Open Weights

Open weights differ from fully open source: the former makes model parameters public but does not necessarily disclose training data and complete training code.

开放权重模型指参数权重文件向公众开放下载，可本地部署微调，但训练数据和完整源码不一定公开，区别于完全开源

Open weights means publicly releasing the trained model parameter files, allowing deployment locally or on private servers without needing to send data to the cloud.

Open-weight models refer to models whose parameters and weights are publicly released and allow users to download and deploy them independently, such as Meta's Llama series and Mistral.

Meta adopts an open-weights strategy, making model parameters publicly available for research and commercial use, though the training data and full pipeline are not entirely transparent.

"Open-weight" and "fully open source" are not equivalent: the former only makes model parameter files public, while training data, complete training code, and certain commercial usage rights may not be released alongside.

Open weight means the model parameters can be downloaded and executed locally, but the training code, training data, and terms of use are not necessarily open; Gemma uses a custom Gemma Terms of Use.

Open-weight models refer to models that, after training, publicly release the neural network parameter weights under an open license, allowing them to be downloaded and run on one's own hardware and fine-tuned with proprietary data without paying inference fees.

Open weights and fully open source are not equivalent: the former only makes model weight files publicly available, while the latter follows standard open-source protocols as defined by the OSI.

An open-weights strategy means that model parameters can be downloaded and used for free, but training data and the complete training process are generally not made public, and it has become the mainstream choice for companies such as Meta Llama and Mistral.

开放权重模型仅发布训练完成的模型参数文件，而严格意义的开源AI要求公开训练代码、数据集与完整复现流程

开放权重（Open Weight）仅指模型的参数权重文件可供下载和使用，但训练数据、完整训练代码、RLHF标注数据等核心资产往往并不公开，与真正意义上的开源（Open Source）存在区别

Security hardening for open-weights models includes preventing prompt injection, model theft, and adversarial example attacks, and these operational responsibilities fall entirely on the adopter.

Open weights allow teams to fine-tune, quantize, or run inference in private environments themselves, which is critical for enterprise-level routing scenarios where request distribution logic is strongly tied to business data.

The methodological value of open-weight models in security research lies in turning "the vendor says it is fixed" into "the community can verify whether it is fixed."

When only model weights are opened, a company can still maintain a cost advantage through inference optimization, service operations and scale effects; but after opening the inference framework, competitors can directly reuse the same engineering implementation and narrow the performance gap.

Tenstorrent positions itself as a promoter of open-weight AI chips, where open-weight refers to the openness and portability of hardware design specifications, instruction sets, and the accompanying software stack (compiler, runtime, operator libraries).

Open-weight in the AI chip context differs from the concept of open-weight in the open-source large model field: the former emphasizes openness of hardware and software stacks, while the latter refers to publicly releasing model parameters to allow downloading and fine-tuning.

Open weights mean that the parameter weights of a model after training can be publicly downloaded or obtained, allowing users to deploy, fine-tune, or even further develop it on their own infrastructure.

When evaluating whether a model can be used in production, the existence of a repository ID only proves it was published and cannot replace checking the model card's license field and actual download availability; one should check whether the parameter files download reliably, whether the license permits the intended use, and whether an available inference service or quantized versions are provided.