qwen3-30m-tinystories-final

5
16.0B
license:apache-2.0
by
Mostafa8Mehrabi
Language Model
OTHER
16B params
New
5 downloads
Early-stage
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Mobile
Laptop
Server
36GB+ RAM
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Laptop
Server
Quick Summary

🚀 Qwen3-30M TinyStories Pretrained (FP16) - Notebook Version Pretrained Qwen3-30M model on TinyStories dataset using FP16 precision in notebook environment.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
15GB+ RAM

Code Examples

🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))
🚀 Usagepythontransformers
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))

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