pi-ml-intern

ML Intern extension for Pi — autonomous ML research & implementation with literature-backed recipes. Heavily inspired by Hugging Face's ml-intern.

Only activates when you explicitly invoke /ml-intern, with no impact on Pi's default behavior.

⚠️ EXPERIMENTAL — This extension is under active development (v0.1.6). There may be bugs and performance may lag bahind the original project. Feedback and contributions welcome.

Quick Start

Install

pi install npm:@santiago-r/pi-ml-intern

Or manually: copy this directory to ~/.pi/agent/extensions/ml-intern/ (global) or .pi/extensions/ml-intern/ (project-local).

Usage example

/ml-intern Generate a state-of-the-art cardiac event classifier (multi-label) from publicly available ECG datasets. Emphasize optimization of low-level signal pre-processing. Emphasize explainability, output SHAP values alongside predictions. Evaluate on arrythmia detection ROC-AUC for a held-out test split.

Type /ml-intern followed by your ML task. Include concrete file paths, dataset formats, evaluation criteria, and any constraints. The more specific you are, the better the result.

Requirements

• Pi (coding agent harness)

Recommended access tokens

Set these for full capability. Without them, HF Hub lookups and GitHub code searches are rate-limited to public repos only, and HF Jobs is unavailable.

• HF_TOKEN — enables gated/private datasets, model downloads, and HF Jobs GPU training. Get one here.
• GITHUB_TOKEN — lifts GitHub API rate limits. Get one here.

Tokens are auto-loaded from .env in the working directory. No manual export needed.

What it does

When you use /ml-intern, the agent:

1. Researches first — finds landmark papers, crawls citation graphs, reads methodology sections
2. Validates resources — checks dataset schemas, model architectures, API compatibility
3. Implements — includes instructions against hallucinated imports, wrong arguments, and silent substitutions
4. Tracks progress — uses plan_tool for tasks with 3+ steps

Tools included

Tool	Description
plan_tool	Track ML task progress (pending/in_progress/completed)
hf_papers	Paper search, citation graphs, methodology section reading
hf_jobs	Submit & manage remote GPU/CPU compute jobs on HF Cloud
hub_repo_details	HF Hub model/dataset/space details and search
hf_inspect_dataset	Dataset schema, splits, and sample inspection
github_find_examples	Find working ML example scripts in GitHub repos
github_list_repos	Discover repos for GitHub orgs/users
github_read_file	Read file contents from GitHub repos
explore_hf_docs	Browse HF documentation structure
fetch_hf_docs	Fetch full HF documentation pages
find_hf_api	Search HF REST API endpoints
research	Spawn isolated sub-agent for deep literature research

Running headless (no TUI)

Prefer the CLI? Set ML_INTERN_FORCE=1 for print mode:

ML_INTERN_FORCE=1 pi -p "Train a small GPT on input_data/my_data.jsonl"

This activates all 12 research tools and the ml-intern system prompt for the duration of the command, then exits. Works in shell scripts, CI, cron jobs, or even in agent subtasks if you are crazy enough.

Attribution

Heavily inspired by Hugging Face's ml-intern (Apache 2.0). All original TypeScript code is Apache 2.0-licensed.

License

Apache 2.0 — see LICENSE.