Metadata-Version: 2.4
Name: omgformers
Version: 2.0.6.dev0
Summary: Parallel Diffusion Language Model — 66 features
Home-page: https://github.com/fastloraoffical/OMGformers
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Keywords: deep-learning,transformers,diffusion,language-model,nlp,pytorch,lora,moe,attention
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# OMGFormers

**Parallel Diffusion Language Model — 66 features**

[![PyPI version](https://badge.fury.io/py/omgformers.svg)](https://pypi.org/project/omgformers/)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
[![Python](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/)

> GitHub: https://github.com/fastloraoffical/OMGformers

---

## Installation

```bash
pip install omgformers
```

---

## What is OMGFormers?

OMGFormers is a research-grade PyTorch library for building **Parallel Diffusion Language Models**. It provides a modular, composable set of building blocks covering:

- **Attention mechanisms** — GQA, MLA, Sliding Window, Linear, Block-Sparse, Flash Attention 2, RoPE variants (YaRN, NTK, LongRoPE), ALiBi, T5 relative bias
- **Feed-forward layers** — SwiGLU, GeGLU, ReGLU, Standard FFN
- **Mixture of Experts** — Dense MoE, Soft MoE, load-balancing loss
- **Diffusion** — Mask scheduler, Parallel decoder for masked diffusion LM training
- **LoRA / DoRA** — Parameter-efficient fine-tuning adapters with merge/save/load
- **Training utilities** — EMA, Lion optimizer, warm-up cosine schedules, FSDP, gradient checkpointing, checkpoint manager
- **Tokenizer** — HuggingFace-compatible tokenizer with char-level fallback, special token management, encode/decode batch, mask_tokens for diffusion
- **Advanced** — KV cache, multi-token prediction, model merging (SLERP, DARE, TIES), reward model, PPO, int8/int4 quantization, GGUF export, RAG context injection, dynamic batching, chunked long-doc attention

---

## Quick Start

```python
import torch
from omgformers import OMGConfig, OMGModel, create_base_model, OMGTokenizer

# Build a small model
cfg = OMGConfig(
    vocab_size=32000,
    hidden_size=512,
    num_layers=6,
    num_heads=8,
)
model = OMGModel(cfg)

# Or use the fast initializer
model, cfg = create_base_model(hidden_size=512, num_layers=6)

# Tokenizer
tok = OMGTokenizer.from_pretrained("gpt2")  # or char-level fallback
ids = tok.encode("Hello, world!")
print(tok.decode(ids))
```

### Fine-tuning

```python
from omgformers import FineTuneConfig, FineTuner

ft_cfg = FineTuneConfig(method="lora", lora_rank=16, steps=1000)
tuner  = FineTuner(model, tokenizer=tok, config=ft_cfg)
tuner.train(train_dataloader)
```

### LoRA

```python
from omgformers import add_lora, merge_lora, save_lora, LoRAConfig

lora_cfg = LoRAConfig(rank=16, alpha=32, target_modules=["q_proj", "v_proj"])
model    = add_lora(model, lora_cfg)
# ... train ...
model    = merge_lora(model)
save_lora(model, "my_lora_weights/")
```

### Mixture of Experts

```python
from omgformers import MoEConfig, OMGConfig

cfg = OMGConfig(
    hidden_size=1024,
    num_layers=12,
    moe=MoEConfig(num_experts=8, top_k=2, aux_loss_coeff=0.01),
)
```

---

## What's New in v2.0.6-preview

| Feature | Description |
|---------|-------------|
| #53 | Fast base model initialization (`create_base_model`) |
| #54 | Fine-tuning engine (`FineTuner`) |
| #55 | Resume from checkpoint (`Trainer.resume_from_checkpoint`) |
| #56 | Checkpoint manager (`CheckpointManager`) |
| #57 | Flash Attention 2 real implementation (`flash_attention_forward`) |
| #58 | MoE → OMGConfig full integration (`MoEConfig`) |
| #61 | OMGTokenizer (HF + char-level fallback) |
| #62 | Special token management |
| #63 | Tokenizer save/load |
| #64 | Tokenizer `from_pretrained` |
| #65 | `encode_batch` / `decode_batch` |
| #66 | `mask_tokens` for diffusion training |

Bug fixes: #T5, #T6, #T7, #Mo1–Mo4, #A1–A5, #M1–M3, #C1–C3

---

## Requirements

- Python ≥ 3.9
- PyTorch ≥ 2.0
- Optional: `transformers`, `safetensors`, `flash-attn`, `bitsandbytes`

---

## License

Apache License 2.0 — see [LICENSE](LICENSE).
