards). This guarantees data confidentiality, privacy, and industry-standard compliance. Additionally, Meta provides a deployment guideline for secure and seamless Llama 3.2 deployment.
Yes, Llama 3.2 allows businesses ample flexibility to fine-tune the model on their data. This helps them train the model for specific use cases related to their performance goals and business requirements.
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