Open-Weight Model

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

"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-sourcing the weights means others can directly load and use them or fine-tune them, but it does not include the training data, training methods, or distributed training framework.

Open-weight models refer to models whose parameters are publicly released and can be run locally, but whose training data and code are not necessarily open-source; typical examples include Meta's Llama, Mistral, and DeepSeek.

Open-source solutions allow users to flexibly switch underlying models, including locally deployed open-source models such as Llama and Qwen

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（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

DeepSeek chose the open-weight route, at the cost that responsibility for safety alignment shifts from the model provider to the deployer; the provider can only impose limited constraints through licenses and usage policies, and cannot filter or reject requests in real time on the server side as with a closed-source API.

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.

Open weights mean others can load, run inference on, fine-tune or distill the model, but they cannot directly replicate the distributed strategy, data cleaning pipeline, alignment methods and hardware scheduling experience in the training pipeline.

Open-source weights mean local deployment is possible, with data never leaving the internal network, which is a hard compliance requirement for agents that handle sensitive information such as customer screenshots.

Local open-source-weights inference costs close to zero, can work offline, and keeps data on the device, so it does not grow linearly with listing volume.

Open-source weights mean the model parameter files can be downloaded and run on your own; distillation is using a large model to generate data to train a model with far fewer parameters, and distilled small models commonly come in sizes of 7B, 14B and 32B, and after quantization and compression can run on a single consumer-grade graphics card or even purely on CPU.

The mechanism by which open-source weights constrain pricing is: once weights are released under a permissive license such as Apache 2.0, anyone with sufficient VRAM can self-deploy, marginal cost degenerates to electricity and hardware depreciation, and vendor pricing is capped by the cost of the self-hosted alternative.

Open-weight models theoretically allow teams to deploy the same set of weights in their own environments, satisfying compliance requirements for data residency.

In large-scale model training, a theoretical understanding of optimization convergence can significantly reduce trial-and-error costs.

Different open-source models have different license agreements and usage restrictions, and some models have special terms in commercial use scenarios.

Shieldstral is released as open-weight, meaning the model parameters can be freely downloaded and used, but the training data, training code, and data processing pipeline may not be publicly available.