zlynx 0.1.7

Zlynx is a lightweight, highly-customizable deep learning library built on top of JAX and Flax NNX

Zlynx

A lightweight, highly-customizable deep learning library built on JAX and Flax NNX. Designed for researchers and developers who want fine-grained control over model architectures, training loops, and distributed setups without the bloat of massive frameworks.

Install

uv pip install zlynx

Define & Load Models

from zlynx import Z

class MyModel(Z): ...

# Load from HuggingFace
model = MyModel.load_hf("username/my-model", format="safetensors")

# Load from Kaggle
model = MyModel.load_kaggle("username/my-model", sharding="fsdp")

# Load from local checkpoint
model = MyModel.load("./checkpoint", key=jax.random.key(0))

Built-in Llama

from zlynx.models.llama import LlamaConfig, LlamaLanguageModel

config = LlamaConfig(vocab_size=32000, hidden_size=512, num_hidden_layers=2)
model = LlamaLanguageModel(config)

# Generate
output_ids = model.generate(input_ids, key=jax.random.key(0), max_new_tokens=128)

Train

from zlynx.trainer import Trainer, TrainerConfig

trainer = Trainer(
    model=model,
    loss_fn=loss_fn,
    train_dataset=dataset,
    config=TrainerConfig(
        per_device_batch_size=32,
        learning_rate=5e-5,
        num_epochs=3,
        sharding="auto",
    ),
)
trainer.train()

PEFT (LoRA, DoRA, VeRA, LoHa, LoKr, AdaLoRA)

from zlynx.modules.peft import apply_peft

model = apply_peft(model, method="lora", r=16, alpha=32, target_modules=["q_proj", "v_proj"])

Save & Push

model.save("./my-model", format="safetensors")
model.push_hf("username/my-model")
model.push_kaggle("username/my-model")

Features

• Checkpointing — Orbax + SafeTensors, HuggingFace Hub & Kaggle integration
• Training — gradient accumulation, LR scheduling, multi-backend logging (W&B, TensorBoard)
• Sharding — auto, DDP, FSDP with one config change
• PEFT — 6 adapter methods via apply_peft()
• GaLore — gradient low-rank projection for memory-efficient full fine-tuning
• Data — Grain-based pipeline accepting lists, HF datasets, dicts, and iterables
• Modules — Attention (GQA/MQA), MLP (SwiGLU), RMSNorm, RoPE, KV Cache, DiT blocks

Documentation

• Getting Started
• MNIST Tutorial
• API Reference — Trainer
• API Reference — Modules
• API Reference — Models