metadata
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
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
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:
@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.