> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openinterpreter.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS Sagemaker

To use Open Interpreter with a model from AWS Sagemaker, set the `model` flag:

<CodeGroup>
  ```bash Terminal theme={null}
  interpreter --model sagemaker/<model-name>
  ```

  ```python Python theme={null}
  # Sagemaker requires boto3 to be installed on your machine:
  !pip install boto3

  from interpreter import interpreter

  interpreter.llm.model = "sagemaker/<model-name>"
  interpreter.chat()
  ```
</CodeGroup>

# Supported Models

We support the following completion models from AWS Sagemaker:

* Meta Llama 2 7B
* Meta Llama 2 7B (Chat/Fine-tuned)
* Meta Llama 2 13B
* Meta Llama 2 13B (Chat/Fine-tuned)
* Meta Llama 2 70B
* Meta Llama 2 70B (Chat/Fine-tuned)
* Your Custom Huggingface Model

<CodeGroup>
  ```bash Terminal theme={null}
  interpreter --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b
  interpreter --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f
  interpreter --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b
  interpreter --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b-f
  interpreter --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b
  interpreter --model sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b-b-f
  interpreter --model sagemaker/<your-huggingface-deployment-name>
  ```

  ```python Python theme={null}
  interpreter.llm.model = "sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b"
  interpreter.llm.model = "sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b-f"
  interpreter.llm.model = "sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b"
  interpreter.llm.model = "sagemaker/jumpstart-dft-meta-textgeneration-llama-2-13b-f"
  interpreter.llm.model = "sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b"
  interpreter.llm.model = "sagemaker/jumpstart-dft-meta-textgeneration-llama-2-70b-b-f"
  interpreter.llm.model = "sagemaker/<your-huggingface-deployment-name>"
  ```
</CodeGroup>

# Required Environment Variables

Set the following environment variables [(click here to learn how)](https://chat.openai.com/share/1062cdd8-62a1-4aa8-8ec9-eca45645971a) to use these models.

| Environment Variable    | Description                                     | Where to Find                                                                       |
| ----------------------- | ----------------------------------------------- | ----------------------------------------------------------------------------------- |
| `AWS_ACCESS_KEY_ID`     | The API access key for your AWS account.        | [AWS Account Overview -> Security Credentials](https://console.aws.amazon.com/)     |
| `AWS_SECRET_ACCESS_KEY` | The API secret access key for your AWS account. | [AWS Account Overview -> Security Credentials](https://console.aws.amazon.com/)     |
| `AWS_REGION_NAME`       | The AWS region you want to use                  | [AWS Account Overview -> Navigation bar -> Region](https://console.aws.amazon.com/) |