OPC-R1-8B

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "INSAIT-Institute/OPC-R1-8B"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input

problem = "Compute the number of real solutions of the equation $x^2 + 2x + 2 = 0$."
solution = "The equation can be factored in as $x^2 + 2x + 2 = (x+1)^2 + 1$. Because $(x+1)^2 \\geq 0$, then $x^2 + 2x + 2 \\geq 1 > 0$. Therefore, there are \\boxed{0} real solutions."

prompt = "<substitute the evaluation prompt template from the paper>"
messages = [
    {"role": "user", "content": prompt.format(problem=problem, solution=solution)}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")

print("content:", content)