autohf 1.0.0

One-line AutoML: from idea to trained model using Hugging Face + AutoGluon

AutoHF

One-line AutoML: from idea to trained model using Hugging Face + AutoGluon.

AutoHF is an autonomous machine learning pipeline that takes a natural language description of a task (e.g., "sentiment analysis") and automatically finds the best datasets on Hugging Face, ranks them by quality, and trains a state-of-the-art model using AutoGluon.

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Features

• Intent-to-Task: Automatically detects ML task types (classification, regression, etc.) and keywords from natural language.
• Autonomous Dataset Discovery: Searches the Hugging Face Hub for relevant datasets using multi-strategy search.
• Intelligent Ranking: Ranks datasets based on quality signals like downloads, likes, and metadata completeness.
• Automated Training: Leverages AutoGluon to train high-quality models with minimal configuration.
• Agentic Architecture: Inspired by patterns from AutoGen, LangGraph, and OpenHands for robust state management and collaboration.
• Interactive Gemma Chat: Run a single prompt or start an interactive chat session with local Gemma models.

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Installation

# Basic installation
pip install autohf

# With training support (recommended)
pip install "autohf[train]"

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CLI Quick Start (Step-by-Step)

AutoHF provides a simple command-line interface:

Step 1: Detect and Train a Model

To find the best datasets and train a model directly from a task description:

autohf train "sentiment analysis"

Or with custom presets and training limits:

autohf train "spam detection" --preset high_quality --time-limit 600

Step 2: Search and Rank Datasets

If you only want to discover and rank the top Hugging Face datasets for your task without training:

autohf search "question answering"

You can also list top models for the task:

autohf search "question answering" --models

Step 3: Interactive local Gemma Chat

To query or chat with a local Gemma model (such as google/gemma-4-E2B-it):

# Start an interactive multi-turn chat REPL session
autohf chat

# Or run a single prompt query directly
autohf chat "Explain AutoML in one sentence."

Note: Make sure your HF_TOKEN environment variable is set to download the model.

Step 4: Show package info and supported task types

autohf info

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Python API Usage

from autohf import AutoHF

# Initialize and train
hf = AutoHF.from_preset("medium_quality")
result = hf.train("customer review classification")

# Access results
print(f"Best model: {result.best_model_name}")
print(f"Accuracy: {result.metrics['accuracy']}")
print(f"Model saved at: {result.model_path}")

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License

MIT License. See LICENSE for details.