> For the complete documentation index, see [llms.txt](https://docs.gm.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.gm.ai/gmai-ecosystem/gminfra/gmmodel.md).

# gmMODEL

gmINFRA is an **advanced infrastructure platform** designed by GM.AI to support seamless and secure interactions with blockchain technology through AI-driven tools. At its core is **gm-01-8B,** a large language model developed by GM.AI, which is currently in beta and freely available for all users. This model is pretrained and instruction-tuned, capable of generative text functions and supporting blockchain function calls.

{% content-ref url="/pages/5440QKLsAl6Wi9b4YlE3" %}
[Features](/gmai-ecosystem/gminfra/gmmodel/features.md)
{% endcontent-ref %}

{% content-ref url="/pages/zXiTBQROKKvaxYAoNWhu" %}
[Developer Guideline](/gmai-ecosystem/gminfra/gmmodel/developer-guideline.md)
{% endcontent-ref %}

{% content-ref url="/pages/QjgKVRkUWLE3Mg6wPdjG" %}
[gmiNFRA Chatbot](/gmai-ecosystem/gminfra/gmmodel/gminfra-chatbot.md)
{% endcontent-ref %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://docs.gm.ai/gmai-ecosystem/gminfra/gmmodel.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

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.
