Scheduled Commit

data/retrieval_battle-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -20,3 +20,5 @@
 {"tstamp": 1722446198.8045, "task_type": "retrieval", "type": "tievote", "models": ["", ""], "ip": "", "0_conv_id": "9e2f3e5d4eb04b5db030979a91bfe01e", "0_model_name": "text-embedding-3-large", "0_prompt": "How can a small visual-linguistic model be effectively trained using knowledge distillation?", "0_output": [["How can a small visual-linguistic model be effectively trained using knowledge distillation?", "Title: Compressing Visual-linguistic Model via Knowledge Distillation\n\nAbstract: Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student's detector while the features are from Teacher's own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages."]], "0_corpus": "arxiv", "1_conv_id": "99d70563aab749489c924deb1dc45544", "1_model_name": "Salesforce/SFR-Embedding-2_R", "1_prompt": "How can a small visual-linguistic model be effectively trained using knowledge distillation?", "1_output": [["How can a small visual-linguistic model be effectively trained using knowledge distillation?", "Title: Compressing Visual-linguistic Model via Knowledge Distillation\n\nAbstract: Despite exciting progress in pre-training for visual-linguistic (VL) representations, very few aspire to a small VL model. In this paper, we study knowledge distillation (KD) to effectively compress a transformer-based large VL model into a small VL model. The major challenge arises from the inconsistent regional visual tokens extracted from different detectors of Teacher and Student, resulting in the misalignment of hidden representations and attention distributions. To address the problem, we retrain and adapt the Teacher by using the same region proposals from Student's detector while the features are from Teacher's own object detector. With aligned network inputs, the adapted Teacher is capable of transferring the knowledge through the intermediate representations. Specifically, we use the mean square error loss to mimic the attention distribution inside the transformer block and present a token-wise noise contrastive loss to align the hidden state by contrasting with negative representations stored in a sample queue. To this end, we show that our proposed distillation significantly improves the performance of small VL models on image captioning and visual question answering tasks. It reaches 120.8 in CIDEr score on COCO captioning, an improvement of 5.1 over its non-distilled counterpart; and an accuracy of 69.8 on VQA 2.0, a 0.8 gain from the baseline. Our extensive experiments and ablations confirm the effectiveness of VL distillation in both pre-training and fine-tuning stages."]], "1_corpus": "arxiv"}
 {"tstamp": 1722447804.2737, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "a69e4fe18f394932a19bcd5b57c0266f", "0_model_name": "jinaai/jina-embeddings-v2-base-en", "0_prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "0_output": [["How can in-context learning emerge from pretraining documents with long-range coherence?", "Title: Order Preservation in Limit Algebras\n\nAbstract: The matrix units of a digraph algebra, A, induce a relation, known as the diagonal order, on the projections in a masa in the algebra. Normalizing partial isometries in A act on these projections by conjugation; they are said to be order preserving when they respect the diagonal order. Order preserving embeddings, in turn, are those embeddings which carry order preserving normalizers to order preserving normalizers. This paper studies operator algebras which are direct limits of finite dimensional algebras with order preserving embeddings. We give a complete classification of direct limits of full triangular matrix algebras with order preserving embeddings. We also investigate the problem of characterizing algebras with order preserving embeddings."]], "0_corpus": "arxiv", "1_conv_id": "05b4950e4c6247cd99901948d031c52e", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "1_output": [["How can in-context learning emerge from pretraining documents with long-range coherence?", "Title: An Explanation of In-context Learning as Implicit Bayesian Inference\n\nAbstract: Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning."]], "1_corpus": "arxiv"}
 {"tstamp": 1722448831.6873, "task_type": "retrieval", "type": "bothbadvote", "models": ["", ""], "ip": "", "0_conv_id": "7a79ffac6e0d41b3add4d6518d91cca8", "0_model_name": "BAAI/bge-large-en-v1.5", "0_prompt": "What's the purpose of `if __name__ == '__main__'`?", "0_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n    import pdb\n    pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "0_corpus": "stackexchange", "1_conv_id": "4e34da23af094e5bae7f2ad3357c2fe9", "1_model_name": "jinaai/jina-embeddings-v2-base-en", "1_prompt": "What's the purpose of `if __name__ == '__main__'`?", "1_output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "1_corpus": "stackexchange"}
+{"tstamp": 1722451025.5473, "task_type": "retrieval", "type": "leftvote", "models": ["", ""], "ip": "", "0_conv_id": "a19bf7564c984e3ba9c90cb4ad0724c9", "0_model_name": "intfloat/multilingual-e5-large-instruct", "0_prompt": "is harvard kennedy school part of harvard university", "0_output": [["is harvard kennedy school part of harvard university", "Harvard Kennedy School\n\nHKS courses Harvard Kennedy School maintains six academic divisions each headed by a faculty chair. In addition to offerings in the Harvard Kennedy School course listing, students are eligible to cross-register for courses at the other graduate and professional schools at Harvard and at the MIT Sloan School of Management, The Fletcher School of Law and Diplomacy at Tufts University, and the MIT School of Architecture and Planning. MPP coursework is focused on one of five areas, called a Policy Area of Concentration (PAC), and includes a year-long research seminar in their second year, which includes a master's thesis called a Policy Analysis Exercise.\nRankings\nHarvard Kennedy School has routinely ranked as the best, or among the best, of the world's public policy graduate schools. U.S. News & World Report ranks it the best graduate school for social policy, the best for health policy, and second best for public policy analysis. In 2015 rankings, Kennedy School is ranked first in the subcategory of health policy and second in the category of public policy analysis and social policy.\nKennedy's School's foreign affairs programs have consistently ranked at the top or near the top of Foreign Policy magazine's Inside the Ivory Tower survey, which lists the world's top twenty academic international relations programs at the undergraduate, Master's, and Ph.D. levels. In 2012, for example, the survey ranked Kennedy School first overall for doctoral and undergraduate programs and third overall in the Master's category.\nStudent organizations\nHarvard Kennedy School maintains a range of student activities, including interest-driven student caucuses, the student government (Kennedy School Student Government, known as KSSG), student-edited policy journals including Harvard Journal of Hispanic Policy, Kennedy School Review, the Journal of Middle Eastern Politics and Policy, a student newspaper (The Citizen), and a number of student athletic groups."]], "0_corpus": "wikipedia", "1_conv_id": "8ccc2c3a69d3474e967663216549eb5c", "1_model_name": "voyage-multilingual-2", "1_prompt": "is harvard kennedy school part of harvard university", "1_output": [["is harvard kennedy school part of harvard university", "Harvard Kennedy School\n\nIra Jackson (MPA '86), dean, Peter F. Drucker and Masatoshi Ito Graduate School of Management at Claremont Graduate University\nNancy Koehn (MPP), author, historian, and Harvard Business School business history professor\nMark Lilla (MPP '80), professor of humanities, Columbia University\nHollis Robbins (MPP '90), dean of humanities, University of Utah\nMark Schuster (MPP '88), dean and founding CEO, Kaiser Permanente Bernard J. Tyson School of Medicine\nStephen Joel Trachtenberg (MPA '66), former president, George Washington University\nWilliam E. Trueheart (MPA '73), former president, Bryant University\nJonathan Zittrain (MPA '95), professor of international law, Harvard Law School and co-founder of Harvard's Berkman Klein Center for Internet & Society\nArts\nWill Butler (MPA '17), musician and former member of Arcade Fire\nAshley Judd (MPA '10), actress and activist\nHill Harper (MPA '92), actor and author\nThor Steingraber (MPA '09), opera director\nDamian Woetzel (MPA '07), former principal dancer, New York City Ballet\nBusiness\nRune Bjerke (MPA '97), CEO, DNB ASA\nGregory C. Carr (MPP '86), founder, Boston Technology\nLeonard S. Coleman Jr. (MPA '75), former president of the National League\nBenjamin Fernandes (Exec. Ed'17), Tanzanian entrepreneur\nDebra L. Lee (MPP '80), President and CEO, Black Entertainment Television\nDaniel Mudd (MPA '86), former president and CEO of U.S. Fannie Mae"]], "1_corpus": "wikipedia"}
+{"tstamp": 1722451052.3458, "task_type": "retrieval", "type": "rightvote", "models": ["", ""], "ip": "", "0_conv_id": "92ab18d28fff48efba3b08078d47ad81", "0_model_name": "sentence-transformers/all-MiniLM-L6-v2", "0_prompt": "why does sx mean surgery?", "0_output": [["why does sx mean surgery?", "Stackoverflow Stackexchange\n\nQ: printf format for unsigned __int64 on Windows I need to print a ULONGLONG value (unsigned __int64). What format should i use in printf ?\nI found %llu in another question but they say it is for linux only.\nThanks for your help.\n\nA: I recommend you use PRIu64 format specified from a standard C library. It was designed to provide users with a format specifier for unsigned 64-bit integer across different architectures.\nHere is an example (in C, not C++):\n#include <stdint.h>   /* For uint64_t */\n#include <inttypes.h> /* For PRIu64 */\n#include <stdio.h>    /* For printf */\n#include <stdlib.h>   /* For exit status */\n\nint main()\n{\n    uint64_t n = 1986;\n    printf(\"And the winning number is.... %\" PRIu64 \"!\\n\", n);\n    return EXIT_SUCCESS;\n}\n\n"]], "0_corpus": "stackexchange", "1_conv_id": "81ae0eae1b3c462584fef0c6acb82a03", "1_model_name": "mixedbread-ai/mxbai-embed-large-v1", "1_prompt": "why does sx mean surgery?", "1_output": [["why does sx mean surgery?", "English Stackexchange\n\nQ: Is there a verb form of the word 'surgery'? Surgery as a noun is also performance of a surgical procedure. Surgical is an adjective. I couldn't find anywhere a verb for surgery. Like how you 'operate' for the noun operation. \n\nA: I would have said performing surgery. \n"]], "1_corpus": "stackexchange"}

data/retrieval_individual-407c4836-37e2-4f9f-8e9b-06706cc4440c.jsonl

@@ -58,3 +58,9 @@
 {"tstamp": 1722447761.2931, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722447761.1024, "finish": 1722447761.2931, "ip": "", "conv_id": "05b4950e4c6247cd99901948d031c52e", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "How can in-context learning emerge from pretraining documents with long-range coherence?", "output": [["How can in-context learning emerge from pretraining documents with long-range coherence?", "Title: An Explanation of In-context Learning as Implicit Bayesian Inference\n\nAbstract: Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, where the model learns to do a downstream task simply by conditioning on a prompt consisting of input-output examples. The LM learns from these examples without being explicitly pretrained to learn. Thus, it is unclear what enables in-context learning. In this paper, we study how in-context learning can emerge when pretraining documents have long-range coherence. Here, the LM must infer a latent document-level concept to generate coherent next tokens during pretraining. At test time, in-context learning occurs when the LM also infers a shared latent concept between examples in a prompt. We prove when this occurs despite a distribution mismatch between prompts and pretraining data in a setting where the pretraining distribution is a mixture of HMMs. In contrast to messy large-scale datasets used to train LMs capable of in-context learning, we generate a small-scale synthetic dataset (GINC) where Transformers and LSTMs both exhibit in-context learning. Beyond the theory, experiments on GINC exhibit large-scale real-world phenomena including improved in-context performance with model scaling (despite the same pretraining loss), sensitivity to example order, and instances where zero-shot is better than few-shot in-context learning."]], "corpus": "arxiv"}
 {"tstamp": 1722448793.3454, "task_type": "retrieval", "type": "chat", "model": "BAAI/bge-large-en-v1.5", "gen_params": {}, "start": 1722448793.1805, "finish": 1722448793.3454, "ip": "", "conv_id": "7a79ffac6e0d41b3add4d6518d91cca8", "model_name": "BAAI/bge-large-en-v1.5", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: Why does it do this ? if - __name__ == '__main__' \nDuplicate of:\nWhat does if __name__== \"__main__\" do? \n\nConsider this code:\nif __name__ == '__main__':\n    import pdb\n    pdb.run(\"interact()\\n\")\n\nWhat does the following line mean?\nif(__name__=='__main__')\n\nI fainted.\n\nA: That is a check to see if you are directly running the script or if it is included in a library.\nWhen you run a python script like this:\npython myScript.py\n\nIt sends a parameter, telling you to run the programs first method, which is widely called \"main\", so when __name__ is __main__ you know that the program was executed from a command line or double clicked.\n"]], "corpus": "stackexchange"}
 {"tstamp": 1722448793.3454, "task_type": "retrieval", "type": "chat", "model": "jinaai/jina-embeddings-v2-base-en", "gen_params": {}, "start": 1722448793.1805, "finish": 1722448793.3454, "ip": "", "conv_id": "4e34da23af094e5bae7f2ad3357c2fe9", "model_name": "jinaai/jina-embeddings-v2-base-en", "prompt": "What's the purpose of `if __name__ == '__main__'`?", "output": [["What's the purpose of `if __name__ == '__main__'`?", "Stackoverflow Stackexchange\n\nQ: html dangerous tags to avoid while developing a chat application I am developing a chat application using PHP and jQuery... all messages sent by the chat pair are appended to a <div>. While doing this, I found that tags such as <script> can be entered by users. What tags should I avoid while appending the user's message?\n\nA: Avoid tags that will inject content into the page:\n\n\n*\n\n*applet\n\n*script\n\n*style\n\n*link\n\n*iframe\n\n\nOther tags may be necessary to remove, as well. Maybe you don't want to embed images. These are things that you will need to determine. At the bare minimum, allow <p> and <br />.\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722450982.32, "task_type": "retrieval", "type": "chat", "model": "intfloat/multilingual-e5-large-instruct", "gen_params": {}, "start": 1722450981.8509, "finish": 1722450982.32, "ip": "", "conv_id": "a19bf7564c984e3ba9c90cb4ad0724c9", "model_name": "intfloat/multilingual-e5-large-instruct", "prompt": "is harvard kennedy school part of harvard university", "output": [["is harvard kennedy school part of harvard university", "Harvard Kennedy School\n\nHKS courses Harvard Kennedy School maintains six academic divisions each headed by a faculty chair. In addition to offerings in the Harvard Kennedy School course listing, students are eligible to cross-register for courses at the other graduate and professional schools at Harvard and at the MIT Sloan School of Management, The Fletcher School of Law and Diplomacy at Tufts University, and the MIT School of Architecture and Planning. MPP coursework is focused on one of five areas, called a Policy Area of Concentration (PAC), and includes a year-long research seminar in their second year, which includes a master's thesis called a Policy Analysis Exercise.\nRankings\nHarvard Kennedy School has routinely ranked as the best, or among the best, of the world's public policy graduate schools. U.S. News & World Report ranks it the best graduate school for social policy, the best for health policy, and second best for public policy analysis. In 2015 rankings, Kennedy School is ranked first in the subcategory of health policy and second in the category of public policy analysis and social policy.\nKennedy's School's foreign affairs programs have consistently ranked at the top or near the top of Foreign Policy magazine's Inside the Ivory Tower survey, which lists the world's top twenty academic international relations programs at the undergraduate, Master's, and Ph.D. levels. In 2012, for example, the survey ranked Kennedy School first overall for doctoral and undergraduate programs and third overall in the Master's category.\nStudent organizations\nHarvard Kennedy School maintains a range of student activities, including interest-driven student caucuses, the student government (Kennedy School Student Government, known as KSSG), student-edited policy journals including Harvard Journal of Hispanic Policy, Kennedy School Review, the Journal of Middle Eastern Politics and Policy, a student newspaper (The Citizen), and a number of student athletic groups."]], "corpus": "wikipedia"}
+{"tstamp": 1722450982.32, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722450981.8509, "finish": 1722450982.32, "ip": "", "conv_id": "8ccc2c3a69d3474e967663216549eb5c", "model_name": "voyage-multilingual-2", "prompt": "is harvard kennedy school part of harvard university", "output": [["is harvard kennedy school part of harvard university", "Harvard Kennedy School\n\nIra Jackson (MPA '86), dean, Peter F. Drucker and Masatoshi Ito Graduate School of Management at Claremont Graduate University\nNancy Koehn (MPP), author, historian, and Harvard Business School business history professor\nMark Lilla (MPP '80), professor of humanities, Columbia University\nHollis Robbins (MPP '90), dean of humanities, University of Utah\nMark Schuster (MPP '88), dean and founding CEO, Kaiser Permanente Bernard J. Tyson School of Medicine\nStephen Joel Trachtenberg (MPA '66), former president, George Washington University\nWilliam E. Trueheart (MPA '73), former president, Bryant University\nJonathan Zittrain (MPA '95), professor of international law, Harvard Law School and co-founder of Harvard's Berkman Klein Center for Internet & Society\nArts\nWill Butler (MPA '17), musician and former member of Arcade Fire\nAshley Judd (MPA '10), actress and activist\nHill Harper (MPA '92), actor and author\nThor Steingraber (MPA '09), opera director\nDamian Woetzel (MPA '07), former principal dancer, New York City Ballet\nBusiness\nRune Bjerke (MPA '97), CEO, DNB ASA\nGregory C. Carr (MPP '86), founder, Boston Technology\nLeonard S. Coleman Jr. (MPA '75), former president of the National League\nBenjamin Fernandes (Exec. Ed'17), Tanzanian entrepreneur\nDebra L. Lee (MPP '80), President and CEO, Black Entertainment Television\nDaniel Mudd (MPA '86), former president and CEO of U.S. Fannie Mae"]], "corpus": "wikipedia"}
+{"tstamp": 1722451038.8687, "task_type": "retrieval", "type": "chat", "model": "sentence-transformers/all-MiniLM-L6-v2", "gen_params": {}, "start": 1722451036.7142, "finish": 1722451038.8687, "ip": "", "conv_id": "92ab18d28fff48efba3b08078d47ad81", "model_name": "sentence-transformers/all-MiniLM-L6-v2", "prompt": "why does sx mean surgery?", "output": [["why does sx mean surgery?", "Stackoverflow Stackexchange\n\nQ: printf format for unsigned __int64 on Windows I need to print a ULONGLONG value (unsigned __int64). What format should i use in printf ?\nI found %llu in another question but they say it is for linux only.\nThanks for your help.\n\nA: I recommend you use PRIu64 format specified from a standard C library. It was designed to provide users with a format specifier for unsigned 64-bit integer across different architectures.\nHere is an example (in C, not C++):\n#include <stdint.h>   /* For uint64_t */\n#include <inttypes.h> /* For PRIu64 */\n#include <stdio.h>    /* For printf */\n#include <stdlib.h>   /* For exit status */\n\nint main()\n{\n    uint64_t n = 1986;\n    printf(\"And the winning number is.... %\" PRIu64 \"!\\n\", n);\n    return EXIT_SUCCESS;\n}\n\n"]], "corpus": "stackexchange"}
+{"tstamp": 1722451038.8687, "task_type": "retrieval", "type": "chat", "model": "mixedbread-ai/mxbai-embed-large-v1", "gen_params": {}, "start": 1722451036.7142, "finish": 1722451038.8687, "ip": "", "conv_id": "81ae0eae1b3c462584fef0c6acb82a03", "model_name": "mixedbread-ai/mxbai-embed-large-v1", "prompt": "why does sx mean surgery?", "output": [["why does sx mean surgery?", "English Stackexchange\n\nQ: Is there a verb form of the word 'surgery'? Surgery as a noun is also performance of a surgical procedure. Surgical is an adjective. I couldn't find anywhere a verb for surgery. Like how you 'operate' for the noun operation. \n\nA: I would have said performing surgery. \n"]], "corpus": "stackexchange"}
+{"tstamp": 1722451064.4955, "task_type": "retrieval", "type": "chat", "model": "voyage-multilingual-2", "gen_params": {}, "start": 1722451064.1256, "finish": 1722451064.4955, "ip": "", "conv_id": "1a1afc85cc934a73b5a381ff2a07e6fd", "model_name": "voyage-multilingual-2", "prompt": "who plays manny in diary of a wimpy kid", "output": [["who plays manny in diary of a wimpy kid", "List of Diary of a Wimpy Kid characters\n\nManny Heffley Emanuel \"Manny\" Heffley is Greg and Rodrick's 3-year-old (5-year-old in the Funbrain version) brother who is constantly spoiled and pampered by his parents, who even allow him to get away with drawing on Greg's door in permanent marker. As the youngest child of the Heffley family, he plays a major role in the Diary of a Wimpy Kid series, having appeared in all the main series books to date. He has shown cases of unusually high intelligence; for example, switching all his family's power to his room in Cabin Fever during a blizzard, resulting in Greg, Rodrick, and Susan almost freezing to death (excluding Frank, as he was in a hotel). In The Long Haul, Manny is given a piglet at a country fair.\nIn the film series, Manny is portrayed by Connor and Owen Fielding in the first three films, Wyatt and Dylan Walters in The Long Haul, and Gracen Newton in the animated films.\nGreg's extended family\nGramma\n”Gramma” is Susan's mother and Greg's maternal grandmother. Gramma is known to drive a car like the Heffleys'. Greg often calls her to his home as a way to avoid his dad's scoldings, since his father will not do this when she is around. Her favorite grandchild is Manny, although she denies and lies about this despite overwhelming evidence in the form of pictures on her refrigerator and many of Manny's drawings around her house. She also seems to have a passion for bingo and knitting. Gramma has a dog named Sweetie, who she overfeeds."]], "corpus": "wikipedia"}
+{"tstamp": 1722451064.4955, "task_type": "retrieval", "type": "chat", "model": "Alibaba-NLP/gte-Qwen2-7B-instruct", "gen_params": {}, "start": 1722451064.1256, "finish": 1722451064.4955, "ip": "", "conv_id": "3457bbe9f3594289adb53e9dacb1d8c8", "model_name": "Alibaba-NLP/gte-Qwen2-7B-instruct", "prompt": "who plays manny in diary of a wimpy kid", "output": [["who plays manny in diary of a wimpy kid", "List of Saw characters\n\nIn Saw VI, Erickson and Perez, whose death was faked for her protection, resume the investigation and probe Strahm's apparent link to John Kramer. As they uncover evidence that indicates Strahm was framed, their suspicion of Hoffman grows. Once their audio lab technician removes the distortion from the Seth Baxter tape, revealing Hoffman as John's accomplice, Hoffman slashes Erickson's jugular vein and stabs Perez to death. Erickson is still alive as Hoffman pours gasoline over him and sets the lab on fire to destroy the evidence.\nLuba Gibbs\nPortrayed by Meagan Good\nAppears in: Saw V, Saw VI (mentioned only)\nStatus: Deceased\nDied in: Saw V\nLuba Gibbs, a city planner, is a victim in the main game of Saw V, alongside four other people. After surviving the first room, Luba assaults Charles Salomon after he attacks Mallick Scott, leaving Charles to die when explosive devices in the second room detonate. In the third room, Brit Stevenson fatally stabs Luba in the neck, stating that she does not trust her, and she and Mallick use the corpse as an electrical conductor to open the door to the final room.\nIt is revealed that Luba played a role in an arson scam that killed eight people, an event that links all the victims of the game. She had accepted bribes from a sports team owned by her father, Richard Gibbs, to allow a stadium to be built on the site of a building Mallick burned down.\nMallick\nPortrayed by Greg Bryk\nAppears in: Saw V, Saw VI (mentioned only), Saw 3D"]], "corpus": "wikipedia"}