NxMobileLM-1.5B-SFT

Name: NxMobileLM-1.5B-SFT
Rating: 8.5 (10 reviews)
Author: NTQAI

1.5B

4 languages

license:mit

NTQAI

Language Model

OTHER

1.5B params

New

2 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

4GB+ RAM

Mobile

Laptop

Server

Quick Summary

This model is licensed under MIT and supports the English language.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2GB+ RAM

Code Examples

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Usepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "NTQAI/NxMobileLM-1.5B-SFT"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
inputs = tokenizer("What is the capital of Vietnam?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

Citationtext

@misc{NxMobileLM-1.5B-SFT,
  title={NxMobileLM-1.5B-SFT},
  author={NTQAI},
  year={2025},
  url={https://huggingface.co/NTQAI/NxMobileLM-1.5B-SFT},
}

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