$ llm_benchmark run
-------Linux----------
{'id': '0', 'name': 'NVIDIA GeForce RTX 3060', 'driver': '580.119.02', 'gpu_memory_total': '12288.0 MB', 'gpu_memory_free': '711.0 MB', 'gpu_memory_used': '11197.0 MB', 'gpu_load': '0.0%', 'gpu_temperature': '40.0°C'}
Only one GPU card
Total memory size : 125.63 GB
cpu_info: unknown
gpu_info: NVIDIA GeForce RTX 3060
os_version: Pop!_OS 24.04 LTS
ollama_version: 0.14.3
----------
LLM models file path：/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/llm_benchmark/data/benchmark_models_32gb_ram.yml
Checking and pulling the following LLM models
phi4:14b
deepseek-r1:14b
gpt-oss:20b
----------
model_name = phi4:14b
prompt = Write a step-by-step guide on how to bake a chocolate cake from scratch.
prompt = Develop a python function that solves the following problem, sudoku game
prompt = Create a dialogue between two characters that discusses economic crisis
prompt = In a forest, there are brave lions living there. Please continue the story.
prompt = I'd like to book a flight for 4 to Seattle in U.S.
--------------------
----------------------------------------
model_name = deepseek-r1:14b
prompt = Summarize the key differences between classical and operant conditioning in psychology.
prompt = Translate the following English paragraph into Chinese and elaborate more -> Artificial intelligence is transforming various industries by enhancing efficiency and enabling new capabilities.
╭───────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────────────────────────────────────────────────────────╮
│ /home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/llm_benchmark/main.py:73 in run │
│ │
│ 70 │ │ bench_results_info.update(result2) │
│ 71 │ │ result3 = run_benchmark.run_benchmark(models_file_path,benchmark_file_path, 'vis │
│ 72 │ │ bench_results_info.update(result3) │
│ ❱ 73 │ │ result4 = run_benchmark.run_benchmark(models_file_path,benchmark_file_path, 'ins │
│ 74 │ │ bench_results_info.update(result4) │
│ 75 │ else: │
│ 76 │ │ bench_results_info.update({"llama2:7b":7.65}) │
│ │
│ ╭──────────────────────────────────────────────────────────────────────────────────────── locals ────────────────────────────────────────────────────────────────────────────────────────╮ │
│ │ bench_results_info = {} │ │
│ │ benchmark_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+32 │ │
│ │ custombenchmark = None │ │
│ │ ft_mem_size = 125.63 │ │
│ │ is_simulation = False │ │
│ │ models_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+47 │ │
│ │ ollama_version = '0.14.3' │ │
│ │ ollamabin = 'ollama' │ │
│ │ result1 = {} │ │
│ │ result2 = {} │ │
│ │ result3 = {} │ │
│ │ sendinfo = True │ │
│ │ sys_info = {'system': 'Linux', 'memory': 125.63174057006836, 'cpu': 'unknown', 'gpu': 'NVIDIA GeForce RTX 3060', 'os_version': 'Pop!_OS 24.04 LTS', 'system_name': 'Linux'} │ │
│ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
│ │
│ /home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/llm_benchmark/run_benchmark.py:98 in run_benchmark │
│ │
│ 95 │ │ │ │ │ │ else: │
│ 96 │ │ │ │ │ │ │ for one_prompt in one_model_type['prompts']: │
│ 97 │ │ │ │ │ │ │ │ print(f"prompt = {one_prompt['prompt']}") │
│ ❱ 98 │ │ │ │ │ │ │ │ result = subprocess.run([ollamabin, 'run', model_name, o │
│ 99 │ │ │ │ │ │ │ │ std_err = result.stderr │
│ 100 │ │ │ │ │ │ │ │ #print(result.stderr) │
│ 101 │ │ │ │ │ │ │ │ file1.write(std_err) │
│ │
│ ╭───────────────────────────────────────────────────────────────────────────────────────────────────────── locals ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │
│ │ allowed_models = {'phi4:14b', 'deepseek-r1:14b', 'gpt-oss:20b'} │ │
│ │ ans = {} │ │
│ │ benchmark_dict = { │ │
│ │ │ 'version': 2.0, │ │
│ │ │ 'modeltypes': [ │ │
│ │ │ │ {'type': 'chat', 'prompts': [{'prompt': 'What are your thoughts on the latest scientific discovery regarding black holes?', 'keywords': 'science, recent'}]}, │ │
│ │ │ │ {'type': 'completion', 'prompts': [{'prompt': 'In a world where time travel is possible, a scientist invents a device that allo'+100, 'keywords': None}]}, │ │
│ │ │ │ { │ │
│ │ │ │ │ 'type': 'instruct', │ │
│ │ │ │ │ 'models': [{'model': 'mistral:7b'}, {'model': 'llama3:8b'}, {'model': 'llama3.1:8b'}, {'model': 'phi4:14b'}, {'model': 'qwen:1.8b'}, {'model': 'qwen2:7b'}, {'model': 'phi:2.7b'}], │ │
│ │ │ │ │ 'prompts': [ │ │
│ │ │ │ │ │ {'prompt': 'Write a step-by-step guide on how to bake a chocolate cake from scratch.', 'keywords': 'cooking, recipe'}, │ │
│ │ │ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game', 'keywords': 'python, sudoku'}, │ │
│ │ │ │ │ │ {'prompt': 'Create a dialogue between two characters that discusses economic crisis', 'keywords': 'dialogue'}, │ │
│ │ │ │ │ │ {'prompt': 'In a forest, there are brave lions living there. Please continue the story.', 'keywords': 'sentence completition'}, │ │
│ │ │ │ │ │ {'prompt': "I'd like to book a flight for 4 to Seattle in U.S.", 'keywords': 'flight booking'} │ │
│ │ │ │ │ ] │ │
│ │ │ │ }, │ │
│ │ │ │ { │ │
│ │ │ │ │ 'type': 'question-answer', │ │
│ │ │ │ │ 'reference_url': 'https://www.turing.com/interview-questions/artificial-intelligence', │ │
│ │ │ │ │ 'models': [{'model': 'gemma:2b'}, {'model': 'gemma:7b'}, {'model': 'gemma2:9b'}], │ │
│ │ │ │ │ 'prompts': [ │ │
│ │ │ │ │ │ {'prompt': 'Explain Artificial Intelligence and give its applications.', 'keywords': None}, │ │
│ │ │ │ │ │ {'prompt': 'How are machine learning and AI related?', 'keywords': None}, │ │
│ │ │ │ │ │ {'prompt': 'What is Deep Learning based on?', 'keywords': None}, │ │
│ │ │ │ │ │ {'prompt': 'What is the full form of LSTM?', 'keywords': None}, │ │
│ │ │ │ │ │ {'prompt': 'What are different components of GAN?', 'keywords': None} │ │
│ │ │ │ │ ] │ │
│ │ │ │ }, │ │
│ │ │ │ { │ │
│ │ │ │ │ 'type': 'vision-image', │ │
│ │ │ │ │ 'reference_url': 'https://chuangtc.com/Research/llm-vlm.php', │ │
│ │ │ │ │ 'models': [{'model': 'llava:7b'}, {'model': 'llava:13b'}], │ │
│ │ │ │ │ 'prompts': [{'prompt': 'Describe the image,', 'keywords': 'sample1.jpg,sample2.jpg,sample3.jpg,sample4.jpg,sample5.jpg'}] │ │
│ │ │ │ }, │ │
│ │ │ │ { │ │
│ │ │ │ │ 'type': 'instruction-question-answer-code-generation', │ │
│ │ │ │ │ 'models': [{'model': 'deepseek-r1:1.5b'}, {'model': 'deepseek-r1:8b'}, {'model': 'deepseek-r1:14b'}, {'model': 'gpt-oss:20b'}], │ │
│ │ │ │ │ 'prompts': [ │ │
│ │ │ │ │ │ {'prompt': 'Summarize the key differences between classical and operant conditioning in psyc'+7, 'keywords': 'Instruction-Following'}, │ │
│ │ │ │ │ │ {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'}, │ │
│ │ │ │ │ │ {'prompt': 'What are the main causes of the American Civil War?', 'keywords': 'Question-Answering'}, │ │
│ │ │ │ │ │ {'prompt': 'How does photosynthesis contribute to the carbon cycle?', 'keywords': 'Question-Answering'}, │ │
│ │ │ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game.', 'keywords': 'Code Generation'} │ │
│ │ │ │ │ ] │ │
│ │ │ │ }, │ │
│ │ │ │ { │ │
│ │ │ │ │ 'type': 'custom-model', │ │
│ │ │ │ │ 'models': None, │ │
│ │ │ │ │ 'prompts': [ │ │
│ │ │ │ │ │ {'prompt': 'Summarize the key differences between classical and operant conditioning in psyc'+7, 'keywords': 'Instruction-Following'}, │ │
│ │ │ │ │ │ {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'}, │ │
│ │ │ │ │ │ {'prompt': 'What are the main causes of the American Civil War?', 'keywords': 'Question-Answering'}, │ │
│ │ │ │ │ │ {'prompt': 'How does photosynthesis contribute to the carbon cycle?', 'keywords': 'Question-Answering'}, │ │
│ │ │ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game.', 'keywords': 'Code Generation'} │ │
│ │ │ │ │ ] │ │
│ │ │ │ } │ │
│ │ │ ] │ │
│ │ } │ │
│ │ benchmark_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+32 │ │
│ │ file1 = <_io.TextIOWrapper name='log_2026-01-22-152412.log' mode='w' encoding='utf-8'> │ │
│ │ line = '' │ │
│ │ loc_dt = datetime.datetime(2026, 1, 22, 15, 24, 12, 940758) │ │
│ │ model_name = 'deepseek-r1:14b' │ │
│ │ model_type = 'instruction-question-answer-code-generation' │ │
│ │ models_dict = {'version': 2.0, 'models': [{'model': 'phi4:14b'}, {'model': 'deepseek-r1:14b'}, {'model': 'gpt-oss:20b'}]} │ │
│ │ models_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+47 │ │
│ │ ollamabin = 'ollama' │ │
│ │ one_model_type = { │ │
│ │ │ 'type': 'instruction-question-answer-code-generation', │ │
│ │ │ 'models': [{'model': 'deepseek-r1:1.5b'}, {'model': 'deepseek-r1:8b'}, {'model': 'deepseek-r1:14b'}, {'model': 'gpt-oss:20b'}], │ │
│ │ │ 'prompts': [ │ │
│ │ │ │ {'prompt': 'Summarize the key differences between classical and operant conditioning in psyc'+7, 'keywords': 'Instruction-Following'}, │ │
│ │ │ │ {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'}, │ │
│ │ │ │ {'prompt': 'What are the main causes of the American Civil War?', 'keywords': 'Question-Answering'}, │ │
│ │ │ │ {'prompt': 'How does photosynthesis contribute to the carbon cycle?', 'keywords': 'Question-Answering'}, │ │
│ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game.', 'keywords': 'Code Generation'} │ │
│ │ │ ] │ │
│ │ } │ │
│ │ one_prompt = {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'} │ │
│ │ onemodel = {'model': 'deepseek-r1:14b'} │ │
│ │ result = CompletedProcess(args=['ollama', 'run', 'deepseek-r1:14b', 'Summarize the key differences between classical and operant conditioning in psychology.', '--verbose'], returncode=0, │ │
│ │ stdout="\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?2026l\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?2026l\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?2026l\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?20… │ │
│ │ I\x1b[?25l\x1b[?25h need\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h summarize\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h differences\x1b[?25l\x1b[?25h between\x1b[?25l\x1b[?25h │ │
│ │ classical\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h conditioning\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h psychology\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ Hmm\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h let\x1b[?25l\x1b[?25h me\x1b[?25l\x1b[?25h think\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h what\x1b[?25l\x1b[?25h each\x1b[?25l\x1b[?25h │ │
│ │ of\x1b[?25l\x1b[?25h these\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h first\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h │ │
│ │ Conditioning\x1b[?25l\x1b[?25h...\x1b[?25l\x1b[?25h I\x1b[?25l\x1b[?25h remember\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h │ │
│ │ associations\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Like\x1b[?25l\x1b[?25h Ivan\x1b[?25l\x1b[?25h Pav\x1b[?25l\x1b[?25hlov\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h experiment\x1b[?25l\x1b[?25h │ │
│ │ with\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h dogs\x1b[?25l\x1b[?25h where\x1b[?25l\x1b[?25h they\x1b[?25l\x1b[?25h learned\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h │ │
│ │ sal\x1b[?25l\x1b[?25hivate\x1b[?25l\x1b[?25h at\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h bell\x1b[?25l\x1b[?25h because\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h was\x1b[?25l\x1b[?25h │ │
│ │ associated\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h food\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h So\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h │ │
│ │ all\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h pairing\x1b[?25l\x1b[?25h two\x1b[?25l\x1b[?25h stimuli\x1b[?25l\x1b[?25h together\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h The\x1b[?25l\x1b[?25h │ │
│ │ un\x1b[?25l\x1b[?25hcondition\x1b[?25l\x1b[?25hed\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hUC\x1b[?25l\x1b[?25hS\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h │ │
│ │ like\x1b[?25l\x1b[?25h food\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h │ │
│ │ (\x1b[?25l\x1b[?25hCS\x1b[?25l\x1b[?25h),\x1b[?25l\x1b[?25h which\x1b[?25l\x1b[?25h becomes\x1b[?25l\x1b[?25h something\x1b[?25l\x1b[?25h else\x1b[?25l\x1b[?25h that\x1b[?25l\x1b[?25h │ │
│ │ triggers\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h same\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │
│ │ Conditioning\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h different\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h That\x1b[?25l\x1b[?25h one\x1b[?25l\x1b[?25h deals\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h │ │
│ │ voluntary\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h B\x1b[?25l\x1b[?25h.F\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ Skinner\x1b[?25l\x1b[?25h did\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h lot\x1b[?25l\x1b[?25h of\x1b[?25l\x1b[?25h work\x1b[?25l\x1b[?25h here\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ It\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h how\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h strengthened\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │
│ │ weakened\x1b[?25l\x1b[?25h based\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h rewards\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h punishments\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ So\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h if\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h leads\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h │ │
│ │ positive\x1b[?25l\x1b[?25h outcome\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h more\x1b[?25l\x1b[?25h likely\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h │ │
│ │ happen\x1b[?25l\x1b[?25h again\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hre\x1b[?25l\x1b[?25hin\x1b[?25l\x1b[?25hforcement\x1b[?25l\x1b[?25h),\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │
│ │ if\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h leads\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h negative\x1b[?25l\x1b[?25h outcome\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │
│ │ it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h less\x1b[?25l\x1b[?25h likely\x1b[?25l\x1b[?25h │ │
│ │ (\x1b[?25l\x1b[?25hpun\x1b[?25l\x1b[?25hishment\x1b[?25l\x1b[?25h).\n\n\x1b[?25l\x1b[?25hLet\x1b[?25l\x1b[?25h me\x1b[?25l\x1b[?25h break\x1b[?25l\x1b[?25h down\x1b[?25l\x1b[?25h │ │
│ │ the\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h differences\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h For\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h purpose\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │
│ │ classical\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h creating\x1b[?25l\x1b[?25h associations\x1b[?25l\x1b[?25h between\x1b[?25l\x1b[?25h │ │
│ │ stimuli\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h while\x1b[?25l\x1b[?25h oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h how\x1b[?25l\x1b[?25h │ │
│ │ behaviors\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h learned\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hIn\x1b[?25l\x1b[?25h │ │
│ │ terms\x1b[?25l\x1b[?25h of\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h process\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h classical\x1b[?25l\x1b[?25h involves\x1b[?25l\x1b[?25h pairing\x1b[?25l\x1b[?25h │ │
│ │ UCS\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h CS\x1b[?25l\x1b[?25h until\x1b[?25l\x1b[?25h they\x1b[?25l\x1b[?25h el\x1b[?25l\x1b[?25hicit\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h │ │
│ │ same\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h uses\x1b[?25l\x1b[?25h reinforcement\x1b[?25l\x1b[?25h │ │
│ │ schedules\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h shape\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h over\x1b[?25l\x1b[?25h time\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hKey\x1b[?25l\x1b[?25h │ │
│ │ elements\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h has\x1b[?25l\x1b[?25h UCS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h CS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │
│ │ and\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hCR\x1b[?25l\x1b[?25h).\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │
│ │ focuses\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h ant\x1b[?25l\x1b[?25heced\x1b[?25l\x1b[?25hents\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │
│ │ and\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h rein\x1b[?25l\x1b[?25hforc\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │
│ │ punish\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hApplications\x1b[?25l\x1b[?25h differ\x1b[?25l\x1b[?25h too\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h │ │
│ │ is\x1b[?25l\x1b[?25h used\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h things\x1b[?25l\x1b[?25h like\x1b[?25l\x1b[?25h ph\x1b[?25l\x1b[?25hobia\x1b[?25l\x1b[?25h treatment\x1b[?25l\x1b[?25h │ │
│ │ through\x1b[?25l\x1b[?25h des\x1b[?25l\x1b[?25hens\x1b[?25l\x1b[?25hit\x1b[?25l\x1b[?25hization\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │
│ │ conditioning\x1b[?25l\x1b[?25h applies\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h teaching\x1b[?25l\x1b[?25h new\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │
│ │ like\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h animal\x1b[?25l\x1b[?25h training\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h improving\x1b[?25l\x1b[?25h human\x1b[?25l\x1b[?25h │ │
│ │ behavior\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h rewards\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hWait\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h am\x1b[?25l\x1b[?25h I\x1b[?25l\x1b[?25h │ │
│ │ mixing\x1b[?25l\x1b[?25h up\x1b[?25l\x1b[?25h anything\x1b[?25l\x1b[?25h?\x1b[?25l\x1b[?25h Let\x1b[?25l\x1b[?25h me\x1b[?25l\x1b[?25h │ │
│ │ double\x1b[?25l\x1b[?25h-check\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h-response\x1b[?25l\x1b[?25h │ │
│ │ relationships\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h not\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h itself\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h but\x1b[?25l\x1b[?25h │ │
│ │ the\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h stimuli\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h │ │
│ │ about\x1b[?25l\x1b[?25h controlling\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h which\x1b[?25l\x1b[?25h │ │
│ │ can\x1b[?25l\x1b[?25h be\x1b[?25l\x1b[?25h either\x1b[?25l\x1b[?25h positive\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hadding\x1b[?25l\x1b[?25h something\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h │ │
│ │ or\x1b[?25l\x1b[?25h negative\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hrem\x1b[?25l\x1b[?25hoving\x1b[?25l\x1b[?25h │ │
│ │ something\x1b[?25l\x1b[?25h).\n\n\x1b[?25l\x1b[?25hSo\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h putting\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h all\x1b[?25l\x1b[?25h │ │
│ │ together\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h differences\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h their\x1b[?25l\x1b[?25h │ │
│ │ purpose\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h processes\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h elements\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │
│ │ where\x1b[?25l\x1b[?25h they\x1b[?25l\x1b[?25h're\x1b[?25l\x1b[?25h applied\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hI\x1b[?25l\x1b[?25h think\x1b[?25l\x1b[?25h │ │
│ │ that\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h solid\x1b[?25l\x1b[?25h understanding\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Now\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │
│ │ I\x1b[?25l\x1b[?25h should\x1b[?25l\x1b[?25h structure\x1b[?25l\x1b[?25h this\x1b[?25l\x1b[?25h into\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h clear\x1b[?25l\x1b[?25h │ │
│ │ summary\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h making\x1b[?25l\x1b[?25h sure\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h highlight\x1b[?25l\x1b[?25h each\x1b[?25l\x1b[?25h difference\x1b[?25l\x1b[?25h │ │
│ │ conc\x1b[?25l\x1b[?25his\x1b[?25l\x1b[?25hely\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h\x1b[?25l\x1b[?25h\x1b[?25l\x1b[?25h\x1b[38;5;245m\x1b[1m...done │ │
│ │ thinking.\n\n\x1b[0m**\x1b[?25l\x1b[?25hKey\x1b[?25l\x1b[?25h Differences\x1b[?25l\x1b[?25h Between\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │
│ │ Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h**\n\n\x1b[?25l\x1b[?25h1\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ **\x1b[?25l\x1b[?25hPurpose\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h │ │
│ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Focus\x1b[?25l\x1b[?25hes\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h creating\x1b[?25l\x1b[?25h associations\x1b[?25l\x1b[?25h │ │
│ │ between\x1b[?25l\x1b[?25h stimuli\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h resulting\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h an\x1b[?25l\x1b[?25h automatic\x1b[?25l\x1b[?25h │ │
│ │ response\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │
│ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Concent\x1b[?25l\x1b[?25hrates\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h learning\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h │ │
│ │ the\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h of\x1b[?25l\x1b[?25h voluntary\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25h2\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ **\x1b[?25l\x1b[?25hProcess\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h │ │
│ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h In\x1b[?25l\x1b[?25hvol\x1b[?25l\x1b[?25hves\x1b[?25l\x1b[?25h pairing\x1b[?25l\x1b[?25h an\x1b[?25l\x1b[?25h │ │
│ │ un\x1b[?25l\x1b[?25hcondition\x1b[?25l\x1b[?25hed\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hUC\x1b[?25l\x1b[?25hS\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h │ │
│ │ with\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hCS\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h until\x1b[?25l\x1b[?25h │ │
│ │ the\x1b[?25l\x1b[?25h CS\x1b[?25l\x1b[?25h el\x1b[?25l\x1b[?25hicits\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h │ │
│ │ (\x1b[?25l\x1b[?25hCR\x1b[?25l\x1b[?25h).\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │
│ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Uses\x1b[?25l\x1b[?25h reinforcement\x1b[?25l\x1b[?25h schedules\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h strengthen\x1b[?25l\x1b[?25h │ │
│ │ desired\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h where\x1b[?25l\x1b[?25h actions\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h reinforced\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │
│ │ punished\x1b[?25l\x1b[?25h based\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h outcomes\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25h3\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ **\x1b[?25l\x1b[?25hKey\x1b[?25l\x1b[?25h Elements\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h │ │
│ │ **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h UCS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │
│ │ CS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h CR\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h │ │
│ │ **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h │ │
│ │ Ant\x1b[?25l\x1b[?25heced\x1b[?25l\x1b[?25hents\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h │ │
│ │ with\x1b[?25l\x1b[?25h rein\x1b[?25l\x1b[?25hforc\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │
│ │ punish\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25h4\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │
│ │ **\x1b[?25l\x1b[?25hApplications\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h │ │
│ │ **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Used\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h treating\x1b[?25l\x1b[?25h │ │
│ │ ph\x1b[?25l\x1b[?25hob\x1b[?25l\x1b[?25hias\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h des\x1b[?25l\x1b[?25hens\x1b[?25l\x1b[?25hit\x1b[?25l\x1b[?25hization\x1b[?25l\x1b[?25h │ │
│ │ by\x1b[?25l\x1b[?25h gradually\x1b[?25l\x1b[?25h exposing\x1b[?25l\x1b[?25h individuals\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h feared\x1b[?25l\x1b[?25h │ │
│ │ stimuli\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │
│ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Applied\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h teaching\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h │ │
│ │ (\x1b[?25l\x1b[?25he\x1b[?25l\x1b[?25h.g\x1b[?25l\x1b[?25h.,\x1b[?25l\x1b[?25h animal\x1b[?25l\x1b[?25h training\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │
│ │ improving\x1b[?25l\x1b[?25h human\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h rewards\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │
│ │ penalties\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hThese\x1b[?25l\x1b[?25h approaches\x1b[?25l\x1b[?25h offer\x1b[?25l\x1b[?25h distinct\x1b[?25l\x1b[?25h insights\x1b[?25l\x1b[?25h │ │
│ │ into\x1b[?25l\x1b[?25h how\x1b[?25l\x1b[?25h learning\x1b[?25l\x1b[?25h occurs\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h classical\x1b[?25l\x1b[?25h │ │
│ │ conditioning\x1b[?25l\x1b[?25h focusing\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h associations\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │
│ │ oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h conditioning\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h behavioral\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h\n\ntotal duration: │ │
│ │ 33.606704304s\nload duration: 4.344495371s\nprompt eval count: 18 token(s)\nprompt eval duration: 113.372084ms\nprompt eval rate: 158.77 tokens/s\neval count: 692 │ │
│ │ token(s)\neval duration: 27.01884757s\neval rate: 25.61 tokens/s\n\x1b[?25l\x1b[?25h", stderr='') │ │
│ │ std_err = '' │ │
│ │ stored_nums = [] │ │
│ │ type = 'instruction-question-answer-code-generation' │ │
│ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
│ │
│ /usr/lib/python3.12/subprocess.py:571 in run │
│ │
│ 568 │ │ │ raise │
│ 569 │ │ retcode = process.poll() │
│ 570 │ │ if check and retcode: │
│ ❱ 571 │ │ │ raise CalledProcessError(retcode, process.args, │
│ 572 │ │ │ │ │ │ │ │ │ output=stdout, stderr=stderr) │
│ 573 │ return CompletedProcess(process.args, retcode, stdout, stderr) │
│ 574 │
│ │
│ ╭─────────────────────────────────────────────────────────────────────────── locals ────────────────────────────────────────────────────────────────────────────╮ │
│ │ capture_output = True │ │
│ │ check = True │ │
│ │ input = None │ │
│ │ kwargs = {'text': True, 'encoding': 'utf-8', 'stdout': -1, 'stderr': -1} │ │
│ │ popenargs = (['ollama', 'run', 'deepseek-r1:14b', 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, '--verbose'],) │ │
│ │ process = <Popen: returncode: 1 args: ['ollama', 'run', 'deepseek-r1:14b', 'Translate ...> │ │
│ │ retcode = 1 │ │
│ │ stderr = '' │ │
│ │ stdout = "Error: unknown shorthand flag: '>' in ->\n" │ │
│ │ timeout = None │ │
│ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
CalledProcessError: Command '['ollama', 'run', 'deepseek-r1:14b', 'Translate the following English paragraph into Chinese and elaborate more -> Artificial intelligence is transforming various industries by enhancing
efficiency and enabling new capabilities.', '--verbose']' returned non-zero exit status 1.
$ cat /proc/cpuinfo |grep "model name"
model name : Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz
Also, I run ollama in docker with the following script:
$ cat ollama #!/bin/bash docker exec -it ollama ollama $*benchmark output:
$ llm_benchmark run -------Linux---------- {'id': '0', 'name': 'NVIDIA GeForce RTX 3060', 'driver': '580.119.02', 'gpu_memory_total': '12288.0 MB', 'gpu_memory_free': '711.0 MB', 'gpu_memory_used': '11197.0 MB', 'gpu_load': '0.0%', 'gpu_temperature': '40.0°C'} Only one GPU card Total memory size : 125.63 GB cpu_info: unknown gpu_info: NVIDIA GeForce RTX 3060 os_version: Pop!_OS 24.04 LTS ollama_version: 0.14.3 ---------- LLM models file path：/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/llm_benchmark/data/benchmark_models_32gb_ram.yml Checking and pulling the following LLM models phi4:14b deepseek-r1:14b gpt-oss:20b ---------- model_name = phi4:14b prompt = Write a step-by-step guide on how to bake a chocolate cake from scratch. prompt = Develop a python function that solves the following problem, sudoku game prompt = Create a dialogue between two characters that discusses economic crisis prompt = In a forest, there are brave lions living there. Please continue the story. prompt = I'd like to book a flight for 4 to Seattle in U.S. -------------------- ---------------------------------------- model_name = deepseek-r1:14b prompt = Summarize the key differences between classical and operant conditioning in psychology. prompt = Translate the following English paragraph into Chinese and elaborate more -> Artificial intelligence is transforming various industries by enhancing efficiency and enabling new capabilities. ╭───────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last) ──────────────────────────────────────────────────────────────────────────────────────────────╮ │ /home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/llm_benchmark/main.py:73 in run │ │ │ │ 70 │ │ bench_results_info.update(result2) │ │ 71 │ │ result3 = run_benchmark.run_benchmark(models_file_path,benchmark_file_path, 'vis │ │ 72 │ │ bench_results_info.update(result3) │ │ ❱ 73 │ │ result4 = run_benchmark.run_benchmark(models_file_path,benchmark_file_path, 'ins │ │ 74 │ │ bench_results_info.update(result4) │ │ 75 │ else: │ │ 76 │ │ bench_results_info.update({"llama2:7b":7.65}) │ │ │ │ ╭──────────────────────────────────────────────────────────────────────────────────────── locals ────────────────────────────────────────────────────────────────────────────────────────╮ │ │ │ bench_results_info = {} │ │ │ │ benchmark_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+32 │ │ │ │ custombenchmark = None │ │ │ │ ft_mem_size = 125.63 │ │ │ │ is_simulation = False │ │ │ │ models_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+47 │ │ │ │ ollama_version = '0.14.3' │ │ │ │ ollamabin = 'ollama' │ │ │ │ result1 = {} │ │ │ │ result2 = {} │ │ │ │ result3 = {} │ │ │ │ sendinfo = True │ │ │ │ sys_info = {'system': 'Linux', 'memory': 125.63174057006836, 'cpu': 'unknown', 'gpu': 'NVIDIA GeForce RTX 3060', 'os_version': 'Pop!_OS 24.04 LTS', 'system_name': 'Linux'} │ │ │ ╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │ │ │ │ /home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/llm_benchmark/run_benchmark.py:98 in run_benchmark │ │ │ │ 95 │ │ │ │ │ │ else: │ │ 96 │ │ │ │ │ │ │ for one_prompt in one_model_type['prompts']: │ │ 97 │ │ │ │ │ │ │ │ print(f"prompt = {one_prompt['prompt']}") │ │ ❱ 98 │ │ │ │ │ │ │ │ result = subprocess.run([ollamabin, 'run', model_name, o │ │ 99 │ │ │ │ │ │ │ │ std_err = result.stderr │ │ 100 │ │ │ │ │ │ │ │ #print(result.stderr) │ │ 101 │ │ │ │ │ │ │ │ file1.write(std_err) │ │ │ │ ╭───────────────────────────────────────────────────────────────────────────────────────────────────────── locals ─────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ │ │ allowed_models = {'phi4:14b', 'deepseek-r1:14b', 'gpt-oss:20b'} │ │ │ │ ans = {} │ │ │ │ benchmark_dict = { │ │ │ │ │ 'version': 2.0, │ │ │ │ │ 'modeltypes': [ │ │ │ │ │ │ {'type': 'chat', 'prompts': [{'prompt': 'What are your thoughts on the latest scientific discovery regarding black holes?', 'keywords': 'science, recent'}]}, │ │ │ │ │ │ {'type': 'completion', 'prompts': [{'prompt': 'In a world where time travel is possible, a scientist invents a device that allo'+100, 'keywords': None}]}, │ │ │ │ │ │ { │ │ │ │ │ │ │ 'type': 'instruct', │ │ │ │ │ │ │ 'models': [{'model': 'mistral:7b'}, {'model': 'llama3:8b'}, {'model': 'llama3.1:8b'}, {'model': 'phi4:14b'}, {'model': 'qwen:1.8b'}, {'model': 'qwen2:7b'}, {'model': 'phi:2.7b'}], │ │ │ │ │ │ │ 'prompts': [ │ │ │ │ │ │ │ │ {'prompt': 'Write a step-by-step guide on how to bake a chocolate cake from scratch.', 'keywords': 'cooking, recipe'}, │ │ │ │ │ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game', 'keywords': 'python, sudoku'}, │ │ │ │ │ │ │ │ {'prompt': 'Create a dialogue between two characters that discusses economic crisis', 'keywords': 'dialogue'}, │ │ │ │ │ │ │ │ {'prompt': 'In a forest, there are brave lions living there. Please continue the story.', 'keywords': 'sentence completition'}, │ │ │ │ │ │ │ │ {'prompt': "I'd like to book a flight for 4 to Seattle in U.S.", 'keywords': 'flight booking'} │ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ │ 'type': 'question-answer', │ │ │ │ │ │ │ 'reference_url': 'https://www.turing.com/interview-questions/artificial-intelligence', │ │ │ │ │ │ │ 'models': [{'model': 'gemma:2b'}, {'model': 'gemma:7b'}, {'model': 'gemma2:9b'}], │ │ │ │ │ │ │ 'prompts': [ │ │ │ │ │ │ │ │ {'prompt': 'Explain Artificial Intelligence and give its applications.', 'keywords': None}, │ │ │ │ │ │ │ │ {'prompt': 'How are machine learning and AI related?', 'keywords': None}, │ │ │ │ │ │ │ │ {'prompt': 'What is Deep Learning based on?', 'keywords': None}, │ │ │ │ │ │ │ │ {'prompt': 'What is the full form of LSTM?', 'keywords': None}, │ │ │ │ │ │ │ │ {'prompt': 'What are different components of GAN?', 'keywords': None} │ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ │ 'type': 'vision-image', │ │ │ │ │ │ │ 'reference_url': 'https://chuangtc.com/Research/llm-vlm.php', │ │ │ │ │ │ │ 'models': [{'model': 'llava:7b'}, {'model': 'llava:13b'}], │ │ │ │ │ │ │ 'prompts': [{'prompt': 'Describe the image,', 'keywords': 'sample1.jpg,sample2.jpg,sample3.jpg,sample4.jpg,sample5.jpg'}] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ │ 'type': 'instruction-question-answer-code-generation', │ │ │ │ │ │ │ 'models': [{'model': 'deepseek-r1:1.5b'}, {'model': 'deepseek-r1:8b'}, {'model': 'deepseek-r1:14b'}, {'model': 'gpt-oss:20b'}], │ │ │ │ │ │ │ 'prompts': [ │ │ │ │ │ │ │ │ {'prompt': 'Summarize the key differences between classical and operant conditioning in psyc'+7, 'keywords': 'Instruction-Following'}, │ │ │ │ │ │ │ │ {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'}, │ │ │ │ │ │ │ │ {'prompt': 'What are the main causes of the American Civil War?', 'keywords': 'Question-Answering'}, │ │ │ │ │ │ │ │ {'prompt': 'How does photosynthesis contribute to the carbon cycle?', 'keywords': 'Question-Answering'}, │ │ │ │ │ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game.', 'keywords': 'Code Generation'} │ │ │ │ │ │ │ ] │ │ │ │ │ │ }, │ │ │ │ │ │ { │ │ │ │ │ │ │ 'type': 'custom-model', │ │ │ │ │ │ │ 'models': None, │ │ │ │ │ │ │ 'prompts': [ │ │ │ │ │ │ │ │ {'prompt': 'Summarize the key differences between classical and operant conditioning in psyc'+7, 'keywords': 'Instruction-Following'}, │ │ │ │ │ │ │ │ {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'}, │ │ │ │ │ │ │ │ {'prompt': 'What are the main causes of the American Civil War?', 'keywords': 'Question-Answering'}, │ │ │ │ │ │ │ │ {'prompt': 'How does photosynthesis contribute to the carbon cycle?', 'keywords': 'Question-Answering'}, │ │ │ │ │ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game.', 'keywords': 'Code Generation'} │ │ │ │ │ │ │ ] │ │ │ │ │ │ } │ │ │ │ │ ] │ │ │ │ } │ │ │ │ benchmark_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+32 │ │ │ │ file1 = <_io.TextIOWrapper name='log_2026-01-22-152412.log' mode='w' encoding='utf-8'> │ │ │ │ line = '' │ │ │ │ loc_dt = datetime.datetime(2026, 1, 22, 15, 24, 12, 940758) │ │ │ │ model_name = 'deepseek-r1:14b' │ │ │ │ model_type = 'instruction-question-answer-code-generation' │ │ │ │ models_dict = {'version': 2.0, 'models': [{'model': 'phi4:14b'}, {'model': 'deepseek-r1:14b'}, {'model': 'gpt-oss:20b'}]} │ │ │ │ models_file_path = '/home/allan/.local/share/pipx/venvs/llm-benchmark/lib/python3.12/site-packages/l'+47 │ │ │ │ ollamabin = 'ollama' │ │ │ │ one_model_type = { │ │ │ │ │ 'type': 'instruction-question-answer-code-generation', │ │ │ │ │ 'models': [{'model': 'deepseek-r1:1.5b'}, {'model': 'deepseek-r1:8b'}, {'model': 'deepseek-r1:14b'}, {'model': 'gpt-oss:20b'}], │ │ │ │ │ 'prompts': [ │ │ │ │ │ │ {'prompt': 'Summarize the key differences between classical and operant conditioning in psyc'+7, 'keywords': 'Instruction-Following'}, │ │ │ │ │ │ {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'}, │ │ │ │ │ │ {'prompt': 'What are the main causes of the American Civil War?', 'keywords': 'Question-Answering'}, │ │ │ │ │ │ {'prompt': 'How does photosynthesis contribute to the carbon cycle?', 'keywords': 'Question-Answering'}, │ │ │ │ │ │ {'prompt': 'Develop a python function that solves the following problem, sudoku game.', 'keywords': 'Code Generation'} │ │ │ │ │ ] │ │ │ │ } │ │ │ │ one_prompt = {'prompt': 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, 'keywords': 'Instruction-Following'} │ │ │ │ onemodel = {'model': 'deepseek-r1:14b'} │ │ │ │ result = CompletedProcess(args=['ollama', 'run', 'deepseek-r1:14b', 'Summarize the key differences between classical and operant conditioning in psychology.', '--verbose'], returncode=0, │ │ │ │ stdout="\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?2026l\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?2026l\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?2026l\x1b[?2026h\x1b[?25l\x1b[1G\x1b[?25h\x1b[?20… │ │ │ │ I\x1b[?25l\x1b[?25h need\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h summarize\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h differences\x1b[?25l\x1b[?25h between\x1b[?25l\x1b[?25h │ │ │ │ classical\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h conditioning\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h psychology\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ Hmm\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h let\x1b[?25l\x1b[?25h me\x1b[?25l\x1b[?25h think\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h what\x1b[?25l\x1b[?25h each\x1b[?25l\x1b[?25h │ │ │ │ of\x1b[?25l\x1b[?25h these\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h first\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h │ │ │ │ Conditioning\x1b[?25l\x1b[?25h...\x1b[?25l\x1b[?25h I\x1b[?25l\x1b[?25h remember\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h │ │ │ │ associations\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Like\x1b[?25l\x1b[?25h Ivan\x1b[?25l\x1b[?25h Pav\x1b[?25l\x1b[?25hlov\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h experiment\x1b[?25l\x1b[?25h │ │ │ │ with\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h dogs\x1b[?25l\x1b[?25h where\x1b[?25l\x1b[?25h they\x1b[?25l\x1b[?25h learned\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h │ │ │ │ sal\x1b[?25l\x1b[?25hivate\x1b[?25l\x1b[?25h at\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h bell\x1b[?25l\x1b[?25h because\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h was\x1b[?25l\x1b[?25h │ │ │ │ associated\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h food\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h So\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h │ │ │ │ all\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h pairing\x1b[?25l\x1b[?25h two\x1b[?25l\x1b[?25h stimuli\x1b[?25l\x1b[?25h together\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h The\x1b[?25l\x1b[?25h │ │ │ │ un\x1b[?25l\x1b[?25hcondition\x1b[?25l\x1b[?25hed\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hUC\x1b[?25l\x1b[?25hS\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h │ │ │ │ like\x1b[?25l\x1b[?25h food\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h │ │ │ │ (\x1b[?25l\x1b[?25hCS\x1b[?25l\x1b[?25h),\x1b[?25l\x1b[?25h which\x1b[?25l\x1b[?25h becomes\x1b[?25l\x1b[?25h something\x1b[?25l\x1b[?25h else\x1b[?25l\x1b[?25h that\x1b[?25l\x1b[?25h │ │ │ │ triggers\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h same\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │ │ │ Conditioning\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h different\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h That\x1b[?25l\x1b[?25h one\x1b[?25l\x1b[?25h deals\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h │ │ │ │ voluntary\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h B\x1b[?25l\x1b[?25h.F\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ Skinner\x1b[?25l\x1b[?25h did\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h lot\x1b[?25l\x1b[?25h of\x1b[?25l\x1b[?25h work\x1b[?25l\x1b[?25h here\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ It\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h how\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h strengthened\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │ │ │ weakened\x1b[?25l\x1b[?25h based\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h rewards\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h punishments\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ So\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h if\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h leads\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h │ │ │ │ positive\x1b[?25l\x1b[?25h outcome\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h more\x1b[?25l\x1b[?25h likely\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h │ │ │ │ happen\x1b[?25l\x1b[?25h again\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hre\x1b[?25l\x1b[?25hin\x1b[?25l\x1b[?25hforcement\x1b[?25l\x1b[?25h),\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │ │ │ if\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h leads\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h negative\x1b[?25l\x1b[?25h outcome\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │ │ │ it\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h less\x1b[?25l\x1b[?25h likely\x1b[?25l\x1b[?25h │ │ │ │ (\x1b[?25l\x1b[?25hpun\x1b[?25l\x1b[?25hishment\x1b[?25l\x1b[?25h).\n\n\x1b[?25l\x1b[?25hLet\x1b[?25l\x1b[?25h me\x1b[?25l\x1b[?25h break\x1b[?25l\x1b[?25h down\x1b[?25l\x1b[?25h │ │ │ │ the\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h differences\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h For\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h purpose\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │ │ │ classical\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h creating\x1b[?25l\x1b[?25h associations\x1b[?25l\x1b[?25h between\x1b[?25l\x1b[?25h │ │ │ │ stimuli\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h while\x1b[?25l\x1b[?25h oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h how\x1b[?25l\x1b[?25h │ │ │ │ behaviors\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h learned\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hIn\x1b[?25l\x1b[?25h │ │ │ │ terms\x1b[?25l\x1b[?25h of\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h process\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h classical\x1b[?25l\x1b[?25h involves\x1b[?25l\x1b[?25h pairing\x1b[?25l\x1b[?25h │ │ │ │ UCS\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h CS\x1b[?25l\x1b[?25h until\x1b[?25l\x1b[?25h they\x1b[?25l\x1b[?25h el\x1b[?25l\x1b[?25hicit\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h │ │ │ │ same\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h uses\x1b[?25l\x1b[?25h reinforcement\x1b[?25l\x1b[?25h │ │ │ │ schedules\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h shape\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h over\x1b[?25l\x1b[?25h time\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hKey\x1b[?25l\x1b[?25h │ │ │ │ elements\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h has\x1b[?25l\x1b[?25h UCS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h CS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │ │ │ and\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hCR\x1b[?25l\x1b[?25h).\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │ │ │ focuses\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h ant\x1b[?25l\x1b[?25heced\x1b[?25l\x1b[?25hents\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │ │ │ and\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h rein\x1b[?25l\x1b[?25hforc\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │ │ │ punish\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hApplications\x1b[?25l\x1b[?25h differ\x1b[?25l\x1b[?25h too\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h │ │ │ │ is\x1b[?25l\x1b[?25h used\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h things\x1b[?25l\x1b[?25h like\x1b[?25l\x1b[?25h ph\x1b[?25l\x1b[?25hobia\x1b[?25l\x1b[?25h treatment\x1b[?25l\x1b[?25h │ │ │ │ through\x1b[?25l\x1b[?25h des\x1b[?25l\x1b[?25hens\x1b[?25l\x1b[?25hit\x1b[?25l\x1b[?25hization\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │ │ │ conditioning\x1b[?25l\x1b[?25h applies\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h teaching\x1b[?25l\x1b[?25h new\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │ │ │ like\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h animal\x1b[?25l\x1b[?25h training\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h improving\x1b[?25l\x1b[?25h human\x1b[?25l\x1b[?25h │ │ │ │ behavior\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h rewards\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hWait\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h am\x1b[?25l\x1b[?25h I\x1b[?25l\x1b[?25h │ │ │ │ mixing\x1b[?25l\x1b[?25h up\x1b[?25l\x1b[?25h anything\x1b[?25l\x1b[?25h?\x1b[?25l\x1b[?25h Let\x1b[?25l\x1b[?25h me\x1b[?25l\x1b[?25h │ │ │ │ double\x1b[?25l\x1b[?25h-check\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h about\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h-response\x1b[?25l\x1b[?25h │ │ │ │ relationships\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h not\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h itself\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h but\x1b[?25l\x1b[?25h │ │ │ │ the\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h stimuli\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h is\x1b[?25l\x1b[?25h │ │ │ │ about\x1b[?25l\x1b[?25h controlling\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h which\x1b[?25l\x1b[?25h │ │ │ │ can\x1b[?25l\x1b[?25h be\x1b[?25l\x1b[?25h either\x1b[?25l\x1b[?25h positive\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hadding\x1b[?25l\x1b[?25h something\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h │ │ │ │ or\x1b[?25l\x1b[?25h negative\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hrem\x1b[?25l\x1b[?25hoving\x1b[?25l\x1b[?25h │ │ │ │ something\x1b[?25l\x1b[?25h).\n\n\x1b[?25l\x1b[?25hSo\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h putting\x1b[?25l\x1b[?25h it\x1b[?25l\x1b[?25h all\x1b[?25l\x1b[?25h │ │ │ │ together\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h the\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h differences\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h their\x1b[?25l\x1b[?25h │ │ │ │ purpose\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h processes\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h key\x1b[?25l\x1b[?25h elements\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │ │ │ where\x1b[?25l\x1b[?25h they\x1b[?25l\x1b[?25h're\x1b[?25l\x1b[?25h applied\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hI\x1b[?25l\x1b[?25h think\x1b[?25l\x1b[?25h │ │ │ │ that\x1b[?25l\x1b[?25h's\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h solid\x1b[?25l\x1b[?25h understanding\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h Now\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │ │ │ I\x1b[?25l\x1b[?25h should\x1b[?25l\x1b[?25h structure\x1b[?25l\x1b[?25h this\x1b[?25l\x1b[?25h into\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h clear\x1b[?25l\x1b[?25h │ │ │ │ summary\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h making\x1b[?25l\x1b[?25h sure\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h highlight\x1b[?25l\x1b[?25h each\x1b[?25l\x1b[?25h difference\x1b[?25l\x1b[?25h │ │ │ │ conc\x1b[?25l\x1b[?25his\x1b[?25l\x1b[?25hely\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h\x1b[?25l\x1b[?25h\x1b[?25l\x1b[?25h\x1b[38;5;245m\x1b[1m...done │ │ │ │ thinking.\n\n\x1b[0m**\x1b[?25l\x1b[?25hKey\x1b[?25l\x1b[?25h Differences\x1b[?25l\x1b[?25h Between\x1b[?25l\x1b[?25h Classical\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │ │ │ Oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h**\n\n\x1b[?25l\x1b[?25h1\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ **\x1b[?25l\x1b[?25hPurpose\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h │ │ │ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Focus\x1b[?25l\x1b[?25hes\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h creating\x1b[?25l\x1b[?25h associations\x1b[?25l\x1b[?25h │ │ │ │ between\x1b[?25l\x1b[?25h stimuli\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h resulting\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h an\x1b[?25l\x1b[?25h automatic\x1b[?25l\x1b[?25h │ │ │ │ response\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │ │ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Concent\x1b[?25l\x1b[?25hrates\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h learning\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h │ │ │ │ the\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h of\x1b[?25l\x1b[?25h voluntary\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25h2\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ **\x1b[?25l\x1b[?25hProcess\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h │ │ │ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h In\x1b[?25l\x1b[?25hvol\x1b[?25l\x1b[?25hves\x1b[?25l\x1b[?25h pairing\x1b[?25l\x1b[?25h an\x1b[?25l\x1b[?25h │ │ │ │ un\x1b[?25l\x1b[?25hcondition\x1b[?25l\x1b[?25hed\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hUC\x1b[?25l\x1b[?25hS\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h │ │ │ │ with\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h (\x1b[?25l\x1b[?25hCS\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h until\x1b[?25l\x1b[?25h │ │ │ │ the\x1b[?25l\x1b[?25h CS\x1b[?25l\x1b[?25h el\x1b[?25l\x1b[?25hicits\x1b[?25l\x1b[?25h a\x1b[?25l\x1b[?25h conditioned\x1b[?25l\x1b[?25h response\x1b[?25l\x1b[?25h │ │ │ │ (\x1b[?25l\x1b[?25hCR\x1b[?25l\x1b[?25h).\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │ │ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Uses\x1b[?25l\x1b[?25h reinforcement\x1b[?25l\x1b[?25h schedules\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h strengthen\x1b[?25l\x1b[?25h │ │ │ │ desired\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h where\x1b[?25l\x1b[?25h actions\x1b[?25l\x1b[?25h are\x1b[?25l\x1b[?25h reinforced\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │ │ │ punished\x1b[?25l\x1b[?25h based\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h outcomes\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25h3\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ **\x1b[?25l\x1b[?25hKey\x1b[?25l\x1b[?25h Elements\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h │ │ │ │ **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h UCS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h │ │ │ │ CS\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h CR\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h │ │ │ │ **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h │ │ │ │ Ant\x1b[?25l\x1b[?25heced\x1b[?25l\x1b[?25hents\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h │ │ │ │ with\x1b[?25l\x1b[?25h rein\x1b[?25l\x1b[?25hforc\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │ │ │ punish\x1b[?25l\x1b[?25hers\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25h4\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h │ │ │ │ **\x1b[?25l\x1b[?25hApplications\x1b[?25l\x1b[?25h:\x1b[?25l\x1b[?25h**\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h │ │ │ │ **\x1b[?25l\x1b[?25hClass\x1b[?25l\x1b[?25hical\x1b[?25l\x1b[?25h Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Used\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h treating\x1b[?25l\x1b[?25h │ │ │ │ ph\x1b[?25l\x1b[?25hob\x1b[?25l\x1b[?25hias\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h des\x1b[?25l\x1b[?25hens\x1b[?25l\x1b[?25hit\x1b[?25l\x1b[?25hization\x1b[?25l\x1b[?25h │ │ │ │ by\x1b[?25l\x1b[?25h gradually\x1b[?25l\x1b[?25h exposing\x1b[?25l\x1b[?25h individuals\x1b[?25l\x1b[?25h to\x1b[?25l\x1b[?25h feared\x1b[?25l\x1b[?25h │ │ │ │ stimuli\x1b[?25l\x1b[?25h.\n\x1b[?25l\x1b[?25h \x1b[?25l\x1b[?25h -\x1b[?25l\x1b[?25h **\x1b[?25l\x1b[?25hOper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h │ │ │ │ Conditioning\x1b[?25l\x1b[?25h:**\x1b[?25l\x1b[?25h Applied\x1b[?25l\x1b[?25h in\x1b[?25l\x1b[?25h teaching\x1b[?25l\x1b[?25h behaviors\x1b[?25l\x1b[?25h │ │ │ │ (\x1b[?25l\x1b[?25he\x1b[?25l\x1b[?25h.g\x1b[?25l\x1b[?25h.,\x1b[?25l\x1b[?25h animal\x1b[?25l\x1b[?25h training\x1b[?25l\x1b[?25h)\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │ │ │ improving\x1b[?25l\x1b[?25h human\x1b[?25l\x1b[?25h behavior\x1b[?25l\x1b[?25h through\x1b[?25l\x1b[?25h rewards\x1b[?25l\x1b[?25h or\x1b[?25l\x1b[?25h │ │ │ │ penalties\x1b[?25l\x1b[?25h.\n\n\x1b[?25l\x1b[?25hThese\x1b[?25l\x1b[?25h approaches\x1b[?25l\x1b[?25h offer\x1b[?25l\x1b[?25h distinct\x1b[?25l\x1b[?25h insights\x1b[?25l\x1b[?25h │ │ │ │ into\x1b[?25l\x1b[?25h how\x1b[?25l\x1b[?25h learning\x1b[?25l\x1b[?25h occurs\x1b[?25l\x1b[?25h,\x1b[?25l\x1b[?25h with\x1b[?25l\x1b[?25h classical\x1b[?25l\x1b[?25h │ │ │ │ conditioning\x1b[?25l\x1b[?25h focusing\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h stimulus\x1b[?25l\x1b[?25h associations\x1b[?25l\x1b[?25h and\x1b[?25l\x1b[?25h │ │ │ │ oper\x1b[?25l\x1b[?25hant\x1b[?25l\x1b[?25h conditioning\x1b[?25l\x1b[?25h on\x1b[?25l\x1b[?25h behavioral\x1b[?25l\x1b[?25h consequences\x1b[?25l\x1b[?25h.\x1b[?25l\x1b[?25h\n\ntotal duration: │ │ │ │ 33.606704304s\nload duration: 4.344495371s\nprompt eval count: 18 token(s)\nprompt eval duration: 113.372084ms\nprompt eval rate: 158.77 tokens/s\neval count: 692 │ │ │ │ token(s)\neval duration: 27.01884757s\neval rate: 25.61 tokens/s\n\x1b[?25l\x1b[?25h", stderr='') │ │ │ │ std_err = '' │ │ │ │ stored_nums = [] │ │ │ │ type = 'instruction-question-answer-code-generation' │ │ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │ │ │ │ /usr/lib/python3.12/subprocess.py:571 in run │ │ │ │ 568 │ │ │ raise │ │ 569 │ │ retcode = process.poll() │ │ 570 │ │ if check and retcode: │ │ ❱ 571 │ │ │ raise CalledProcessError(retcode, process.args, │ │ 572 │ │ │ │ │ │ │ │ │ output=stdout, stderr=stderr) │ │ 573 │ return CompletedProcess(process.args, retcode, stdout, stderr) │ │ 574 │ │ │ │ ╭─────────────────────────────────────────────────────────────────────────── locals ────────────────────────────────────────────────────────────────────────────╮ │ │ │ capture_output = True │ │ │ │ check = True │ │ │ │ input = None │ │ │ │ kwargs = {'text': True, 'encoding': 'utf-8', 'stdout': -1, 'stderr': -1} │ │ │ │ popenargs = (['ollama', 'run', 'deepseek-r1:14b', 'Translate the following English paragraph into Chinese and elaborate more -> Art'+110, '--verbose'],) │ │ │ │ process = <Popen: returncode: 1 args: ['ollama', 'run', 'deepseek-r1:14b', 'Translate ...> │ │ │ │ retcode = 1 │ │ │ │ stderr = '' │ │ │ │ stdout = "Error: unknown shorthand flag: '>' in ->\n" │ │ │ │ timeout = None │ │ │ ╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ CalledProcessError: Command '['ollama', 'run', 'deepseek-r1:14b', 'Translate the following English paragraph into Chinese and elaborate more -> Artificial intelligence is transforming various industries by enhancing efficiency and enabling new capabilities.', '--verbose']' returned non-zero exit status 1. $ cat /proc/cpuinfo |grep "model name" model name : Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz