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README.md

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+# LLaDA-346M: Large Language Diffusion with Masking
+
+## Model Description
+
+This is a **346 Million parameter** Large Language Diffusion Model trained with masked diffusion processes. This model demonstrates that diffusion-based approaches can be viable alternatives to autoregressive language models.
+
+### Key Features
+- **Architecture**: Masked Diffusion Model (MDM) with Transformer encoder
+- **Parameters**: 346M
+- **Sequence Length**: 512 tokens
+- **Vocab Size**: 50,257 (GPT-2)
+- **Training Data**: 50,000 WikiText-2 samples
+
+## Model Architecture
+
+```
+Token Embeddings (50257 × 1024)
+    ↓
+Position Embeddings (512 × 1024)
+    ↓
+Time Embeddings (MLP)
+    ↓
+Transformer Encoder (12 layers, 16 heads)
+    ├─ Self-Attention
+    └─ Feed-Forward (4096 dim)
+    ↓
+Output Projection (1024 × 50257)
+```
+
+## Training Details
+
+- **Algorithm**: Masked Diffusion Model (MDM)
+- **Loss Function**: Cross-entropy on masked positions
+- **Optimizer**: AdamW (lr=3e-5, betas=(0.9, 0.95))
+- **Batch Size**: 16 (effective: 32 with grad accumulation)
+- **Gradient Checkpointing**: Enabled
+- **Mixed Precision**: AMP (FP32/FP16)
+- **Epochs**: 4
+- **Training Samples**: 50,000
+- **GPU**: NVIDIA V100 (22GB VRAM)
+- **Training Time**: ~20 hours
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Initial Loss | 5.96 |
+| Final Loss | 4.94 |
+| Loss Reduction | 17.1% |
+| Total Parameters | 346M |
+| Model Size (FP32) | 1.38 GB |
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Loading the Model
+
+```python
+import torch
+from transformers import AutoTokenizer
+from your_module import MaskedDiffusionModel
+
+# Load model
+model = MaskedDiffusionModel(
+    vocab_size=50257,
+    hidden_dim=1024,
+    num_layers=12,
+    num_heads=16,
+    ff_dim=4096,
+    dropout=0.1,
+    max_seq_length=512,
+    num_timesteps=100
+)
+
+# Load weights
+checkpoint = torch.load("pytorch_model.bin")
+model.load_state_dict(checkpoint)
+model.eval()
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+```
+
+### Text Generation
+
+```python
+from diffusion_sampler import DiffusionSampler
+
+sampler = DiffusionSampler(model, tokenizer, config, device)
+
+# Generate text
+text = sampler.generate(
+    prompt="The future of AI",
+    num_steps=40,
+    temperature=0.8,
+    top_p=0.9
+)
+print(text)
+```
+
+## Model Characteristics
+
+### Advantages
+✅ **Bidirectional Context**: Sees full context unlike autoregressive models  
+✅ **Parallel Generation**: Can predict multiple tokens simultaneously  
+✅ **Reversal Invariance**: Equal performance on forward and reverse tasks  
+✅ **Global Coherence**: Reduces error accumulation  
+
+### Limitations
+❌ Slower generation (iterative denoising process)  
+❌ Requires more compute for inference  
+❌ Not fine-tuned for specific tasks  
+
+## Training Process
+
+### Forward Process
+- Gradually mask tokens randomly
+- At timestep t ∈ [0,1], each token masked with probability t
+- Creates noisy version of input
+
+### Reverse Process
+- Iteratively predict and unmask tokens
+- Uses transformer to predict masked positions
+- Trained with cross-entropy loss on masked tokens only
+
+## Optimization Techniques
+
+- **Gradient Checkpointing**: Save memory during backprop
+- **Mixed Precision (AMP)**: Use FP16 where possible
+- **Gradient Accumulation**: Simulate larger batches
+- **Layer Norm First**: Improved training stability
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{nie2025llada,
+  title={Large Language Diffusion Models},
+  author={Nie, Shen and others},
+  journal={arXiv preprint arXiv:2502.09992},
+  year={2025}
+}
+```
+
+## License
+
+MIT License - Feel free to use for research and commercial purposes
+
+## Acknowledgments
+
+- Based on "Large Language Diffusion Models" (Nie et al., 2025)
+- Built with PyTorch and Transformers
+- Trained on WikiText-2 dataset
+- Inspired by diffusion models for vision (DiT, Genie)
+
+## Contact & Support
+
+For issues, questions, or suggestions, please open an issue on GitHub or contact the model author.
+
+---
+
+**Last Updated**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

config.json

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+{
+  "architectures": [
+    "MaskedDiffusionModel"
+  ],
+  "model_type": "llada",
+  "vocab_size": 50257,
+  "hidden_size": 1024,
+  "num_hidden_layers": 12,
+  "num_attention_heads": 16,
+  "intermediate_size": 4096,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "attention_probs_dropout_prob": 0.1,
+  "max_position_embeddings": 512,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-12,
+  "pad_token_id": 50256,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "num_timesteps": 100,
+  "masking_schedule": "uniform"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1719f3f94a3b14a5a4d7023efdb2bd10158d9acb25833c67d15a209dbd070aec
+size 1022902031

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}

training_info.json

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+{
+  "model_name": "LLaDA-346M",
+  "parameters": 255709265,
+  "training_samples": 23679,
+  "training_steps": 5916,
+  "final_loss": 1.40234375,
+  "initial_loss": 10.90625,
+  "training_time_hours": 591.6,
+  "config": {}
+}