* [MAQAO] Info: Detected 1 Lprof instances in ortce-gh. 
If this is incorrect, rerun with number-processes-per-node=X
OMP: pid 81502 tid 81511 thread 6 bound to OS proc set {6}
OMP: pid 81502 tid 81514 thread 9 bound to OS proc set {9}
OMP: pid 81502 tid 81512 thread 7 bound to OS proc set {7}
OMP: pid 81502 tid 81553 thread 48 bound to OS proc set {48}
OMP: pid 81502 tid 81547 thread 42 bound to OS proc set {42}
OMP: pid 81502 tid 81544 thread 39 bound to OS proc set {39}
OMP: pid 81502 tid 81545 thread 40 bound to OS proc set {40}
OMP: pid 81502 tid 81541 thread 36 bound to OS proc set {36}
OMP: pid 81502 tid 81555 thread 50 bound to OS proc set {50}
OMP: pid 81502 tid 81535 thread 30 bound to OS proc set {30}
OMP: pid 81502 tid 81533 thread 28 bound to OS proc set {28}
OMP: pid 81502 tid 81537 thread 32 bound to OS proc set {32}
OMP: pid 81502 tid 81502 thread 0 bound to OS proc set {0}
OMP: pid 81502 tid 81543 thread 38 bound to OS proc set {38}
OMP: pid 81502 tid 81556 thread 51 bound to OS proc set {51}
OMP: pid 81502 tid 81536 thread 31 bound to OS proc set {31}
OMP: pid 81502 tid 81534 thread 29 bound to OS proc set {29}
OMP: pid 81502 tid 81565 thread 60 bound to OS proc set {60}
OMP: pid 81502 tid 81513 thread 8 bound to OS proc set {8}
OMP: pid 81502 tid 81507 thread 2 bound to OS proc set {2}
OMP: pid 81502 tid 81518 thread 13 bound to OS proc set {13}
OMP: pid 81502 tid 81574 thread 69 bound to OS proc set {69}
OMP: pid 81502 tid 81573 thread 68 bound to OS proc set {68}
OMP: pid 81502 tid 81523 thread 18 bound to OS proc set {18}
OMP: pid 81502 tid 81546 thread 41 bound to OS proc set {41}
OMP: pid 81502 tid 81540 thread 35 bound to OS proc set {35}
OMP: pid 81502 tid 81572 thread 67 bound to OS proc set {67}
OMP: pid 81502 tid 81558 thread 53 bound to OS proc set {53}
OMP: pid 81502 tid 81509 thread 4 bound to OS proc set {4}
OMP: pid 81502 tid 81516 thread 11 bound to OS proc set {11}
OMP: pid 81502 tid 81560 thread 55 bound to OS proc set {55}
OMP: pid 81502 tid 81557 thread 52 bound to OS proc set {52}
OMP: pid 81502 tid 81562 thread 57 bound to OS proc set {57}
OMP: pid 81502 tid 81559 thread 54 bound to OS proc set {54}
OMP: pid 81502 tid 81570 thread 65 bound to OS proc set {65}
OMP: pid 81502 tid 81519 thread 14 bound to OS proc set {14}
OMP: pid 81502 tid 81550 thread 45 bound to OS proc set {45}
OMP: pid 81502 tid 81564 thread 59 bound to OS proc set {59}
OMP: pid 81502 tid 81510 thread 5 bound to OS proc set {5}
OMP: pid 81502 tid 81515 thread 10 bound to OS proc set {10}
OMP: pid 81502 tid 81551 thread 46 bound to OS proc set {46}
OMP: pid 81502 tid 81552 thread 47 bound to OS proc set {47}
OMP: pid 81502 tid 81563 thread 58 bound to OS proc set {58}
OMP: pid 81502 tid 81506 thread 1 bound to OS proc set {1}
OMP: pid 81502 tid 81527 thread 22 bound to OS proc set {22}
OMP: pid 81502 tid 81561 thread 56 bound to OS proc set {56}
OMP: pid 81502 tid 81548 thread 43 bound to OS proc set {43}
OMP: pid 81502 tid 81522 thread 17 bound to OS proc set {17}
OMP: pid 81502 tid 81571 thread 66 bound to OS proc set {66}
OMP: pid 81502 tid 81569 thread 64 bound to OS proc set {64}
OMP: pid 81502 tid 81568 thread 63 bound to OS proc set {63}
OMP: pid 81502 tid 81508 thread 3 bound to OS proc set {3}
OMP: pid 81502 tid 81566 thread 61 bound to OS proc set {61}
OMP: pid 81502 tid 81531 thread 26 bound to OS proc set {26}
OMP: pid 81502 tid 81521 thread 16 bound to OS proc set {16}
OMP: pid 81502 tid 81539 thread 34 bound to OS proc set {34}
OMP: pid 81502 tid 81517 thread 12 bound to OS proc set {12}
OMP: pid 81502 tid 81528 thread 23 bound to OS proc set {23}
OMP: pid 81502 tid 81554 thread 49 bound to OS proc set {49}
OMP: pid 81502 tid 81532 thread 27 bound to OS proc set {27}
OMP: pid 81502 tid 81542 thread 37 bound to OS proc set {37}
OMP: pid 81502 tid 81567 thread 62 bound to OS proc set {62}
OMP: pid 81502 tid 81529 thread 24 bound to OS proc set {24}
OMP: pid 81502 tid 81549 thread 44 bound to OS proc set {44}
OMP: pid 81502 tid 81524 thread 19 bound to OS proc set {19}
OMP: pid 81502 tid 81520 thread 15 bound to OS proc set {15}
OMP: pid 81502 tid 81526 thread 21 bound to OS proc set {21}
OMP: pid 81502 tid 81525 thread 20 bound to OS proc set {20}
OMP: pid 81502 tid 81538 thread 33 bound to OS proc set {33}
OMP: pid 81502 tid 81576 thread 71 bound to OS proc set {71}
OMP: pid 81502 tid 81530 thread 25 bound to OS proc set {25}
OMP: pid 81502 tid 81575 thread 70 bound to OS proc set {70}
what is a LLM? and why should I care?
A Large Language Model (LLM) is a type of artificial intelligence (AI) that can process and generate human-like text based on the input it receives. LLMs are trained on vast amounts of text data, which allows them to learn patterns, relationships, and context in language. This enables them to generate coherent and often informative text responses to user queries.
LLMs have become increasingly popular in recent years, and for good reason. They offer a range of benefits that can be useful in various aspects of life, from personal to professional.
Here are some reasons why you should care about LLMs:
1. Improved communication: LLMs can help with writing and communication tasks, such as writing emails, reports, and articles. They can also assist with language translation, summarization, and even conversation.
2. Enhanced productivity: LLMs can automate routine tasks, such as data entry, research, and content creation, freeing up time for more creative and strategic work.
3. Better learning: LLMs can help with language learning, providing interactive and adaptive lessons that cater to individual needs and learning styles.
4. Access to knowledge: LLMs can provide quick answers to complex questions, making it easier to access and learn new information.
5. Personalization: LLMs can be fine-tuned to understand individual preferences, interests, and tone, making it possible to create personalized content and interactions.
6. Accessibility: LLMs can help individuals with disabilities, such as language barriers or cognitive impairments, to access and engage with information and services.
7. Fun and entertainment: LLMs can generate creative content, such as stories, poetry, and even entire scripts, for entertainment purposes.
8. Research and innovation: LLMs can aid in scientific research, data analysis, and hypothesis generation, driving innovation and discovery.
Some notable examples of LLMs include:
1. Chatbots: Virtual assistants that can engage in conversation and answer questions.
2. Language translation tools: Like Google Translate, which can translate text and speech in real-time.
3. Writing assistants: Tools like Grammarly and Language Tool, which can help with grammar, syntax, and style.
4. Research assistants: LLMs that can help with literature reviews, data analysis, and research writing.
5. Content generation tools: Like WordLift, which can generate content based on user input and preferences.

In summary, LLMs are powerful tools that can enhance communication, productivity, learning, and access to



Your experiment path is /scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0

To display your profiling results:
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#    LEVEL    |     REPORT     |                                                                                         COMMAND                                                                                         #
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#  Functions  |  Cluster-wide  |  maqao lprof -df xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0      #
#  Functions  |  Per-node      |  maqao lprof -df -dn xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0  #
#  Functions  |  Per-process   |  maqao lprof -df -dp xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0  #
#  Functions  |  Per-thread    |  maqao lprof -df -dt xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0  #
#  Loops      |  Cluster-wide  |  maqao lprof -dl xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0      #
#  Loops      |  Per-node      |  maqao lprof -dl -dn xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0  #
#  Loops      |  Per-process   |  maqao lprof -dl -dp xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0  #
#  Loops      |  Per-thread    |  maqao lprof -dl -dt xp=/scratch/users/amazouz/QAAS/service/Llama.cpp/ortce-gh/175-931-3387/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1759313640/tools/lprof_npsu_run_0  #
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