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The world's first self-crystallizing neural training acceleration engine

Project description

NexusTrain ⚡

The World's First Self-Crystallizing Neural Training Engine

Yazar: Ömür Bera Işık | Lisans: MIT | Python 3.9+

pip install nexustrain
pip install nexustrain[full]   # + HuggingFace ekosistemi
pip install nexustrain[all]    # her şey

10 Özgün Teknoloji

Modül Ne yapar?
CrystalCore™ Sık çalışan operasyonları runtime'da otomatik kristalize eder (torch.compile)
MorphicMemory™ Markov zinciriyle allocation'ları tahmin eder, ölü tensor'ları yeniden doğurur
SpectraOptimizer™ Gradient geçmişine FFT uygular, rezonant frekansları söndürür
ResonanceScheduler™ Gradient spektrum entropisiyle eğitim fazını otomatik tespit eder
ChromaticPrecision™ Her katmana gradient varyansına göre ayrı dtype atar (FP32/BF16/FP16)
GradientHarmonics™ Haar wavelet ile gradient'i ayrıştırır, frekans alanında gürültü enjekte eder
NeuralProfiler™ Küçük LSTM ile OOM ve gradient patlamasını adım öncesi tahmin eder
CrystalPipeline™ VRAM'e göre gradient accumulation'ı dinamik ayarlar, async checkpoint
ZeroWaste™ Atık operasyonları ve sıfır gradient alan parametreleri tespit eder
UniversalAdapter™ HF/TRL/PEFT/DeepSpeed/Accelerate/vanilla PyTorch ile otomatik uyum

Hızlı Başlangıç

HuggingFace / TRL ile

from nexustrain import NexusTrain, NexusConfig
from trl import SFTTrainer, SFTConfig

# Model yükle
from transformers import AutoModelForCausalLM, AutoTokenizer
model     = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")

# NexusTrain konfigürasyonu
cfg = NexusConfig(power=1.0)
nt  = NexusTrain(model, tokenizer, cfg)

# Trainer oluştur ve bağla
trainer = SFTTrainer(model=model, tokenizer=tokenizer,
                     train_dataset=dataset, args=SFTConfig(...))
trainer = nt.patch_trainer(trainer)
nt.engage()

trainer.train()
nt.summary()

Tek satır sarmalayıcı

from nexustrain import nexus_wrap

nt, model = nexus_wrap(model, tokenizer, power=1.0)
trainer   = nt.patch_trainer(trainer)
nt.engage()

Sadece SpectraOptimizer

from nexustrain import SpectraOptimizer

optimizer = SpectraOptimizer(
    model.parameters(),
    lr               = 2e-4,
    spectral_damping = 0.1,
)

Sadece ResonanceScheduler

from nexustrain import SpectraOptimizer, ResonanceScheduler, NexusConfig

opt   = SpectraOptimizer(model.parameters(), lr=2e-4)
cfg   = NexusConfig(base_lr=2e-4, warmup_steps=100)
sched = ResonanceScheduler(opt, cfg)

# Her adımda
sched.step(loss=loss.item(), grad_norm=gnorm)

Konfigürasyon

cfg = NexusConfig(
    power               = 1.0,    # 0.0–1.0 global güç

    # Hız
    crystal_core        = True,
    morphic_memory      = True,
    crystal_pipeline    = True,
    zero_waste          = True,

    # Zeka
    spectra_optimizer   = True,
    resonance_scheduler = True,
    chromatic_precision = True,
    gradient_harmonics  = True,
    harmonic_noise      = 0.001,  # genelleme için
    harmonic_compress   = 0.0,    # dağıtık eğitim için

    # İzleme
    neural_profiler     = True,
    profiler_alert_ms   = 500.0,

    # LR
    base_lr             = 2e-4,
    min_lr              = 1e-6,
    warmup_steps        = 100,
    resonance_sensitivity = 0.5,

    # Genel
    device              = "auto",   # cuda/cpu otomatik
    dtype               = "auto",   # bf16/fp16/fp32 otomatik
    seed                = 42,
    output_dir          = "./nexus_output",
)

Ortam Kontrolü

nexustrain-check

veya Python'dan:

from nexustrain import nexus_check
nexus_check()

Kurulum Seçenekleri

pip install nexustrain              # Sadece PyTorch gerekli
pip install nexustrain[full]        # + transformers, peft, trl, bitsandbytes
pip install nexustrain[speed]       # + flash-attn, triton
pip install nexustrain[distributed] # + deepspeed, accelerate
pip install nexustrain[tuning]      # + optuna
pip install nexustrain[logging]     # + wandb, tensorboard
pip install nexustrain[all]         # her şey
pip install nexustrain[dev]         # geliştirici araçları

Test

pip install nexustrain[dev]
pytest tests/ -v

Lisans

MIT © 2025 Ömür Bera Işık — github.com/fastloraoffical

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