> For the complete documentation index, see [llms.txt](https://machine-learning.paperspace.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://machine-learning.paperspace.com/wiki/artifacts.md).

# Artifacts

Artifacts is common ML term used to describe the output created by the training process.
  The output could be a fully trained model, a model checkpoint (for resuming training later), or simply a file created during the training process such as an image generated while training a [Generative Adversarial Network](/wiki/generative-adversarial-network-gan.md) (GAN).  

In the case of a Deep Learning model, the model artifacts are the trained weights stored in a binary format.

## Artifacts + Gradient

Gradient makes artifact management seamless and intuitive.  Anything saved in the `/artifacts` directory will be automatically captured in Gradient as an [artifact](https://docs.paperspace.com/gradient/data/storage#artifact-storage).  Model artifacts are automatically captured when saved to the `/models` directory.

#### Related Materials

{% embed url="<https://docs.paperspace.com/gradient/data/storage#artifact-storage>" %}


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# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://machine-learning.paperspace.com/wiki/artifacts.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.