Scheduled Commit

data/clustering_battle-031dca12-9a97-4178-b56e-afd2ee86cecb.jsonl

@@ -1,2 +1,3 @@
 {"tstamp": 1735881028.3917, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "5fd17f97ea254227ba80c20f8aece83a", "0_model_name": "text-embedding-004", "0_prompt": ["Purkinje", "pyramidal", "motor", "sensory", "green", "purple", "yellow", "red", "blue", "pink", "orange", "AWS", "IBM Cloud", "DigitalOcean", "willow", "maple", "oak"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "e6d47ea35c6e42c38873a1d3636e1cb9", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": ["Purkinje", "pyramidal", "motor", "sensory", "green", "purple", "yellow", "red", "blue", "pink", "orange", "AWS", "IBM Cloud", "DigitalOcean", "willow", "maple", "oak"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
 {"tstamp": 1735881103.9453, "task_type": "clustering", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "c5aaa9763b114424a24a773359d56059", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": ["square", "pentagon", "octagon", "jiu-jitsu", "kung fu", "taekwondo", "muay thai", "judo", "karate", "Mandarin", "French", "Arabic", "Spanish", "Monopoly", "chess", "Catan", "Risk"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "c07e7240bf874cecaeabfe5a9fa595ff", "1_model_name": "text-embedding-3-large", "1_prompt": ["square", "pentagon", "octagon", "jiu-jitsu", "kung fu", "taekwondo", "muay thai", "judo", "karate", "Mandarin", "French", "Arabic", "Spanish", "Monopoly", "chess", "Catan", "Risk"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}
+{"tstamp": 1735933264.0071, "task_type": "clustering", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "e6f275ee59894d8e8c04a86b55189ef3", "0_model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "0_prompt": ["convex", "plane", "parabolic", "elephant", "lion", "giraffe", "tiger", "penguin", "carnation", "daisy", "sunflower", "tulip", "orchid", "rose", "BMW", "Nissan", "GMC"], "0_ncluster": 4, "0_output": "", "0_ndim": "3D (press for 2D)", "0_dim_method": "PCA", "0_clustering_method": "KMeans", "1_conv_id": "12662795cc794d41bf588f198780f8d8", "1_model_name": "embed-english-v3.0", "1_prompt": ["convex", "plane", "parabolic", "elephant", "lion", "giraffe", "tiger", "penguin", "carnation", "daisy", "sunflower", "tulip", "orchid", "rose", "BMW", "Nissan", "GMC"], "1_ncluster": 4, "1_output": "", "1_ndim": "3D (press for 2D)", "1_dim_method": "PCA", "1_clustering_method": "KMeans"}

data/clustering_individual-031dca12-9a97-4178-b56e-afd2ee86cecb.jsonl

@@ -8,3 +8,5 @@
 {"tstamp": 1735881005.1427, "task_type": "clustering", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1735881004.7646, "finish": 1735881005.1427, "ip": "", "conv_id": "e6d47ea35c6e42c38873a1d3636e1cb9", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": ["Purkinje", "pyramidal", "motor", "sensory", "green", "purple", "yellow", "red", "blue", "pink", "orange", "AWS", "IBM Cloud", "DigitalOcean", "willow", "maple", "oak"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1735881096.8436, "task_type": "clustering", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1735881095.7034, "finish": 1735881096.8436, "ip": "", "conv_id": "c5aaa9763b114424a24a773359d56059", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": ["square", "pentagon", "octagon", "jiu-jitsu", "kung fu", "taekwondo", "muay thai", "judo", "karate", "Mandarin", "French", "Arabic", "Spanish", "Monopoly", "chess", "Catan", "Risk"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
 {"tstamp": 1735881096.8436, "task_type": "clustering", "type": "chat", "model": "text-embedding-3-large", "gen_params": {}, "start": 1735881095.7034, "finish": 1735881096.8436, "ip": "", "conv_id": "c07e7240bf874cecaeabfe5a9fa595ff", "model_name": "text-embedding-3-large", "prompt": ["square", "pentagon", "octagon", "jiu-jitsu", "kung fu", "taekwondo", "muay thai", "judo", "karate", "Mandarin", "French", "Arabic", "Spanish", "Monopoly", "chess", "Catan", "Risk"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1735933253.4286, "task_type": "clustering", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1735933253.1347, "finish": 1735933253.4286, "ip": "", "conv_id": "e6f275ee59894d8e8c04a86b55189ef3", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": ["convex", "plane", "parabolic", "elephant", "lion", "giraffe", "tiger", "penguin", "carnation", "daisy", "sunflower", "tulip", "orchid", "rose", "BMW", "Nissan", "GMC"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}
+{"tstamp": 1735933253.4286, "task_type": "clustering", "type": "chat", "model": "embed-english-v3.0", "gen_params": {}, "start": 1735933253.1347, "finish": 1735933253.4286, "ip": "", "conv_id": "12662795cc794d41bf588f198780f8d8", "model_name": "embed-english-v3.0", "prompt": ["convex", "plane", "parabolic", "elephant", "lion", "giraffe", "tiger", "penguin", "carnation", "daisy", "sunflower", "tulip", "orchid", "rose", "BMW", "Nissan", "GMC"], "ncluster": 4, "output": "", "ndim": "3D (press for 2D)", "dim_method": "PCA", "clustering_method": "KMeans"}

data/retrieval_battle-031dca12-9a97-4178-b56e-afd2ee86cecb.jsonl

@@ -14,3 +14,4 @@
 {"tstamp": 1735867861.6484, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "1775dfab1ced4b1fb9d034ba0107a941", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "docker-compose", "0_output": [["docker-compose", "Stackoverflow Stackexchange\n\nQ: Static function declared but not defined in C++ I'm getting an error from the following code using C++.\nMain.cpp\n#include \"file.h\"\n\nint main()\n{\n   int k = GetInteger();\n   return 0;\n}\n\nFile.h\nstatic int GetInteger();\n\nFile.cpp\n#include \"file.h\"\n\nstatic int GetInteger()\n{\n   return 1;\n}\n\nThe error I get:\nError C2129: static function 'int GetInteger(void)' declared but not defined.\n\nI've read the famous article \"Organizing Code File in C and C++\", but don't understand what is wrong with this code.\n\nA: Because in this case, static means that the name of the function has\ninternal linkage; that GetInteger in one translation unit is unrelated\nto GetInteger in any other translation unit.  The keyword static is\noverloaded: in some cases, it affects lifetime, and in others, binding.\nIt's particularly confusing here, because \"static\" is also the name of a\nlifetime.  Functions, and data declared at namespace scope, always\nhave static lifetime; when static appears in their declaration, it\ncauses internal binding, instead of external. \n"]], "0_corpus": "stackexchange", "1_conv_id": "a2388458dae64dc2a436d25afca4a5b0", "1_model_name": "BM25", "1_prompt": "docker-compose", "1_output": [["docker-compose", "Stackoverflow Stackexchange\n\nQ: docker compose inside docker in a docker I am pretty new to docker and was following the documentation found here, trying deploy several containers inside dind using docker-compose 1.14.0 I get the following \ndocker run -v /home/dudarev/compose/:/compose/ --privileged docker:dind /compose/docker-compose\n/usr/local/bin/dockerd-entrypoint.sh: exec: line 21: /compose/docker-compose: not found\n\nDid I miss something?\n\nA: Add docker-compose installation to your Dockerfile before executing docker run. \nFor example, if you have an Ubuntu docker, add to your Dockerfile:\nRUN aptitude -y install docker-compose\nRUN ln -s /usr/local/bin/docker-compose /compose/docker-compose\n\nBecause it looks like if your entry-point looks up docker compose in /compose folder, while docker-compose is installed in /usr/local/bin by default.\n\nIf you want a concrete docker-compose version (for example 1.20.0-rc2):\n\nRUN curl -L https://github.com/docker/compose/releases/download/1.20.0-rc2/docker-compose-`uname -s`-`uname -m` -o /compose/docker-compose\nRUN chmod +x /compose/docker-compose\n\n"]], "1_corpus": "stackexchange"}
 {"tstamp": 1735897301.5128, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "9a463b1c10354845b1a960a1a1dd52bf", "0_model_name": "GritLM/GritLM-7B", "0_prompt": "Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "0_output": [["Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "0_corpus": "wikipedia", "1_conv_id": "1aa53bd671a2435987c08395357601f4", "1_model_name": "intfloat/multilingual-e5-large-instruct", "1_prompt": "Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "1_output": [["Bài kiểm tra nào được đưa ra để xác định liệu robot có thể suy nghĩ hay không?\n", "Computing Machinery and Intelligence\n\n\"Computing Machinery and Intelligence\" is a seminal paper written by Alan Turing on the topic of artificial intelligence. The paper, published in 1950 in Mind, was the first to introduce his concept of what is now known as the Turing test to the general public.\nTuring's paper considers the question \"Can machines think?\" Turing says that since the words \"think\" and \"machine\" cannot be clearly defined we should \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" To do this, he must first find a simple and unambiguous idea to replace the word \"think\", second he must explain exactly which \"machines\" he is considering, and finally, armed with these tools, he formulates a new question, related to the first, that he believes he can answer in the affirmative."]], "1_corpus": "wikipedia"}
 {"tstamp": 1735932045.7805, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "98144d9d0aa54f6bba70378e6d5933da", "0_model_name": "intfloat/e5-mistral-7b-instruct", "0_prompt": "what openai embedding dimension to use", "0_output": [["what openai embedding dimension to use", "Word embedding\n\nIn natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.\nMethods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear.\nWord and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.\nDevelopment and history of the approach\nIn distributional semantics, a quantitative methodological approach to understanding meaning in observed language, word embeddings or semantic feature space models have been used as a knowledge representation for some time. Such models aim to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data.  The underlying idea that \"a word is characterized by the company it keeps\" was proposed in a 1957 article by John Rupert Firth, but also has roots in the contemporaneous work on search systems and in cognitive psychology."]], "0_corpus": "wikipedia", "1_conv_id": "82e5c5b1bf9a468186f7afe7e374731a", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "what openai embedding dimension to use", "1_output": [["what openai embedding dimension to use", "OpenAI\n\nOpenAI is an American artificial intelligence (AI) research organization founded in December 2015 and headquartered in San Francisco. Its mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, with AGI defined as \"highly autonomous systems that outperform humans at most economically valuable work\". As a leading organization in the ongoing AI boom, OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI.\nThe organization consists of the non-profit OpenAI, Inc. registered in Delaware and its for-profit subsidiary OpenAI Global, LLC. Microsoft owns roughly 49% of OpenAI's equity, having invested US$13 billion. It also provides computing resources to OpenAI through its Microsoft Azure cloud platform.\nIn 2023 and 2024, OpenAI faced multiple lawsuits for alleged copyright infringement against authors and media companies whose work was used to train some of OpenAI's products. In November 2023, OpenAI's board removed Sam Altman as CEO citing a lack of confidence in him, and then reinstated him five days later after negotiations resulting in a reconstructed board. OpenAI's board has since added former US Treasury Secretary Lawrence Summers and former National Security Agency head Paul Nakasone.\nHistory"]], "1_corpus": "wikipedia"}
+{"tstamp": 1735933231.4789, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "4cbf0adefe844dceb1103f68f15b27e0", "0_model_name": "BM25", "0_prompt": "can you use rdimm and udimm?", "0_output": [["can you use rdimm and udimm?", "Superuser Stackexchange\n\nQ: Is my consumer RAM RDIMM or UDIMM? I've set out to build a server and I might have ordered the wrong RAM.\nThe motherboard I'm using is an ASUS KGPE-D16. In the specifications, it states that I can have 256GB of RDIMM or 64GB of UDIMM. The RAM I bought is consumer, CORSAIR Vengeance CMZ32GX3M4X1600C10. The memory format for the RAM is just \"DIMM\".\nThe question I have is what type of DIMM is my RAM? RDIMM or UDIMM? Or is it neither, meaning it won't be supported at all?\n\nA: *\n\n*UDIMM = Un-Buffered RAM\n\n*RDIMM = Registered RAM\nUnless you are specifically setting out to by registered RAM, then chances are you've bought Un-Buffered RAM. Un-Buffered RAM makes up the majority of consumer-level RAM available, and registered RAM is typically plastered in warnings it's not for most motherboards.\n"]], "0_corpus": "stackexchange", "1_conv_id": "c9f7febc4928437e9d940e8d8e55992c", "1_model_name": "nomic-ai/nomic-embed-text-v1.5", "1_prompt": "can you use rdimm and udimm?", "1_output": [["can you use rdimm and udimm?", "Superuser Stackexchange\n\nQ: Is my consumer RAM RDIMM or UDIMM? I've set out to build a server and I might have ordered the wrong RAM.\nThe motherboard I'm using is an ASUS KGPE-D16. In the specifications, it states that I can have 256GB of RDIMM or 64GB of UDIMM. The RAM I bought is consumer, CORSAIR Vengeance CMZ32GX3M4X1600C10. The memory format for the RAM is just \"DIMM\".\nThe question I have is what type of DIMM is my RAM? RDIMM or UDIMM? Or is it neither, meaning it won't be supported at all?\n\nA: *\n\n*UDIMM = Un-Buffered RAM\n\n*RDIMM = Registered RAM\nUnless you are specifically setting out to by registered RAM, then chances are you've bought Un-Buffered RAM. Un-Buffered RAM makes up the majority of consumer-level RAM available, and registered RAM is typically plastered in warnings it's not for most motherboards.\n"]], "1_corpus": "stackexchange"}

data/retrieval_individual-031dca12-9a97-4178-b56e-afd2ee86cecb.jsonl

@@ -84,3 +84,5 @@
 {"tstamp": 1735920460.9185, "task_type": "retrieval", "type": "chat", "model": "GritLM/GritLM-7B", "gen_params": {}, "start": 1735920458.6327, "finish": 1735920460.9185, "ip": "", "conv_id": "352c37ba58c9425292a12efdf6215b72", "model_name": "GritLM/GritLM-7B", "prompt": "Which test was devised to determine whether robots can think?", "output": [["Which test was devised to determine whether robots can think?", "Turing test\n\nThe test was introduced by Turing in his 1950 paper \"Computing Machinery and Intelligence\" while working at the University of Manchester. It opens with the words: \"I propose to consider the question, 'Can machines think? Because \"thinking\" is difficult to define, Turing chooses to \"replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.\" Turing describes the new form of the problem in terms of a three-person game called the \"imitation game\", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: \"Are there imaginable digital computers which would do well in the imitation game?\" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that \"machines can think\".\nSince Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a \"mind\", \"understanding\", or \"consciousness\", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.\nHistory"]], "corpus": "wikipedia"}
 {"tstamp": 1735932029.1532, "task_type": "retrieval", "type": "chat", "model": "intfloat/e5-mistral-7b-instruct", "gen_params": {}, "start": 1735932023.8881, "finish": 1735932029.1532, "ip": "", "conv_id": "98144d9d0aa54f6bba70378e6d5933da", "model_name": "intfloat/e5-mistral-7b-instruct", "prompt": "what openai embedding dimension to use", "output": [["what openai embedding dimension to use", "Word embedding\n\nIn natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.\nMethods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear.\nWord and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.\nDevelopment and history of the approach\nIn distributional semantics, a quantitative methodological approach to understanding meaning in observed language, word embeddings or semantic feature space models have been used as a knowledge representation for some time. Such models aim to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data.  The underlying idea that \"a word is characterized by the company it keeps\" was proposed in a 1957 article by John Rupert Firth, but also has roots in the contemporaneous work on search systems and in cognitive psychology."]], "corpus": "wikipedia"}
 {"tstamp": 1735932029.1532, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1735932023.8881, "finish": 1735932029.1532, "ip": "", "conv_id": "82e5c5b1bf9a468186f7afe7e374731a", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "what openai embedding dimension to use", "output": [["what openai embedding dimension to use", "OpenAI\n\nOpenAI is an American artificial intelligence (AI) research organization founded in December 2015 and headquartered in San Francisco. Its mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, with AGI defined as \"highly autonomous systems that outperform humans at most economically valuable work\". As a leading organization in the ongoing AI boom, OpenAI is known for the GPT family of large language models, the DALL-E series of text-to-image models, and a text-to-video model named Sora. Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI.\nThe organization consists of the non-profit OpenAI, Inc. registered in Delaware and its for-profit subsidiary OpenAI Global, LLC. Microsoft owns roughly 49% of OpenAI's equity, having invested US$13 billion. It also provides computing resources to OpenAI through its Microsoft Azure cloud platform.\nIn 2023 and 2024, OpenAI faced multiple lawsuits for alleged copyright infringement against authors and media companies whose work was used to train some of OpenAI's products. In November 2023, OpenAI's board removed Sam Altman as CEO citing a lack of confidence in him, and then reinstated him five days later after negotiations resulting in a reconstructed board. OpenAI's board has since added former US Treasury Secretary Lawrence Summers and former National Security Agency head Paul Nakasone.\nHistory"]], "corpus": "wikipedia"}
+{"tstamp": 1735933200.3628, "task_type": "retrieval", "type": "chat", "model": "BM25", "gen_params": {}, "start": 1735933198.718, "finish": 1735933200.3628, "ip": "", "conv_id": "4cbf0adefe844dceb1103f68f15b27e0", "model_name": "BM25", "prompt": "can you use rdimm and udimm?", "output": [["can you use rdimm and udimm?", "Superuser Stackexchange\n\nQ: Is my consumer RAM RDIMM or UDIMM? I've set out to build a server and I might have ordered the wrong RAM.\nThe motherboard I'm using is an ASUS KGPE-D16. In the specifications, it states that I can have 256GB of RDIMM or 64GB of UDIMM. The RAM I bought is consumer, CORSAIR Vengeance CMZ32GX3M4X1600C10. The memory format for the RAM is just \"DIMM\".\nThe question I have is what type of DIMM is my RAM? RDIMM or UDIMM? Or is it neither, meaning it won't be supported at all?\n\nA: *\n\n*UDIMM = Un-Buffered RAM\n\n*RDIMM = Registered RAM\nUnless you are specifically setting out to by registered RAM, then chances are you've bought Un-Buffered RAM. Un-Buffered RAM makes up the majority of consumer-level RAM available, and registered RAM is typically plastered in warnings it's not for most motherboards.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1735933200.3628, "task_type": "retrieval", "type": "chat", "model": "nomic-ai/nomic-embed-text-v1.5", "gen_params": {}, "start": 1735933198.718, "finish": 1735933200.3628, "ip": "", "conv_id": "c9f7febc4928437e9d940e8d8e55992c", "model_name": "nomic-ai/nomic-embed-text-v1.5", "prompt": "can you use rdimm and udimm?", "output": [["can you use rdimm and udimm?", "Superuser Stackexchange\n\nQ: Is my consumer RAM RDIMM or UDIMM? I've set out to build a server and I might have ordered the wrong RAM.\nThe motherboard I'm using is an ASUS KGPE-D16. In the specifications, it states that I can have 256GB of RDIMM or 64GB of UDIMM. The RAM I bought is consumer, CORSAIR Vengeance CMZ32GX3M4X1600C10. The memory format for the RAM is just \"DIMM\".\nThe question I have is what type of DIMM is my RAM? RDIMM or UDIMM? Or is it neither, meaning it won't be supported at all?\n\nA: *\n\n*UDIMM = Un-Buffered RAM\n\n*RDIMM = Registered RAM\nUnless you are specifically setting out to by registered RAM, then chances are you've bought Un-Buffered RAM. Un-Buffered RAM makes up the majority of consumer-level RAM available, and registered RAM is typically plastered in warnings it's not for most motherboards.\n"]], "corpus": "stackexchange"}