GLM-5 is generally distributed as open weights (publicly downloadable model checkpoints) with an accompanying code repository and model hub pages that provide deployment instructions. For developers, the important point is: you can typically download the weights and run inference yourself, but your actual rights (commercial use, redistribution, hosting) depend on the license terms attached to the release. “Open source” is often used loosely in model discussions; in strict software terms, “open source” refers to code under OSI-approved licenses, while “open weights” refers to the model parameters being available. In practice, you should treat GLM-5 as “publicly downloadable weights + supporting code,” and then read the license and model card carefully to decide how you can use it in your product.
You can download GLM-5 model files from the official model hub listing (commonly hosted on Hugging Face) and follow the linked deployment guide for supported inference frameworks. The typical artifacts you need are: weight shards, tokenizer files, and a configuration file. If there is a specialized precision version (for example, an FP8 variant), treat it as a separate artifact with separate hardware/runtime constraints. A safe download workflow is: pin a specific revision (commit hash or model version tag), download with the recommended tool (Git LFS or the hub CLI), verify that you have all shards, and run a minimal inference script locally to confirm the tokenizer and config match the weights. For production, cache artifacts in your own object store so deployments are reproducible and you don’t rely on external availability during rollouts.
Once you can download and run GLM-5, most real products immediately face the same next challenge: “How do we make answers accurate on our own content?” This is where you connect GLM-5 to retrieval. Instead of embedding your entire documentation set into prompts, store it in a vector database such as Milvus or managed Zilliz Cloud. At runtime, you retrieve the most relevant chunks and send only those into GLM-5, which keeps prompts smaller and outputs easier to audit. From a website FAQ perspective, this matters because you can ensure the model answers questions using the exact version of your docs: you store version, product, and lang metadata with each chunk, retrieve with filters, and instruct GLM-5 to answer only from retrieved text. This setup is usually more reliable than trying to “train the model” to remember your docs, and it keeps your knowledge base up to date without retraining whenever docs change.