CLI Reference

The Distil CLI is a command-line tool for fine-tuning compact language models using the distil labs platform. It enables you to train specialized models up to 70x smaller than teacher models while maintaining comparable accuracy, without requiring ML expertise.

Installation

Install the Distil CLI with a single command:

curl -fsSL https://cli-assets.distillabs.ai/install.sh | sh

Supported Platforms

The Distil CLI supports the following operating systems:

Platform	Supported
Linux (x86_64)	Yes
macOS (Intel)	Yes
macOS (Apple Silicon)	Yes
Windows	No

Claude Skill

Use our Claude Skill to train models directly from Claude Code or Claude.ai. The skill teaches Claude how to guide you through the entire training workflow.

Installation

Claude Code:

/plugin marketplace add https://github.com/distil-labs/distil-cli-skill
/plugin install distil-cli@distil-cli-skill

Claude.ai / Claude Desktop:

Download the skill as ZIP from its GitHub page or directly here.
Go to claude.ai → Settings → Capabilities → Skills
Click “Upload skill” and select the ZIP file
Toggle the skill ON

Capabilities

Environment	What Claude Can Do
Claude Code	Full end-to-end workflow: task selection, data preparation, running CLI commands, training, and deployment
Claude.ai	Task selection and data preparation: help choose the right task type and create data files. You run CLI commands yourself.

Usage

Once installed, ask Claude to help you train a model:

“Help me train a classification model for customer support intent detection”

Claude will guide you through creating a model, preparing data files, uploading data, running teacher evaluation, training, and deployment.

Model Identifiers

When working with the Distil platform, you’ll encounter several types of identifiers. Understanding these is key to navigating the CLI effectively.

Model Name

The model name is a human-readable identifier you choose when creating a model. It helps you organize and recognize your models.

distil model create my-qa-model

Model names should be descriptive of the task or project (e.g., customer-support-classifier, product-qa-bot).

Model ID

The model ID is a unique identifier automatically assigned when you create a model. This is the primary identifier used in most CLI commands.

# Create a model and receive its ID
distil model create my-model-name
# Output: Model created with ID: d64ee301-76d2-4f06-8e7d-398e40c0d7de

# Use the model ID in subsequent commands
distil model show d64ee301-76d2-4f06-8e7d-398e40c0d7de

You can find your model IDs by listing all models:

distil model list

Component IDs

Each model tracks multiple components through the training workflow. These component IDs are automatically managed but useful for debugging and API integration:

Component	Description
Upload ID	Identifies a specific data upload. Created when you run `upload-data`. A model can have multiple uploads if you iterate on your data.
Teacher Evaluation ID	Identifies a teacher evaluation run. Created when you run `run-teacher-evaluation`. Shows how well the teacher model performs on your task.
Training ID	Identifies a training job. Created when you run `run-training`. Tracks the distillation process that creates your small model.

View all component IDs for a model with:

distil model show <model-id>

CLI Commands

Authentication

Command	Description
`distil login`	Authenticate with the distil labs platform. Opens a browser for login.
`distil register`	Create a new distil labs account.
`distil whoami`	Display the currently authenticated user.
`distil logout`	Log out from the platform and clear credentials.

Model Management

Command	Description
`distil model create <name>`	Create a new model with the specified name. Returns the model ID.
`distil model list`	List all your models with their IDs, names, and status.
`distil model show <model-id>`	Show detailed information about a specific model, including all component IDs.

Data Upload

Command	Description
`distil model upload-data <model-id> --data <directory>`	Upload all data files from a directory. Expects standard filenames (`job_description.json`, `train.csv`, `test.csv`, `config.yaml`).
`distil model upload-data <model-id> --job-description <file> --train <file> --test <file> [--config <file>] [--unstructured <file>]`	Upload data files individually with explicit paths.
`distil model download-data <model-id>`	Download the uploaded data files for a model.

Teacher Evaluation

Command	Description
`distil model run-teacher-evaluation <model-id>`	Start a teacher evaluation to validate that a large model can solve your task.
`distil model teacher-evaluation <model-id>`	Check the status and results of the teacher evaluation.

Training

Command	Description
`distil model run-training <model-id>`	Start training to distill knowledge into a compact model.
`distil model training <model-id>`	Check the status and results of the training job.

Model Deployment

Command	Description
`distil model deploy local <model-id>`	Deploy a model locally (experimental). Requires llama-cpp on your machine.
`distil model deploy remote <model-id>`	Deploy a model to distil labs playground infrastructure.
`distil model invoke <model-id>`	Python client script to invoke a deployed model.

Model Download

Command	Description
`distil model download <model-id>`	Download your trained model.

Global Options

Option	Description
`--output json`	Output results in JSON format for scripting and automation.
`--help`	Display help information for any command.

Update & Docs

Command	Description
`distil docs`	Opens distil documentation in your default browser.
`distil model update`	Update distil CLI to latest version.

Next Steps

Account and Authentication

How to train your SLM?