🚀 Qwen3-30M TinyStories Pretrained (FP16) - Notebook Version
Pretrained Qwen3-30M model on TinyStories dataset using FP16 precision in notebook environment.
📊 Training Results
- Final Training Loss: 1.5244
- Final Validation Loss: 1.5601832866668701
- Training Samples: -1
- Epochs: 3
- Precision: FP16
- Dataset: TinyStories (child-friendly stories)
🚀 Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-30m-tinystories-final")
model = AutoModelForCausalLM.from_pretrained(
"Mostafa8Mehrabi/qwen3-30m-tinystories-final",
torch_dtype=torch.float16,
device_map="auto"
)
# Generate a story
prompt = "Once upon a time, there was a little girl named"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
📁 Checkpoints
Training checkpoints (also in FP16) are available at: Mostafa8Mehrabi/qwen3-30m-tinystories-checkpoints
📖 About TinyStories Dataset
The TinyStories dataset contains simple, child-friendly stories that are perfect for:
- Story generation
- Child-safe content creation
- Educational applications
- Creative writing assistance
🔧 Training Environment
This model was trained in a notebook environment with the following configuration:
- Batch Size: 128
- Learning Rate: 5e-05
- Max Length: 512
- Number of Processes: 8