---
language: en
license: mit
tags:
- pytorch
- text-generation
- qwen3
- tinystories
---

# Qwen3-0.6B Pre-trained on TinyStories

This is a Qwen3-0.6B model pre-trained on the TinyStories dataset for 200k iterations.

## Model Details

- **Architecture**: Qwen3-0.6B
- **Training Data**: TinyStories dataset from HuggingFace
- **Training Iterations**: 200,000
- **Parameters**: ~596M 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/qwen3-200k-tinystories",
    filename="Qwen3_200k_model_params.pt"
)

# Download config
config_path = hf_hub_download(
    repo_id="vuminhtue/qwen3-200k-tinystories",
    filename="config.json"
)
```

### Load and Use

```python
import torch
import tiktoken
from Qwen3_model import Qwen3Model  # You need this file from the original code

# Set up configuration
QWEN3_CONFIG = {
    "vocab_size": 151936,
    "context_length": 40960,
    "emb_dim": 1024,
    "n_heads": 16,
    "n_layers": 28,
    "hidden_dim": 3072,
    "head_dim": 128,
    "qk_norm": True,
    "n_kv_groups": 8,
    "rope_base": 1000000.0,
    "dtype": torch.bfloat16,
}

# Load model
model = Qwen3Model(QWEN3_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

- Grouped Query Attention (GQA) with 8 KV groups
- RoPE (Rotary Position Embeddings)
- RMSNorm for normalization
- SiLU activation function
- 28 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{qwen3-tinystories-2025,
  author = {Tue Vu},
  title = {Qwen3-0.6B Pre-trained on TinyStories},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/vuminhtue/qwen3-200k-tinystories}},
}
```

## License

MIT License

## Contact

For questions or issues, please open an issue on the HuggingFace model page.