pyautollm 1.1.0

AI-Native LLM Training & Infrastructure Abstraction Library

PyAutoLLM

AI-Native LLM Training & Infrastructure Abstraction Library

Fine-tune open-weight LLMs with three lines of Python code. No ML expertise, no infrastructure knowledge required.

import pytune

model = pytune.models.llama2_7b()
model.add_data("./my-documents")
trained_model = model.train()

That's it. The system automatically:

• Analyzes your data
• Selects the best fine-tuning method
• Picks the cheapest GPU infrastructure
• Handles all orchestration
• Evaluates quality
• Returns a ready-to-use model

Why PyAutoLLM?

• No Jargon: You don't need to know LoRA, QLoRA, distributed training, or GPUs
• Guided Setup: Step-by-step instructions for infrastructure (RunPod, Lambda Labs, AWS)
• Transparent Costs: See exactly what you'll pay before training
• Smart Defaults: Recommended settings work for 95% of users
• Open Source: No vendor lock-in; runs anywhere (Ollama, vLLM, HF Inference)
• Three-Tier Pricing:
  • Cheap & Fast: $10, 2 hours, 90% quality (QLoRA on single GPU)
  • Balanced: $30, 5 hours, 95% quality (LoRA, recommended)
  • Best Quality: $80, 12 hours, 98% quality (Full retraining)

Quick Start

Installation (Coming Soon)

pip install pyautollm

Basic Workflow

import pytune

# 1. Create a model
model = pytune.models.llama2_7b()

# 2. Add your data (PDF, DOCX, CSV, TXT, Markdown, etc.)
model.add_data("./documents")

# 3. See recommended training options
recommendation = model.plan()
# Output:
# Cheap & Fast:    $10, 2h, 90% quality
# Balanced (*)     $30, 5h, 95% quality
# Best Quality:    $80, 12h, 98% quality

# 4. Train with recommended settings (or pick another tier)
trained_model = model.train()

# 5. Use your trained model
response = trained_model.generate("Your prompt here")

First Time Setup (RunPod)

# First time only: PyAutoLLM guides you through RunPod setup
# 1. Go to https://www.runpod.io
# 2. Create a pod with H100 GPU
# 3. Copy your API token
# 4. Paste it when prompted

# After setup, credentials are cached
model.train()  # Runs immediately on second call

Documentation

• PRODUCT_VISION.md — What is PyAutoLLM and why it matters
• ROADMAP.md — Development phases, timeline, and deliverables

Project Status

🚧 Currently in development: Phase 1 (MVP) — 4 weeks to beta release (v0.1.0)

Milestones:

• Phase 1 (Jul-Aug): Single model, RunPod only, guided UX
• Phase 2 (Aug): Multi-model support
• Phase 3 (Sep): Multi-provider, advanced methods
• Phase 4 (Oct): Polish, production-ready v1.0

Architecture

PyAutoLLM is built with:

• Rust: High-performance orchestration layer (infrastructure, training coordination)
• Python: User-facing API, data analysis, ML integrations (PyO3 bindings)
• Unsloth: Fast fine-tuning engine
• TRL: Hugging Face training utilities
• Docling: Document parsing

Python API (user-friendly, guided UX)
    ↓
Rust Orchestrator (performance layer)
    ├─ Pod management (RunPod, Lambda, AWS)
    ├─ Cost calculation
    ├─ Training coordination
    └─ Monitoring & logging
    ↓
Training Engine (Unsloth + TRL)

Features (MVP Phase 1)

• ✅ Single-model fine-tuning (Llama 2 7B)
• ✅ Data analysis (quality scoring, deduplication)
• ✅ Intelligent method selection (QLoRA vs LoRA)
• ✅ Cost forecasting (±10% accuracy)
• ✅ RunPod integration with guided setup
• ✅ Automatic evaluation (LLM-as-Judge)
• ✅ Training monitoring & logging

Coming Soon (Phase 2-4):

• Multi-model support (Mistral, LLaMA 3, Phi, etc.)
• Multi-provider infrastructure (Lambda Labs, AWS, GCP)
• Advanced training methods (DPO, continued pretraining, multi-GPU)
• Inference abstraction (vLLM, Ollama, HF Inference)
• Model versioning & registry
• Production monitoring & A/B testing

Use Cases

• Customer Support Agents: Fine-tune on your support documentation
• Domain Experts: Create specialized assistants (legal, medical, financial)
• Content Generation: Adapt models to your brand voice
• Internal Tools: Build AI features without infrastructure expertise
• Research: Experiment with fine-tuning without DevOps headaches

Contributing

This is an open-source project. We welcome contributions!

• 📖 Documentation improvements
• 🧪 Tests and examples
• 🚀 Performance optimization
• 🐛 Bug reports and fixes

License

MIT License — See LICENSE file for details.

Contact & Community

• Issues: Report bugs and request features on GitHub
• Discussions: Join our community discussions
• Twitter: @PyAutoLLM (coming soon)

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Status: v0.1.0-beta (in development)
Start Date: 2026-07-21
Target Release: 2026-10-01