Atlas-Pro-1.5B-Preview-GGUF

6
30 languages
Q4
license:mit
by
Spestly
Language Model
OTHER
1.5B params
New
6 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
4GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM

Code Examples

**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)
**Usage**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Atlas Pro model
model_name = "Spestly/Atlas-R1-Pro-1.5B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate a response
prompt = "Write a Python function to calculate the Fibonacci sequence."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

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