Meta AI und Llama

For Llama, begin with the specific release’s model card, license, acceptable-use terms, and supported configuration. The family includes different model sizes and modalities, so a result for one checkpoint should not be generalized to every release. Downloading weights can enable local operation or adaptation, but the complete deployment also needs compatible architecture code, tokenization, configuration, and hardware. Quantized or community-modified versions are distinct artifacts whose behavior should be evaluated directly. Distinguish open access from unrestricted reuse. Read the actual terms rather than inferring rights from the availability of a download. Code, model weights, and training-data information can have different conditions or levels of completeness. Evaluate the intended application with representative inputs and a defined failure policy. Local control shifts infrastructure, security, updates, and monitoring responsibilities to the operator. A consumer product using related research may add tools, policies, and other components not present in a downloaded checkpoint.