replicate-cli

Original：🇺🇸 English

Translated

This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.

2installs

Sourcerawveg/skillsforge-marketplace

Added on2026-02-16

NPX Install

npx skill4agent add rawveg/skillsforge-marketplace replicate-cli

SKILL.md Content

View Translation Comparison →

Replicate CLI

The Replicate CLI is a command-line tool for interacting with Replicate's AI model platform. It enables running predictions, managing models, creating deployments, and fine-tuning models directly from the terminal.

Authentication

Before using the Replicate CLI, set the API token:

bash

export REPLICATE_API_TOKEN=<token-from-replicate.com/account>

Alternatively, authenticate interactively:

bash

replicate auth login

Verify authentication:

bash

replicate account current

Core Commands

Running Predictions

The primary use case is running predictions against hosted models.

Basic prediction:

bash

replicate run <owner/model> input_key=value

Examples:

Image generation:

bash

replicate run stability-ai/sdxl prompt="a studio photo of a rainbow colored corgi"

Text generation with streaming:

bash

replicate run meta/llama-2-70b-chat --stream prompt="Tell me a joke"

Prediction flags:

```
--stream
```
- Stream output tokens in real-time (for text models)
```
--no-wait
```
- Submit prediction without waiting for completion
```
--web
```
- Open prediction in browser
```
--json
```
- Output result as JSON
```
--save
```
- Save outputs to local directory

--output-directory <dir>

- Specify output directory (default:

./{prediction-id}

)

Input Handling

File uploads: Prefix local file paths with

bash

replicate run nightmareai/real-esrgan image=@photo.jpg

Output chaining: Use

{{.output}}

template syntax to chain predictions:

bash

replicate run stability-ai/sdxl prompt="a corgi" | \
replicate run nightmareai/real-esrgan image={{.output[0]}}

Model Operations

View model schema (see required inputs and outputs):

bash

replicate model schema <owner/model>
replicate model schema stability-ai/sdxl --json

List models:

bash

replicate model list
replicate model list --json

Show model details:

bash

replicate model show <owner/model>

Create a new model:

bash

replicate model create <owner/name> \
  --hardware gpu-a100-large \
  --private \
  --description "Model description"

Model creation flags:

```
--hardware <sku>
```
- Hardware SKU (see
```
references/hardware.md
```
)
```
--private
```
/
```
--public
```
- Visibility setting
```
--description <text>
```
- Model description
```
--github-url <url>
```
- Link to source repository
```
--license-url <url>
```
- License information
```
--cover-image-url <url>
```
- Cover image for model page

Training (Fine-tuning)

Fine-tune models using the training command:

bash

replicate train <base-model> \
  --destination <owner/new-model> \
  input_key=value

Example - Fine-tune SDXL with DreamBooth:

bash

replicate train stability-ai/sdxl \
  --destination myuser/custom-sdxl \
  --web \
  input_images=@training-images.zip \
  use_face_detection_instead=true

List trainings:

bash

replicate training list

Show training details:

bash

replicate training show <training-id>

Deployments

Deployments provide dedicated, always-on inference endpoints with predictable performance.

Create deployment:

bash

replicate deployments create <name> \
  --model <owner/model> \
  --hardware <sku> \
  --min-instances 1 \
  --max-instances 3

Example:

bash

replicate deployments create text-to-image \
  --model stability-ai/sdxl \
  --hardware gpu-a100-large \
  --min-instances 1 \
  --max-instances 5

Update deployment:

bash

replicate deployments update <name> \
  --max-instances 10 \
  --version <version-id>

List deployments:

bash

replicate deployments list

Show deployment details and schema:

bash

replicate deployments show <name>
replicate deployments schema <name>

Hardware

List available hardware options:

bash

replicate hardware list

See

references/hardware.md

for detailed hardware information and selection guidelines.

Scaffolding

Create a local development environment from an existing prediction:

bash

replicate scaffold <prediction-id-or-url> --template=<node|python>

This generates a project with the prediction's model and inputs pre-configured.

Command Aliases

For convenience, these aliases are available:

Alias	Equivalent Command
`replicate run`	`replicate prediction create`
`replicate stream`	`replicate prediction create --stream`
`replicate train`	`replicate training create`

Short aliases for subcommands:

```
replicate m
```
=
```
replicate model
```
```
replicate p
```
=
```
replicate prediction
```
```
replicate t
```
=
```
replicate training
```
```
replicate d
```
=
```
replicate deployments
```
```
replicate hw
```
=
```
replicate hardware
```
```
replicate a
```
=
```
replicate account
```

Common Workflows

Image Generation Pipeline

Generate an image and upscale it:

bash

replicate run stability-ai/sdxl \
  prompt="professional photo of a sunset" \
  negative_prompt="blurry, low quality" | \
replicate run nightmareai/real-esrgan \
  image={{.output[0]}} \
  --save

Check Model Inputs Before Running

Always check the model schema to understand required inputs:

bash

replicate model schema owner/model-name

Batch Processing

Run predictions and save outputs:

bash

for prompt in "cat" "dog" "bird"; do
  replicate run stability-ai/sdxl prompt="$prompt" --save --output-directory "./outputs/$prompt"
done

Monitor Long-Running Tasks

Submit without waiting, then check status:

bash

# Submit
replicate run owner/model input=value --no-wait --json > prediction.json

# Check status later
replicate prediction show $(jq -r '.id' prediction.json)

Best Practices

Always check schema first - Run
```
replicate model schema <model>
```
to understand required and optional inputs before running predictions.
Use streaming for text models - Add
```
--stream
```
flag when running language models to see output in real-time.
Save outputs explicitly - Use
```
--save
```
and
```
--output-directory
```
to organize prediction outputs.
Use JSON output for automation - Add
```
--json
```
flag when parsing outputs programmatically.
Open in web for debugging - Add
```
--web
```
flag to view predictions in the Replicate dashboard for detailed logs.
Chain predictions efficiently - Use the
```
{{.output}}
```
syntax to pass outputs between models without intermediate saves.

Troubleshooting

Authentication errors:

Verify
```
REPLICATE_API_TOKEN
```
is set correctly
Run
```
replicate account current
```
to test authentication

Model not found:

Check model name format:
```
owner/model-name
```
Verify model exists at replicate.com

Input validation errors:

Run
```
replicate model schema <model>
```
to see required inputs
Check input types (string, number, file)

File upload issues:

Ensure
```
@
```
prefix is used for local files
Verify file path is correct and file exists

Additional Resources

Replicate documentation: https://replicate.com/docs
Model explorer: https://replicate.com/explore
API reference: https://replicate.com/docs/reference/http
GitHub repository: https://github.com/replicate/cli

replicate-cli

NPX Install

Tags

SKILL.md Content

Replicate CLI

Authentication

Core Commands

Running Predictions

Input Handling

Model Operations

Training (Fine-tuning)

Deployments

Hardware

Scaffolding

Command Aliases

Common Workflows

Image Generation Pipeline

Check Model Inputs Before Running

Batch Processing

Monitor Long-Running Tasks

Best Practices

Troubleshooting

Additional Resources