Qwen3-4B-Thinking-2507-DES-Reasoning

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])

from transformers import pipeline
import torch

model_id = "sequelbox/Qwen3-4B-Thinking-2507-DES-Reasoning"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Compare two energy management strategies for a mobile CPU. The task is to process a batch of 10 million instructions. Strategy 1 (Race-to-Halt): Run the CPU at its maximum frequency (3 GHz, 1.2V, 5W power) to finish the task as quickly as possible, then enter a deep sleep state (0.05W). Strategy 2 (Pace): Run the CPU at a lower frequency (1.5 GHz, 0.95V, 1.5W power) that is just fast enough to meet a soft deadline of 100ms. The simulation must model the active time, sleep time, and state transition energy costs (100 microjoules to enter/exit sleep). The goal is to determine which strategy results in lower total energy consumption for the task."
#prompt = "A downtown bus terminal has 10 bays serving 20 different bus routes. The current system uses fixed bay assignments. This often leads to queues of buses waiting for their designated bay to become free, while other bays are empty. An alternative dynamic system is proposed where an arriving bus is allocated to the first available bay. Simulate both systems, considering bus arrival variability and passenger boarding times (which depend on the number of waiting passengers for that route). The objective is to compare the two systems based on bus turnaround time and passenger comfort."
#prompt = "An ERP system calculates an optimal production schedule daily based on an algorithm. However, an experienced human scheduler reviews and frequently modifies this schedule based on heuristics, such as prioritizing orders for historically difficult clients or building up just in case inventory ahead of a machine they don't trust. Model the production system under two scenarios: 1) The ERP schedule is followed perfectly. 2) The ERP schedule is subject to the scheduler's probabilistic overrides. Quantify the "cost of human intervention" by comparing the performance of the two systems on metrics like inventory cost, service level, and schedule stability."
#prompt = "A proposed system involves massive solar power stations in geosynchronous orbit that beam energy down to dedicated receiving stations (rectennas) on Earth via microwaves. Model the integration of a 2 GW baseload space-based solar power feed into a terrestrial grid. The simulation must account for the unique reliability characteristics: it is immune to weather and day/night cycles on Earth but is vulnerable to space debris impacts, solar flares, or beam misalignment, which could cause an instantaneous, total loss of power. The objective is to determine the required level of terrestrial spinning reserves to safely integrate this novel power source."

messages = [
    {"role": "user", "content": prompt},
]

outputs = pipe(
    messages,
    max_new_tokens=16000,
)
print(outputs[0]["generated_text"][-1])