qwen3-50m-fp32

3
600M
license:apache-2.0
by
Mostafa8Mehrabi
Other
OTHER
0.6B params
New
3 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
2GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM

Code Examples

Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

# Standard HuggingFace usage - no special flags needed
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-50m")
model = AutoModelForCausalLM.from_pretrained("Mostafa8Mehrabi/qwen3-50m")

inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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