Pioneer CLI

Command-line interface for the Pioneer AI training platform.

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Installation

npm

npm install -g @fastino-ai/pioneer-cli

Manual Install (requires Bun)

# Install Bun if needed
curl -fsSL https://bun.sh/install | bash

# Clone and install
git clone https://github.com/fastino-ai/pioneer-cli.git
cd pioneer-cli/
bun install

# Run directly
bun run src/index.tsx --help

Quick Install (coming soon)

curl -fsSL https://pioneer.ai/install.sh | sh

Usage

# Show help
pioneer --help

# Authentication
pioneer auth login       # Enter API key interactively
pioneer auth logout      # Clear stored API key
pioneer auth status      # Check if logged in

# Chat (interactive agent)
pioneer chat             # Start chat with AI agent

# Datasets
pioneer dataset list
pioneer dataset get <id>
pioneer dataset delete <id>
pioneer dataset download <id>
pioneer dataset analyze <id>

# Training Jobs
pioneer job list
pioneer job get <id>
pioneer job logs <id>
pioneer job create --model-name "My Model" --dataset-ids ds_123,ds_456

# Models
pioneer model list       # List deployed models
pioneer model trained    # List trained models
pioneer model delete <id>
pioneer model download <id>

End-to-End Example

This example shows the complete workflow from generating a dataset to training a model.

Step 1: Generate a Dataset

Generate a NER (Named Entity Recognition) dataset for medical text:

# Save locally to ./datasets/ (default)
pioneer dataset generate ner \
  --labels "DISEASE,SYMPTOM,TREATMENT,MEDICATION" \
  --num 50 \
  --domain "Medical clinical notes and patient records" \
  --name "medical-ner"

# Or save to remote database
pioneer dataset generate ner \
  --labels "DISEASE,SYMPTOM,TREATMENT,MEDICATION" \
  --num 50 \
  --domain "Medical clinical notes and patient records" \
  --save true \
  --name "Medical NER Dataset"

Or generate a classification dataset for sentiment analysis:

pioneer dataset generate classification \
  --labels "positive,negative,neutral" \
  --num 200 \
  --domain "Customer product reviews" \
  --name "sentiment"

Step 2: View Your Datasets

# List all datasets (both remote and local ./datasets/*.json)
pioneer dataset list

# Get details for a remote dataset
pioneer dataset get <dataset-id>

# Analyze dataset statistics
pioneer dataset analyze <dataset-id>

Step 3: Create a Training Job

Start training with one or more datasets:

pioneer job create \
  --model-name "Medical NER Model v1" \
  --dataset-ids "<dataset-id>" \
  --base-model "fastino/gliner2-base-v1" \
  --epochs 10

Step 4: Monitor Training Progress

# List all training jobs
pioneer job list

# Check job status
pioneer job get <job-id>

# View training logs
pioneer job logs <job-id>

Step 5: Use Your Trained Model

Once training completes:

# List trained models
pioneer model list trained

# Download the trained model
pioneer model download <job-id>

Configuration

The CLI stores configuration in ~/.pioneer/config.json.

Configuration File

The CLI stores your API key in ~/.pioneer/config.json after running pioneer auth login.

{
  "apiKey": "your-api-key",
  "hfToken": "your-hf-token"
}

Authentication

Pioneer API

To use the CLI, you'll need a Pioneer API key. Get one at https://app.pioneer.ai/api-keys .

# Login with your API key
pioneer auth login

# Check authentication status
pioneer auth status

# Logout (clears saved key)
pioneer auth logout

Hugging Face (Optional)

For pushing/pulling datasets and models to/from Hugging Face, you'll need an HF token. Get one at https://huggingface.co/settings/tokens .

# Set your Hugging Face token
pioneer auth hf

# Check token status
pioneer auth hf status

# Clear token
pioneer auth hf logout

Development & Testing

Building & Running

cd pioneer-cli
bun install
bun run dev          # Hot reload
bun run typecheck    # Type checking

Internal Testing Environment Variables

For Pioneer team members testing against local or staging environments:

| Variable | Description | Example | |----------|-------------|---------| | PIONEER_API_URL | Override API base URL | http://localhost:5001 | | PIONEER_API_KEY | Override saved API key | your-test-key | | HF_TOKEN | Override saved HF token | your-hf-token |

# Example: Test against local backend
export PIONEER_API_URL=http://localhost:5001
export PIONEER_API_KEY=your-local-test-key
pioneer dataset list

Note: External users should never need to set these variables. The CLI defaults to production (https://api.pioneer.ai) and uses pioneer auth login for authentication.

Tech Stack

• Runtime: Bun
• UI: Ink (React for CLI)
• Language: TypeScript

License

MIT