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@DWE-CLOUD DWE-CLOUD released this 16 Nov 11:06
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NoLess CLI v0.1.1-alpha - Initial Alpha Release

Release Date: November 16, 2025
Status: Alpha (Experimental)

Introduction

Welcome to the first alpha release of NoLess - an intelligent CLI tool that revolutionizes ML model development by combining multi-agent architecture with local LLMs. This alpha release is feature-complete but still under active testing.

Alpha Release Notice

This is an alpha release intended for:

  • Early adopters and testers
  • Developers who want to experiment with AI-powered code generation
  • Contributors interested in shaping the tool's future

Known limitations:

  • Some edge cases in dataset downloading may fail
  • Generated code quality varies with model choice
  • Performance not yet optimized for all scenarios

We welcome feedback and bug reports!

Core Features

1. Autopilot Mode - One Command to Rule Them All

Build complete ML projects with a single command:

noless autopilot -d "classify dog breeds from images"

What happens behind the scenes:

  • LLM analyzes your description
  • Asks clarifying questions
  • Searches 20,000+ datasets
  • Designs optimal architecture
  • Generates production-ready code
  • Creates tests and documentation

2. Multi-Agent System (6 Specialized Agents)

Agent Role Key Features
Orchestrator Task coordination Plans workflow, manages execution order
DatasetAgent Data discovery Searches OpenML, Kaggle, HuggingFace, UCI
ModelAgent Architecture design Recommends ResNet, BERT, LSTM, etc.
CodeAgent Implementation Generates model.py, train.py with 100-200 lines
EvaluationAgent Testing Creates pytest suites with fixtures
OptimizationAgent Hyperparameter tuning Suggests learning rates, batch sizes

All agents work autonomously and communicate via structured protocols.

3. Interactive Feedback Loop

Real-time collaboration with AI:

noless autopilot -i -d "sentiment classifier for tweets"

During interaction:

  • View syntax-highlighted code previews
  • Give feedback in natural language ("add dropout layers")
  • Request AI review from larger expert models
  • Accept or regenerate until perfect
  • Iterate up to 10 times

4. Local LLM Integration (Ollama)

Privacy-first AI with no cloud dependencies:

Supported models:

  • deepseek-r1:1.5b - Fast, lightweight (recommended for generation)
  • deepseek-coder:6.7b - Code-specialized
  • llama3.1:8b - Balanced quality/speed
  • mixtral:8x7b - Expert-grade (recommended for review)
  • llama3.1:70b - Maximum quality (if you have 48GB+ RAM)

Dual-model system:

noless autopilot \
  --llm-model deepseek-r1:1.5b \      # Fast generation
  --reviewer-model mixtral:8x7b        # Expert review

5. Multi-Source Dataset Discovery

Search across 4 major repositories simultaneously:

Source Datasets Best For
OpenML 5,000+ Tabular, classification
HuggingFace 10,000+ NLP, vision
Kaggle 50,000+ Competitions, real-world
UCI ML 500+ Classic ML benchmarks 
noless search "medical imaging chest x-ray" --limit 20

Features:

  • Smart keyword extraction using LLMs
  • Automatic ranking by relevance
  • One-click download and caching
  • Metadata extraction (features, samples, task type)

6. Complete Project Generation

Every generated project includes:

my_classifier/
├── model.py              # 100-200 lines
│   ├── Complete architecture (ResNet, BERT, etc.)
│   ├── Forward pass implementation
│   ├── Weight initialization
│   └── Type hints & docstrings
│
├── train.py              # 150-250 lines
│   ├── Data loading & augmentation
│   ├── Training loop with progress bars
│   ├── Validation & metrics
│   ├── Checkpointing & early stopping
│   └── Logging (TensorBoard compatible)
│
├── test_model.py         # 50-100 lines
│   ├── Pytest fixtures
│   ├── Model initialization tests
│   ├── Forward pass tests
│   ├── Shape validation
│   └── Dataset loading tests
│
├── config.yaml           # Hyperparameters
│   ├── Model settings (layers, dropout, etc.)
│   ├── Training config (epochs, batch size, LR)
│   └── Data splits (train/val/test)
│
├── requirements.txt      # All dependencies
└── README.md             # Complete documentation

Code quality guarantees:

  • No TODO comments or placeholders
  • Complete implementations
  • Error handling included
  • Type hints everywhere
  • Comprehensive docstrings
  • Follows PEP 8 style

Installation

Option 1: Install from PyPI (recommended)

pip install noless
noless --version

Option 2: Install from source

git clone https://github.com/yourusername/NoLess.git
cd NoLess
pip install -e .

Required: Install Ollama

# macOS/Linux
curl https://ollama.ai/install.sh | sh

# Windows
# Download from: https://ollama.com

# Pull at least one model
ollama pull deepseek-r1:1.5b

Optional: Configure API Keys

For Kaggle datasets:

# 1. Create account at kaggle.com
# 2. Go to Account → API → Create New Token
# 3. Place kaggle.json in:
#    Linux/macOS: ~/.kaggle/kaggle.json
#    Windows: %USERPROFILE%\.kaggle\kaggle.json

For HuggingFace datasets:

pip install datasets
# No API key needed for public datasets

Quick Start Guide

1. Autopilot (Easiest)

Let AI do everything:

noless autopilot -d "classify handwritten digits"

2. Interactive Autopilot

Collaborate with AI:

noless autopilot -i -d "sentiment analysis on movie reviews"

3. Quick Mode (Skip Questions)

noless autopilot --skip-followups -d "object detection for cars"

4. With Specific Models

noless autopilot \
  --llm-model deepseek-r1:1.5b \
  --reviewer-model mixtral:8x7b \
  -d "time series forecasting for stock prices"

5. Manual Project Creation

noless create \
  -t image-classification \
  -f pytorch \
  -a resnet50 \
  --agents \
  --output ./my_project

6. Search Datasets Only

noless search "cifar10" --source openml
noless search "imdb sentiment" --source huggingface

7. Interactive Menu (For Beginners)

noless interactive

Supported Tasks & Architectures

Computer Vision

Tasks: Classification, Detection, Segmentation
Architectures:

  • ResNet (18, 34, 50, 101, 152)
  • EfficientNet (B0-B7)
  • Vision Transformer (ViT-Base, ViT-Large)
  • YOLOv8 (for detection)
  • U-Net (for segmentation)

Natural Language Processing

Tasks: Classification, Sequence-to-Sequence, NER
Architectures:

  • BERT (Base, Large)
  • RoBERTa (Base, Large)
  • DistilBERT
  • T5 (Small, Base, Large)
  • GPT-2

Time Series

Tasks: Forecasting, Anomaly Detection
Architectures:

  • LSTM, GRU
  • Temporal CNNs
  • Transformers
  • Prophet

Tabular Data

Tasks: Classification, Regression
Algorithms:

  • XGBoost, LightGBM, CatBoost
  • Random Forest, Gradient Boosting
  • Neural Networks (TabNet, FT-Transformer)

System Requirements

Minimum

  • Python: 3.8+
  • RAM: 8GB
  • Disk: 5GB free
  • OS: Windows 10+, macOS 10.15+, Linux

Recommended

  • Python: 3.10+
  • RAM: 16GB (for 13B+ models)
  • Disk: 20GB+ (for datasets)
  • GPU: CUDA-capable (optional, for faster training)

For Large Models (70B+)

  • RAM: 48GB+
  • GPU: 80GB VRAM (A100) or CPU with swap

Performance Benchmarks (Alpha)

Code Generation Speed

Model Time Quality
deepseek-r1:1.5b ~30s Good
llama3.1:8b ~60s Very Good
mixtral:8x7b ~120s Excellent

Dataset Search

Source Avg Response Results
OpenML ~2s 50-100
HuggingFace ~3s 20-50
Kaggle ~5s 30-80

Benchmarks on AMD Ryzen 9 7950X, 64GB RAM

Known Issues (Alpha)

High Priority

  1. OpenML large dataset downloads timeout - Workaround: Use smaller datasets or download manually
  2. Kaggle API requires manual setup - Must place kaggle.json in ~/.kaggle/
  3. Generated code may have minor import errors - AI reviewer catches most, but check before running

Medium Priority

  1. Large models (70B) are very slow - Consider smaller models or cloud inference
  2. Some architecture combinations not tested - E.g., BERT + Kaggle dataset
  3. Windows terminal Unicode issues - Use --no-startup flag if you see garbled characters

Low Priority

  1. Progress bars flicker on some terminals - Cosmetic issue only
  2. Agent communication logs are verbose - Will add --quiet flag in beta

Roadmap to Beta (v0.2)

Planned Features

  • Auto-train after generation - Run training immediately after code gen
  • Model fine-tuning workflows - Update existing models with new data
  • Distributed training - Multi-GPU and multi-node support
  • Web UI - Browser-based interface for non-CLI users
  • More dataset sources - Papers with Code, Roboflow, etc.
  • Cloud deployment - One-click deploy to AWS/GCP/Azure
  • Experiment tracking - Integration with MLflow, Weights & Biases
  • Custom templates - User-defined architecture templates

Performance Improvements

  • Parallel agent execution
  • Streaming dataset downloads
  • Code generation caching
  • Better error recovery

Quality Improvements

  • More comprehensive tests for generated code
  • Better prompt engineering for LLMs
  • Architecture validation before generation
  • Automatic dependency resolution

Testing & Validation

Test Coverage

  • Unit tests for core modules (70%+ coverage)
  • Integration tests for agent system
  • End-to-end autopilot workflow tests
  • Generated code tests (manual verification only in alpha)

Validated Combinations

Tested and working:

  • PyTorch + OpenML + Image Classification
  • TensorFlow + HuggingFace + Text Classification
  • scikit-learn + Kaggle + Regression

Not yet fully tested:

  • Object detection workflows
  • Time series with external datasets
  • Custom architectures

Documentation & Resources

Examples

See examples/ directory for:

  • Basic usage scripts
  • Config file examples
  • Custom workflow demonstrations

Contributing (Alpha Testers Welcome!)

We need help testing and improving NoLess! Areas to contribute:

High Priority

  • Bug reports (especially edge cases)
  • Documentation improvements
  • Test coverage expansion
  • Windows compatibility testing

Medium Priority

  • New architecture support
  • More dataset source integrations
  • UI/UX improvements
  • Internationalization

How to Contribute

  1. Star the repository
  2. Try the tool and file issues
  3. Submit PRs (see CONTRIBUTING.md)
  4. Share feedback in Discussions

Community & Support

Getting Help

  • Bug Reports: Open an issue on GitHub
  • Feature Requests: Use Discussions
  • Questions: Check existing issues or ask in Discussions
  • Real-time Chat: Join our Discord (coming soon)

Sharing Your Experience

We'd love to hear:

  • What models you built
  • Issues you encountered
  • Features you need
  • Success stories

Tag us on Twitter: @NoLessCLI

License

MIT License - See LICENSE file

Free for:

  • Personal use
  • Commercial use
  • Modification
  • Distribution

Acknowledgments

Built with amazing open-source tools:

Special thanks to the AI/ML open-source community!

🎉 Try It Now!

# Install
pip install noless

# Start Ollama
ollama serve

# Pull a model
ollama pull deepseek-r1:1.5b

# Build your first ML model!
noless autopilot -i -d "your ML task description here"

Build AI models without limits! 🚀

Version Info

  • Version: 0.1.0-alpha
  • Release Date: November 16, 2025
  • Python: 3.8+
  • License: MIT
  • Status: Alpha (Breaking changes may occur)

Important: This is an alpha release. APIs and command-line interfaces may change in future versions. Pin your version if using in production.

Questions? Feedback? Issues?
Open an issue: https://github.com/DWE-CLOUD/NoLess/issues/new

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