naply 4.3.1

🚀 The Most Powerful AI Framework: Build & Fine-Tune ChatGPT-Level Models - CPU-Optimized, LoRA/QLoRA, Production-Ready

🚀 NAPLY - The Most Powerful AI Framework

Build ChatGPT-level AI models from scratch. CPU-optimized. Production-ready. Lightweight.

pip install naply --upgrade

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⚡ Quick Start - Just 2 Lines!

Standard Model

import naply

# Train your AI (1 line!)
model = naply.Model("medium")
model.train("my_data/", epochs=5)

# Chat with your AI (1 line!)
response = model.chat("Hello! How are you?")

Powerful Model (All 10 Methods!)

from naply.powerful_text_model import PowerfulTextModel

# Most powerful model with all 10 training methods
model = PowerfulTextModel("medium")
model.train("my_data/", epochs=10, method="all")  # Uses all 10 methods!

# Natural language generation (no gibberish!)
response = model.chat("Hello! How are you?")

That's it! Super simple! 🎉

Even Simpler - 1 Function!

import naply.easy as ai

# Train and chat in one go!
ai.quick_start("my_data/", epochs=5)
# Then just type your questions!

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🚀 NEW: Fine-tuning Framework (v4.3.0)

Fine-tune any AI model (LLaMA, GPT, Unsloth, etc.) on your own data with LoRA/QLoRA efficiency.

One-Liner Fine-tuning

import naply

# Fine-tune a base model on your data (auto-saves to "finetune_output")
# Supports: jsonl, csv, txt, parquet, folders
model = naply.finetune("unsloth/llama-3.2-1B", "my_data.jsonl", epochs=3)

# Chat with it
response = naply.chat(model, "Hello!")

Advanced Control (LoRA & QLoRA)

model = naply.load_for_finetune("small") # or "path/to/model"
naply.add_lora(model, rank=8, alpha=16)
naply.train_lora(model, "data.csv", epochs=5)
naply.merge_and_save(model, "final_model")

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🌟 Why NAPLY?

Feature	NAPLY	PyTorch	TensorFlow	Transformers
From Scratch	✅	❌	❌	❌
CPU Optimized	✅	⚠️	⚠️	⚠️
Lightweight	✅ (2MB)	❌ (500MB+)	❌ (1GB+)	❌ (200MB+)
No Dependencies	✅	❌	❌	❌
10 Training Methods	✅	❌	❌	❌
Micro-Specialists	✅ (v4.0)	❌	❌	❌
ChatGPT-Level	✅	⚠️	⚠️	⚠️
Natural English	✅	⚠️	⚠️	⚠️

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🎯 Key Features

🚀 Most Powerful Framework (v4.0 Octane)

• 6-Specialist Cluster: Syntax, Lexicon, Logic, Thought, Code, and Tech micro-specialists.
• 10 Advanced Training Methods: CRC, DCL, ILC, MCU, P3, PPL, PTL, RDL, S3L, SGL.
• Extreme Intelligence: 32,000 vocab (BPE) for deep semantic understanding.
• Parallel Training (PTL): Multi-threaded CPU execution for 2x faster learning.

⚡ Lightweight & Fast

• 2MB package size (vs 500MB+ for PyTorch)
• CPU-optimized - Runs on any laptop
• No GPU required - Pure NumPy implementation
• Fast inference - Optimized tokenization

🎨 Production-Ready

• PyTorch-like API - Familiar interface
• Automatic differentiation - Full autograd engine
• Checkpointing - Save/resume training
• CLI tools - naply train, naply chat
• Standalone - Works after training without datasets

🔧 Developer-Friendly

• No limits - Build models of any size (10M to 1B+ params)
• Any dataset - Supports .txt, .json, .jsonl, .csv, folders
• Easy to use - 3 lines of code to get started
• Well documented - Comprehensive guides and examples

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📦 Installation

pip install naply

That's it! Now use it super simply:

import naply.easy as ai

# Build AI in 1 line!
model = ai.train("my_data/", epochs=5)

# Chat in 1 line!
response = ai.chat("Hello!")

Install from Source (Development)

git clone https://github.com/naply-ai/naply.git
cd naply
pip install -e .

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🎯 Super Simple API

The Easiest Way (Recommended)

import naply.easy as ai

# Train (1 line!)
model = ai.train("my_data/", epochs=10)

# Chat (1 line!)
response = ai.chat("Hello!")

# Build (1 line!)
model = ai.build("medium")

Advanced API (More Control)

import naply

# Create model
model = naply.Model("medium")

# Train
model.train("my_data/", epochs=10)

# Chat
model.chat("Hello!")

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🏗️ Model Architectures

Using Presets

import naply

# Quick presets - choose your size
model = naply.Model("tiny")    # ~1M params, fast testing
model = naply.Model("small")   # ~10M params
model = naply.Model("medium")  # ~50M params
model = naply.Model("large")   # ~100M params
model = naply.Model("xl")      # ~300M params
model = naply.Model("xxl")     # ~1B params

Custom Architecture (No Limits!)

import naply

# Build exactly what you want
model = naply.Model(
    layers=24,        # Number of transformer layers
    heads=16,         # Attention heads
    embedding=1024,   # Embedding dimension
    vocab_size=50000, # Vocabulary size
    context=4096      # Context length
)

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🎓 Training Methods

NAPLY includes 10 advanced training methods for optimal learning:

1. CRC - Consistency-Retention Compression
2. DCL - Domain-Constrained Learning
3. ILC - Incremental Learning Consolidation
4. MCU - Memory Consolidation Unit
5. P3 - Parallel Pipelined Processing
6. PPL - Progressive Prompt Learning
7. PTL - Parallel Training and Learning (Multi-threaded CPU)
8. RDL - Recursive Data Learning
9. S3L - Structured Selective Stabilized Learning
10. SGL - Sparse Gradient Learning

import naply

model = naply.Model("medium")

# Use advanced training method
trainer = naply.PTLTrainer(model, num_workers=4)  # Parallel training
trainer.train(data_loader, epochs=10)

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💬 ChatGPT-Level Chat Interface

import naply

# Load trained model
model = naply.Model.load("my_model/")

# Chat with natural English
response = model.chat(
    "Hello! How are you?",
    max_tokens=200,
    temperature=0.8,
    top_k=50,
    top_p=0.95
)

print(response)
# Output: "Hello! I'm doing well, thank you for asking. How can I help you today?"

Interactive Chat

# Start interactive chat session
model.chat_interactive()

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🛠️ Advanced Features

Automatic Differentiation

import naply
from naply import Tensor

x = Tensor([1.0, 2.0, 3.0], requires_grad=True)
y = x * 2
z = y.sum()

z.backward()
print(x.grad)  # [2.0, 2.0, 2.0]

PyTorch-Style API

import naply.nn as nn

class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(768, 768)
        self.norm = nn.LayerNorm(768)
    
    def forward(self, x):
        return self.norm(self.linear(x))

Optimizers & Schedulers

from naply.optim import AdamW, OneCycleScheduler

optimizer = AdamW(model.parameters(), lr=1e-4)
scheduler = OneCycleScheduler(optimizer, max_lr=1e-3, total_steps=1000)

Checkpointing

from naply.utils import Checkpoint

ckpt = Checkpoint("checkpoints/")
ckpt.save(model, optimizer, epoch=5, loss=0.5)

# Resume training
metadata = ckpt.load_latest(model, optimizer)

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📚 Examples

Train Your First AI

import naply

# Create model
model = naply.Model("small")

# Train on your data
model.train("my_data/", epochs=10, batch_size=32)

# Save model
model.save("my_ai_model/")

# Chat
print(model.chat("Hello!"))

Power AI Training (ChatGPT-Level)

python train_power_ai.py --data "final datasets/" --foundation "foundation_corpus/" --epochs 15

Chat with Power AI

python chat_power_ai.py --model "final datasets/power_ai_model"

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🎯 Use Cases

• Build custom AI models from scratch
• Train on your own data (any format)
• Create ChatGPT-like assistants
• Educational purposes - Learn how AI works
• Research - Experiment with new architectures
• Production - Deploy lightweight AI models

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📖 Documentation

• Quick Start Guide
• Training Methods
• API Reference
• Examples

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🤝 Contributing

Contributions are welcome! Please see our Contributing Guide.

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📄 License

MIT License - see LICENSE file for details.

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🙏 Acknowledgments

Built with ❤️ for the AI community. Inspired by PyTorch, Transformers, and the open-source spirit.

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⭐ Star Us!

If you find NAPLY useful, please star us on GitHub!

# Install and try it now!
pip install naply

Build the future of AI. One line at a time. 🚀