* [MAQAO] Info: Detected 1 Lprof instances in ip-172-31-18-66.
If this is incorrect, rerun with number-processes-per-node=X
[0mOMP: pid 17243 tid 17310 thread 1 bound to OS proc set {1}
OMP: pid 17243 tid 17243 thread 0 bound to OS proc set {0}
OMP: pid 17243 tid 17311 thread 2 bound to OS proc set {2}
OMP: pid 17243 tid 17312 thread 3 bound to OS proc set {3}
OMP: pid 17243 tid 17318 thread 9 bound to OS proc set {9}
OMP: pid 17243 tid 17317 thread 8 bound to OS proc set {8}
OMP: pid 17243 tid 17319 thread 10 bound to OS proc set {10}
OMP: pid 17243 tid 17320 thread 11 bound to OS proc set {11}
OMP: pid 17243 tid 17313 thread 4 bound to OS proc set {4}
OMP: pid 17243 tid 17314 thread 5 bound to OS proc set {5}
OMP: pid 17243 tid 17315 thread 6 bound to OS proc set {6}
OMP: pid 17243 tid 17327 thread 18 bound to OS proc set {18}
OMP: pid 17243 tid 17326 thread 17 bound to OS proc set {17}
OMP: pid 17243 tid 17322 thread 13 bound to OS proc set {13}
OMP: pid 17243 tid 17328 thread 19 bound to OS proc set {19}
OMP: pid 17243 tid 17316 thread 7 bound to OS proc set {7}
OMP: pid 17243 tid 17323 thread 14 bound to OS proc set {14}
OMP: pid 17243 tid 17324 thread 15 bound to OS proc set {15}
OMP: pid 17243 tid 17321 thread 12 bound to OS proc set {12}
OMP: pid 17243 tid 17358 thread 49 bound to OS proc set {49}
OMP: pid 17243 tid 17342 thread 33 bound to OS proc set {33}
OMP: pid 17243 tid 17325 thread 16 bound to OS proc set {16}
OMP: pid 17243 tid 17359 thread 50 bound to OS proc set {50}
OMP: pid 17243 tid 17343 thread 34 bound to OS proc set {34}
OMP: pid 17243 tid 17330 thread 21 bound to OS proc set {21}
OMP: pid 17243 tid 17329 thread 20 bound to OS proc set {20}
OMP: pid 17243 tid 17360 thread 51 bound to OS proc set {51}
OMP: pid 17243 tid 17333 thread 24 bound to OS proc set {24}
OMP: pid 17243 tid 17338 thread 29 bound to OS proc set {29}
OMP: pid 17243 tid 17335 thread 26 bound to OS proc set {26}
OMP: pid 17243 tid 17341 thread 32 bound to OS proc set {32}
OMP: pid 17243 tid 17337 thread 28 bound to OS proc set {28}
OMP: pid 17243 tid 17339 thread 30 bound to OS proc set {30}
OMP: pid 17243 tid 17349 thread 40 bound to OS proc set {40}
OMP: pid 17243 tid 17345 thread 36 bound to OS proc set {36}
OMP: pid 17243 tid 17344 thread 35 bound to OS proc set {35}
OMP: pid 17243 tid 17334 thread 25 bound to OS proc set {25}
OMP: pid 17243 tid 17331 thread 22 bound to OS proc set {22}
OMP: pid 17243 tid 17332 thread 23 bound to OS proc set {23}
OMP: pid 17243 tid 17336 thread 27 bound to OS proc set {27}
OMP: pid 17243 tid 17361 thread 52 bound to OS proc set {52}
OMP: pid 17243 tid 17357 thread 48 bound to OS proc set {48}
OMP: pid 17243 tid 17362 thread 53 bound to OS proc set {53}
OMP: pid 17243 tid 17363 thread 54 bound to OS proc set {54}
OMP: pid 17243 tid 17350 thread 41 bound to OS proc set {41}
OMP: pid 17243 tid 17352 thread 43 bound to OS proc set {43}
OMP: pid 17243 tid 17340 thread 31 bound to OS proc set {31}
OMP: pid 17243 tid 17353 thread 44 bound to OS proc set {44}
OMP: pid 17243 tid 17351 thread 42 bound to OS proc set {42}
OMP: pid 17243 tid 17348 thread 39 bound to OS proc set {39}
OMP: pid 17243 tid 17354 thread 45 bound to OS proc set {45}
OMP: pid 17243 tid 17365 thread 56 bound to OS proc set {56}
OMP: pid 17243 tid 17347 thread 38 bound to OS proc set {38}
OMP: pid 17243 tid 17355 thread 46 bound to OS proc set {46}
OMP: pid 17243 tid 17371 thread 62 bound to OS proc set {62}
OMP: pid 17243 tid 17369 thread 60 bound to OS proc set {60}
OMP: pid 17243 tid 17364 thread 55 bound to OS proc set {55}
OMP: pid 17243 tid 17356 thread 47 bound to OS proc set {47}
OMP: pid 17243 tid 17370 thread 61 bound to OS proc set {61}
OMP: pid 17243 tid 17367 thread 58 bound to OS proc set {58}
OMP: pid 17243 tid 17372 thread 63 bound to OS proc set {63}
OMP: pid 17243 tid 17366 thread 57 bound to OS proc set {57}
OMP: pid 17243 tid 17368 thread 59 bound to OS proc set {59}
OMP: pid 17243 tid 17346 thread 37 bound to OS proc set {37}
what is a LLM? and why it matters
Large Language Models (LLMs) have become a dominant force in the field of artificial intelligence, particularly in natural language processing (NLP). So, what exactly is a LLM, and why does it matter?
What is a Large Language Model (LLM)?
A Large Language Model (LLM) is a type of artificial intelligence (AI) designed to process and understand human language at an unprecedented scale. It's a neural network that takes in a text input and generates a response, often in a conversational manner. LLMs are typically trained on vast amounts of text data, which enables them to learn patterns, relationships, and contextual understanding of language.
There are two primary types of LLMs:
1. **Encoder-Decoder Architectures**: These models use a two-stage process to generate text. The encoder reads the input text and encodes it into a hidden representation, while the decoder generates the output text based on this representation. Examples include the Transformer and BART models.
2. **Autoregressive Models**: These models generate text one token at a time, predicting the next token based on the context of the previous tokens. Examples include the language models used in chatbots and virtual assistants, such as Google's LaMDA and Microsoft's Turing-NLG.
Why does it matter?
LLMs have significant implications in various areas:
1. **Conversational AI**: LLMs enable the creation of more human-like conversational interfaces, such as chatbots, voice assistants, and language translation systems.
2. **Content Generation**: LLMs can generate high-quality content, including text, images, and even code, which can revolutionize content creation and media production.
3. **Research and Education**: LLMs provide a powerful tool for researchers to analyze language patterns, understand linguistic structures, and explore cognitive biases.
4. **Customer Support**: LLMs can help automate customer support, providing quick and accurate responses to common queries, freeing up human support agents to focus on more complex issues.
5. **Language Understanding**: LLMs can improve language understanding and translation, enabling humans to communicate more effectively across languages and cultures.
6. **Creative Writing and Art**: LLMs can assist with creative writing, generating ideas, and even composing music, which can inspire new forms of artistic expression.
The limitations and challenges of LLMs are also significant:
1. **Bias and Sensitivity**: LLMs can perpetuate biases and stereotypes present in the training data, which can lead to problematic
Your experiment path is /home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/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=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
# Functions | Per-node | maqao lprof -df -dn xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
# Functions | Per-process | maqao lprof -df -dp xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
# Functions | Per-thread | maqao lprof -df -dt xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
# Loops | Cluster-wide | maqao lprof -dl xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
# Loops | Per-node | maqao lprof -dl -dn xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
# Loops | Per-process | maqao lprof -dl -dp xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
# Loops | Per-thread | maqao lprof -dl -dt xp=/home/eoseret/Tools/QaaS/qaas_runs/ip-172-31-18-66/175-768-6804/llama.cpp/run/oneview_runs/defaults/orig/oneview_results_1757686954/tools/lprof_npsu_run_0 #
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