# Cerebras Inference

> Use this to write code to call an LLM using LiteLLM and OpenRouter with the Cerebras inference provider

- Skill: `tomevault-io/cerebras-inference` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/cerebras-inference`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/cerebras-inference/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/cerebras-inference

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# Calling an LLM via Cerebras

These instructions allow you write code to call an LLM with Cerebras specified as the inference provider.  
This method uses LiteLLM and OpenRouter.

## Setup

The OPENROUTER_API_KEY must be set in the .env file and loaded in as an environment variable.  

The uv project must include litellm and pydantic.
`uv add litellm pydantic`

## Code snippets

Use code like these examples in order to use Cerebras.

### Imports and constants

```python
from litellm import completion
MODEL = "openrouter/openai/gpt-oss-120b"
EXTRA_BODY = {"provider": {"order": ["cerebras"]}}
```

### Code to call via Cerebras for a text response

```python
response = completion(model=MODEL, messages=messages, reasoning_effort="low", extra_body=EXTRA_BODY)
result = response.choices[0].message.content
```

### Code to call via Cerebras for a Structured Outputs response

```python
response = completion(model=MODEL, messages=messages, response_format=MyBaseModelSubclass, reasoning_effort="low", extra_body=EXTRA_BODY)
result = response.choices[0].message.content
result_as_object = MyBaseModelSubclass.model_validate_json(result)
```

---
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<!-- tomevault:4.0:skill_md:2026-04-14 -->