OMG-hybridOMGa ⚡🧠🔮♻️

Ultimate Hybrid LoRA Suite — LoRA, DoRA, QLoRA, LoRA+, rsLoRA ve OMGa'nın tam eğitim motoruyla birleşimi.

Kurulum

# Temel kurulum (sadece torch + transformers + accelerate)
pip install omg-hybridomga

# Tam kurulum (bitsandbytes, peft, trl, datasets dahil)
pip install "omg-hybridomga[full]"

# Her şey dahil
pip install "omg-hybridomga[all]"

Özellikler

LoRA Katmanları

Bellek & VRAM Yönetimi

• MemoryMonitor — daemon thread VRAM watchdog
• VRAMGuard — otomatik batch küçültme + akıllı kurtarma
• MorphicMemory™ — Markov tahmini + tensor reincarnation
• 2-bit NF2 quantization (bitsandbytes gerektirmez)

Hız & Derleme

• Accelerator — grad accum, AMP, clip, fused optimizer
• CrystalCore™ — runtime kernel kristalizasyonu
• TritonKernels — RMSNorm, SwiGLU Triton fallback
• torch.compile — fullgraph + cache desteği

Optimizer & Scheduler

• EMA / SWA — Exponential/Stochastic Weight Averaging
• SpectraOptimizer™ — frekans-domain adaptive optimizer
• ResonanceScheduler™ — gradient-spectrum self-tuning LR
• WarmupCosineScheduler / WarmupLinearScheduler

Gradient İyileştirme

• GradientHarmonics™ — wavelet gradient processing
• NeuralProfiler™ — LSTM-based OOM/explode prediction
• LossSpikeDetector — spike tespiti + LR müdahalesi

Hızlı Başlangıç

Sadece LoRA Katmanı

from omg_hybridomga import HybridConfig, apply_hybrid_lora

cfg = HybridConfig(method="omga", rank=16, alpha=32)
model = apply_hybrid_lora(model, cfg)

Tam Eğitim Motoru

from omg_hybridomga import HybridOMGa

engine = HybridOMGa(
    "meta-llama/Llama-3.2-3B",
    rank=16,
    method="omga",
    ema=True,
    swa=True,
    crystal_core=True,
    priority_prop=True,
)

model, tokenizer = engine.load()
trainer = engine.get_trainer(dataset)
engine.train(trainer)

LoRA Kaydet / Yükle

from omg_hybridomga import save_hybrid_lora, load_hybrid_lora

save_hybrid_lora(model, "./my_lora_weights")
model = load_hybrid_lora(model, "./my_lora_weights")

Ortam Kontrolü

from omg_hybridomga import check_environment
check_environment()

Opsiyonel Bağımlılıklar

Gereksinimler

• Python ≥ 3.9
• PyTorch ≥ 2.1.0
• transformers ≥ 4.40.0
• accelerate ≥ 0.27.0

Lisans

Apache 2.0 — Ayrıntılar için LICENSE dosyasına bakın.