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README.md

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+---
+language: en
+license: mit
+tags:
+- pytorch
+- text-generation
+- gemma3
+- tinystories
+---
+
+# Gemma3-270M Pre-trained on TinyStories
+
+This is a Gemma3-270M model pre-trained on the TinyStories dataset for 150k iterations.
+
+## Model Details
+
+- **Architecture**: Gemma3-270M
+- **Training Data**: TinyStories dataset from HuggingFace
+- **Training Iterations**: 150,000
+- **Parameters**: ~270M unique parameters
+- **Tokenizer**: GPT-2 tokenizer (tiktoken)
+- **Training Loss**: Available in training history
+
+## Quick Start
+
+### Download the Model
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download model weights
+model_path = hf_hub_download(
+    repo_id="vuminhtue/gemma3_270m_150k_tinystories",
+    filename="Gemma3_270m_150k_model_params.pt"
+)
+
+# Download config
+config_path = hf_hub_download(
+    repo_id="vuminhtue/gemma3_270m_150k_tinystories",
+    filename="config.json"
+)
+```
+
+### Load and Use
+
+```python
+import torch
+import tiktoken
+from Gemma3_model import Gemma3Model  # You need this file from the original code
+
+# Set up configuration
+GEMMA3_CONFIG = {
+    "vocab_size": 256000,
+    "context_length": 8192,
+    "emb_dim": 2048,
+    "n_heads": 8,
+    "n_layers": 18,
+    "hidden_dim": 16384,
+    "head_dim": 256,
+    "dtype": torch.bfloat16,
+}
+
+# Load model
+model = Gemma3Model(GEMMA3_CONFIG)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.load_state_dict(torch.load(model_path, map_location=device))
+model = model.to(device)
+model.eval()
+
+# Generate text
+tokenizer = tiktoken.get_encoding("gpt2")
+# Your generation code here...
+```
+
+## Training Details
+
+- **Optimizer**: AdamW with weight decay (0.1)
+- **Learning Rate**: 1e-4 with warmup and cosine decay
+- **Batch Size**: 32 with gradient accumulation (32 steps)
+- **Context Length**: 128 tokens
+- **Mixed Precision**: bfloat16 training
+
+## Model Architecture
+
+- Multi-Head Attention
+- RoPE (Rotary Position Embeddings)
+- RMSNorm for normalization
+- SiLU activation function
+- 18 transformer layers
+
+## Performance
+
+The model was trained on TinyStories, a dataset of simple stories for children. It can generate coherent short stories in a similar style.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{gemma3-tinystories-2025,
+  author = {Your Name},
+  title = {Gemma3-270M Pre-trained on TinyStories},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/vuminhtue/gemma3_270m_150k_tinystories}},
+}
+```
+
+## License
+
+MIT License
+
+## Contact
+
+For questions or issues, please open an issue on the HuggingFace model page.