Datasets:

Modalities:
Text
Formats:
parquet
Languages:
English
ArXiv:
Libraries:
Datasets
pandas
License:
Select-Stack / README.md
Alignment-Lab-AI's picture
Upload README.md with huggingface_hub
de0f9de verified
metadata
license: cc-by-4.0
language:
  - en
tags:
  - synthetic
  - code
  - orca
  - Alignment-Lab-AI
  - dpo
  - reinforcement-learning
  - RLHF
  - sharegpt
  - chatml
  - text-generation
  - instruction
pretty_name: Buzz-V1.2
size_categories:
  - 1B<n<10B

image/png

Buzz: Advancing Efficiency through Iterative Fine-Tuning

Introduction

Buzz, a highly curated pretraining scale assistant dataset, unifying RL and SFT, developed in collaboration with Hive Digital Technologies.

The Buzz model, Dataset, and Code are to be released to build a toolkit that aims to demonstrate the potential for reuse and optimization of existing pretrained language models to continuously refine the heights of performance that can be achieved with optimal use of FlOps. Alongside Buzz-8b-Large, we release

Features

Buzz contains 435 high quality instruction following and conversational datasets, deduplicated, with formatting built to maintain and extend compatibility between training types and the current local ecosystem.

the datasets within are comprised of various high quality instruction following, conversational, storytelling, and coding datasets, as well as over 5 million new rows of data, in addition to several million reaugmented rows of data, comprising the totality of the learned techniques since our release of Open-Orca cumulatively making up roughly 85 million turns of conversations, in a mix of single and multiturn rows.

Iterative Fine-Tuning Methodology

Our research builds upon the concepts introduced in several key papers, including:

By combining high quality data, iterative fine-tuning with carefully selected "grounding" distributions from previous epochs, we have developed a cost-effective approach that pushes the boundaries of model reuse and optimization.

notably, we observe that training on a single epoch of high quality in domain data can still achieve remarkably low loss values before overfitting.

Data structure and formatting

buzz should be out of the box compatible with the sharegpt type in Axolotl and lmsys' FastChat during training it containsthe following structure [EDIT]: filtered for bias examples!

{
  "source": "string containing the source dataset",
  "stack": "chosen/rejected for RL techniques",
  "question_index": optional row, only contained in DPO specific dataset to match dpo pairs - int64
  "conversations": [
    {
      "from": "system",
      "value": "an initial system prompt or user query, may or may not be present depending on the row"
    },
    {
      "from": "human or system",
      "value": "an initial 'human' query"
    },
    {
      "from": "gpt",
      "value": "a response to the previous turn, may be followed by additional human/gpt alternations"
    }
  ]
}

Conclusion

We intend to focus on updating and improving the dataset, tools to construct it, and other surrounding open sourced infrastructure. Our next effort will focus on context and implementing the research currently being conducted by Wing-Lian, the lead developer of the Axolotl training framework that underpins these experiments. We encourage the community to explore Wing-Lian's work, such as the Llama-3-8b-64k-PoSE and llama-3-8b-256k-PoSE models, which showcase the potential for further advancements in language modeling.

Buzz hopes to be a proof of concept, and a toolkit to demonstrate and enable the community in the pursuit of efficient and effective locally run, personally owned, language models. Through collaboration with Hive Digital Technologies who have enabled us to perform this research, we have demonstrated the immense potential for model reuse and optimization. The Buzz models and dataset are open sourced with [////////].

Credits

to the many researchers who have open sourced their knowledge and tools to allow us to pursue this,

to Hive Digital Technologies for providing compute, advice, and meaningful research insight.

to Meta for developing the Llama models, and maintaining a philosophy of supporting open research and open source.

To wing et al. with Open Access AI Collective for developing axolotl, assisting with research, and generally being geniuses.

to Thomas Capelle et al. working on LLM_Surgery

as well as many, many others who are too numerous to name.

Dataset Sources

Sources in the source column which refer only to a model name are from Nectar, Lmsys 55k preference, and various user submitted distillation datasets inspired by Ontocord's dataset https://huggingface.co/datasets/laion/OIG


Muennighoff/tasky-commits
Muennighoff/natural-instructions
laion/OIG
knowrohit07/know_medical_dialogue_v2
knowrohit07/know-saraswati-cot
knowrohit07/know_medical_dialogues
H-D-T/Select-Stack [stackexchange dump, filtered for difficult, stem, technical stacks, only best rated responses kept for Buzz]
knowrohit07/know_sql
knowrohit07/ArithmeLogic
knowrohit07/know_logic
knowrohit07/know_cot
knowrohit07/GPTscience_maths_csml
AlignmentLab-AI/datasci-python
teknium/OpenHermes-2.5
teknium/openhermes
THUDM/AgentInstruct
Vezora/Tested-22k-Python-Alpaca
HuggingFaceH4/no_robots
migtissera/Synthia-v1.3
migtissera/Tess-Coder-v1.0
migtissera/Quanta
AlekseyKorshuk/gpteacher-role-play-chatml
nampdn-ai/devdocs.io
TIGER-Lab/MathInstruct
TIGER-Lab/Homework-step-Comparator
TIGER-Lab/Homework-step-Extractor
TIGER-Lab/MATH-plus
TIGER-Lab/MetricInstruct
TIGER-Lab/RFT-GSM-28K
Muennighoff/P3
PygmalionAI/Sharegpt-soda
taesiri/arxiv_summary
kaist-ai/Cot-Collection
microsoft/orca-math-word-problems-200k
Open-Orca/FLAN [uploaded to hf, but created by Google]
allenai/WildChat
chargoddard/rpguild
medalpaca/medical_meadow_medqa
medalpaca/medical_meadow_mediqa
medalpaca/medical_meadow_wikidoc
medalpaca/medical_meadow_health_advice
medalpaca/medical_meadow_medical_flashcards
medalpaca/medical_meadow_cord19
medalpaca/medical_meadow_wikidoc_patient_information
medalpaca/medical_meadow_mmmlu
medalpaca/medical_meadow_pubmed_causal
medalpaca/medical_meadow_usmle_self_assessment
keivalya/MedQuad-MedicalQnADataset
IndianaUniversityDatasetsModels/MIMIC-medical-report
Annielytics/DoctorsNotes
lavita/ChatDoctor-HealthCareMagic-100k
lavita/ChatDoctor-iCliniq
mahfoos/Patient-Doctor-Conversation
Amod/mental_health_counseling_conversations
alexandreteles/mental-health-conversational-data
heliosbrahma/mental_health_chatbot_dataset
akemiH/NoteChat
openchat/cogstack-opengpt-sharegpt
h2oai/openassistant_oasst1_h2ogpt_llama2_chat
openchat/openchat_sharegpt4_dataset
garage-bAInd/Open-Platypus
lmsys/lmsys-arena-human-preference-55k
CarperAI/openai_summarize_tldr
Locutusque/OpenCerebrum-dpo
Locutusque/OpenCerebrum-2.0-SFT
internlm/Agent-FLAN
Clinton/Text-to-sql-v1
meta-math/MetaMath_DPO_FewShot
berkeley-nest/Nectar
bigcode/commitpackft
ybisk/piqa
meta-math/MetaMathQA
SAGI-1/reasoningData_200k
Total Turns: 81,167,793
Total Rows: 31,249,070

| # | Source | Percentage | Turns | Rows |
| - | ------ | ---------- | ----- | ---- |
| 1 | Flan: English | 20.33% | 16,500,966 | 8,250,483 |
| 2 | Flan: Non English | 18.47% | 14,995,714 | 7,497,857 |
| 3 | sodey | 9.71% | 7,883,090 | 917,016 |
| 4 | OIG soda_dialog | 7.93% | 6,436,873 | 1,191,582 |
| 5 | various 'orca' style reaugmentations | 3.62% | 2,934,794 | 878,547 |
| 6 | Select Stack | 3.59% | 2,911,650 | 1,455,825 |
| 7 | sft-distil | 3.59% | 2,911,634 | 1,455,817 |
| 8 | OIG abstract_infill | 3.52% | 2,858,795 | 232,188 |
| 9 | medical_meadow_cord19 | 2.79% | 2,265,654 | 755,218 |
| 10 | EverythingIsAllYouNeed0.25 | 2.39% | 1,941,198 | 970,599 |
| 11 | MATH-plus | 2.04% | 1,658,976 | 829,488 |
| 12 | OIG unifiedskg_instructions | 1.14% | 927,267 | 214,793 |
| 13 | OIG nq | 1.03% | 836,194 | 307,373 |
| 14 | MetaMath_DPO_FewShot | 0.97% | 787,998 | 393,999 |
| 15 | MetaMathQA | 0.95% | 770,166 | 385,083 |
| 16 | OpenHermes-2.5 | 0.95% | 769,503 | 367,336 |
| 17 | wildchat-sharegpt | 0.94% | 764,896 | 123,596 |
| 18 | hotdog-gpt | 0.73% | 591,467 | 190,543 |
| 19 | Tess-Coder-v1.0 | 0.72% | 585,038 | 117,008 |
| 20 | OIG canadian_parliament | 0.72% | 581,708 | 290,854 |
| 21 | openhermes | 0.66% | 536,782 | 240,894 |
| 22 | Text-to-sql-v1 | 0.65% | 524,412 | 262,206 |
| 23 | MathInstruct | 0.61% | 491,666 | 245,833 |
| 24 | OIG unnatural_instructions | 0.59% | 476,087 | 238,035 |
| 25 | OIG openai_summarize_tldr | 0.58% | 466,796 | 233,398 |
| 26 | OIG chip2 | 0.52% | 420,564 | 210,282 |
| 27 | orcamath-sharegpt | 0.49% | 399,414 | 199,707 |
| 28 | OIG xp3_sample | 0.46% | 376,276 | 188,138 |
| 29 | anthropic-hh-nectar | 0.43% | 346,892 | 73,687 |
| 30 | reasoningData_200k | 0.41% | 334,004 | 167,002 |
| 31 | OpenCodeInterpreterData | 0.41% | 331,715 | 36,836 |
| 32 | Synthia-v1.3 | 0.41% | 329,115 | 118,841 |
| 33 | yaml | 0.40% | 321,755 | 110,572 |
| 34 | GPTscience_maths_csml | 0.37% | 297,310 | 148,655 |
| 35 | OIG squad_v2 | 0.32% | 260,638 | 19,585 |
| 36 | OIG squad_v2_more_neg | 0.32% | 259,902 | 13,946 |
| 37 | OIG rallio_safety_and_prosocial | 0.31% | 250,534 | 125,235 |
| 38 | MIMIC-medical-report | 0.31% | 250,362 | 83,454 |
| 39 | OIG mathqa_flanv2_kojma_cot | 0.30% | 243,420 | 107,564 |
| 40 | openai_summarize_tldr | 0.29% | 233,336 | 116,668 |
| 41 | OIG sqlv2 | 0.28% | 224,270 | 24,546 |
| 42 | ruby | 0.24% | 197,135 | 68,086 |
| 43 | RPGuild-sharegpt-filtered | 0.24% | 196,309 | 27,053 |
| 44 | OIG multi_news | 0.22% | 179,888 | 89,944 |
| 45 | markdown | 0.22% | 174,608 | 61,260 |
| 46 | javascript | 0.19% | 156,109 | 52,289 |
| 47 | python | 0.19% | 151,866 | 55,045 |
| 48 | know_sql | 0.18% | 148,368 | 49,456 |
| 49 | text | 0.16% | 133,033 | 44,926 |
| 50 | saraswati_stem_formatted | 0.15% | 119,750 | 59,875 |
| 51 | know_saraswati_cot_formatted | 0.14% | 116,408 | 58,204 |
| 52 | json | 0.14% | 115,682 | 39,124 |
| 53 | OIG hc3_human | 0.14% | 112,112 | 56,056 |
| 54 | medical_meadow_medical_flashcards | 0.12% | 100,575 | 33,527 |
| 55 | lmsys-chat-1m-nectar | 0.11% | 86,770 | 43,385 |
| 56 | shell | 0.11% | 85,901 | 30,327 |
| 57 | cogstack-opengpt-sharegpt | 0.10% | 81,667 | 31,532 |
| 58 | Quanta | 0.10% | 78,096 | 26,032 |
| 59 | php | 0.08% | 68,256 | 24,302 |
| 60 | know_logic | 0.08% | 68,208 | 34,104 |
| 61 | html | 0.07% | 57,384 | 19,750 |
| 62 | OIG plot_screenplay_books_dialog | 0.07% | 54,981 | 7,924 |
| 63 | java | 0.07% | 53,574 | 20,150 |
| 64 | Open-Platypus | 0.07% | 53,373 | 24,109 |
| 65 | RFT-GSM-28K | 0.06% | 51,092 | 25,546 |
| 66 | OIG conv_finqa | 0.06% | 50,472 | 9,102 |
| 67 | sharegpt-nectar | 0.06% | 49,896 | 24,948 |
| 68 | OIG cuad | 0.05% | 41,390 | 510 |
| 69 | OpenCerebrum-dpo | 0.05% | 40,534 | 17,013 |
| 70 | Tested-22k-Python-Alpaca | 0.04% | 36,224 | 18,112 |
| 71 | OIG sqlv1 | 0.04% | 34,174 | 17,087 |
| 72 | MedQuad-MedicalQnADataset | 0.04% | 32,718 | 16,359 |
| 73 | piqa | 0.04% | 32,212 | 16,106 |
| 74 | html+erb | 0.04% | 31,679 | 10,708 |
| 75 | OIG image_prompts_instructions | 0.04% | 30,932 | 15,466 |
| 76 | medical_meadow_medqa | 0.04% | 30,534 | 10,178 |
| 77 | ini | 0.04% | 30,461 | 10,396 |
| 78 | medical_meadow_wikidoc | 0.04% | 29,998 | 10,000 |
| 79 | c# | 0.03% | 26,796 | 9,220 |
| 80 | xml | 0.03% | 26,054 | 9,085 |
| 81 | medical_meadow_health_advice | 0.03% | 25,995 | 8,665 |
| 82 | OIG poetry_2_song | 0.03% | 25,462 | 12,731 |
| 83 | flan_v2_niv2-nectar | 0.03% | 24,036 | 12,018 |
| 84 | c | 0.03% | 23,203 | 8,250 |
| 85 | scss | 0.02% | 20,156 | 6,730 |
| 86 | evol_instruct-nectar | 0.02% | 19,930 | 9,965 |
| 87 | ultrachat-nectar | 0.02% | 19,822 | 9,911 |
| 88 | restructuredtext | 0.02% | 18,901 | 6,481 |
| 89 | OpenCerebrum-2.0-SFT | 0.02% | 18,793 | 4,382 |
| 90 | gpteacher-role-play-chatml | 0.02% | 18,222 | 9,111 |
| 91 | OIG grade_school_math_instructions | 0.02% | 17,584 | 8,792 |
| 92 | OIG essays | 0.02% | 17,581 | 2,064 |
| 93 | medical_meadow_wikidoc_patient_information | 0.02% | 17,550 | 5,850 |
| 94 | typescript | 0.02% | 16,912 | 5,816 |
| 95 | coffeescript | 0.02% | 15,836 | 5,403 |
| 96 | go | 0.02% | 14,814 | 4,939 |
| 97 | css | 0.02% | 14,654 | 4,979 |
| 98 | scala | 0.02% | 14,184 | 4,988 |
| 99 | c++ | 0.02% | 13,391 | 4,838 |
| 100 | swift | 0.02% | 13,361 | 4,724 |
| 101 | haml | 0.02% | 12,787 | 4,285 |
| 102 | know_medical_dialogue_v2 | 0.02% | 12,580 | 6,290 |
| 103 | medical_meadow_mmmlu | 0.01% | 11,058 | 3,686 |
| 104 | toml | 0.01% | 10,189 | 3,411 |
| 105 | riddler_formatted | 0.01% | 8,396 | 4,198 |
| 106 | rust | 0.01% | 8,276 | 2,977 |
| 107 | gpt-4-1106-preview | 0.01% | 8,106 | 4,053 |
| 108 | extractor-00000-of-00001 | 0.01% | 7,678 | 3,839 |
| 109 | clojure | 0.01% | 6,974 | 2,394 |
| 110 | Patient-Doctor-Conversation | 0.01% | 6,488 | 3,244 |
| 111 | jsx | 0.01% | 6,390 | 2,176 |
| 112 | kotlin | 0.01% | 6,206 | 2,193 |
| 113 | medical_meadow_mediqa | 0.01% | 6,162 | 2,054 |
| 114 | flan_v2_cot-nectar | 0.01% | 6,000 | 3,000 |
| 115 | perl | 0.01% | 5,837 | 2,217 |
| 116 | mental_health_counseling_conversations | 0.01% | 5,496 | 2,748 |
| 117 | sql | 0.01% | 5,172 | 1,998 |
| 118 | gpt-4-0613 | 0.01% | 4,862 | 2,431 |
| 119 | gpt-3.5-turbo-0613 | 0.01% | 4,742 | 2,371 |
| 120 | nix | 0.01% | 4,704 | 1,582 |
| 121 | false_qa-nectar | 0.01% | 4,640 | 2,320 |
| 122 | unknown | 0.01% | 4,576 | 1,571 |
| 123 | twig | 0.01% | 4,557 | 1,563 |
| 124 | handlebars | 0.01% | 4,176 | 1,405 |
| 125 | haskell | 0.01% | 4,095 | 1,365 |
| 126 | batchfile | 0.00% | 4,003 | 1,409 |
| 127 | less | 0.00% | 3,973 | 1,331 |
| 128 | datasci-python | 0.00% | 3,966 | 1,983 |
| 129 | gpt-4-0314 | 0.00% | 3,962 | 1,981 |
| 130 | groovy | 0.00% | 3,952 | 1,470 |
| 131 | flan_v2_p3-nectar | 0.00% | 3,858 | 1,929 |
| 132 | OIG poetry_instructions | 0.00% | 3,508 | 1,754 |
| 133 | claude-1 | 0.00% | 3,476 | 1,738 |
| 134 | bitbake | 0.00% | 3,419 | 1,264 |
| 135 | claude-2.1 | 0.00% | 3,400 | 1,700 |
| 136 | jade | 0.00% | 3,282 | 1,101 |
| 137 | elixir | 0.00% | 3,281 | 1,138 |
| 138 | claude-instant-1 | 0.00% | 3,262 | 1,631 |
| 139 | viml | 0.00% | 3,150 | 1,050 |
| 140 | slim | 0.00% | 3,111 | 1,043 |
| 141 | emacs-lisp | 0.00% | 2,884 | 983 |
| 142 | cmake | 0.00% | 2,876 | 959 |
| 143 | makefile | 0.00% | 2,721 | 933 |
| 144 | powershell | 0.00% | 2,690 | 970 |
| 145 | cucumber | 0.00% | 2,632 | 951 |
| 146 | llama-2-70b-chat | 0.00% | 2,546 | 1,273 |
| 147 | vicuna-33b | 0.00% | 2,526 | 1,263 |
| 148 | lua | 0.00% | 2,517 | 904 |
| 149 | vicuna-13b | 0.00% | 2,482 | 1,241 |
| 150 | mistral-medium | 0.00% | 2,438 | 1,219 |
| 151 | mixtral-8x7b-instruct-v0.1 | 0.00% | 2,390 | 1,195 |
| 152 | fish | 0.00% | 2,275 | 802 |
| 153 | common-lisp | 0.00% | 2,234 | 761 |
| 154 | smarty | 0.00% | 2,127 | 723 |
| 155 | dart | 0.00% | 2,092 | 750 |
| 156 | sass | 0.00% | 2,060 | 692 |
| 157 | llvm | 0.00% | 1,991 | 778 |
| 158 | claude-2.0 | 0.00% | 1,902 | 951 |
| 159 | saltstack | 0.00% | 1,818 | 617 |
| 160 | gpt-3.5-turbo-1106 | 0.00% | 1,724 | 862 |
| 161 | llama-2-13b-chat | 0.00% | 1,712 | 856 |
| 162 | vue | 0.00% | 1,705 | 583 |
| 163 | diff | 0.00% | 1,564 | 656 |
| 164 | asciidoc | 0.00% | 1,523 | 508 |
| 165 | truthful_qa-nectar | 0.00% | 1,488 | 744 |
| 166 | zephyr-7b-beta | 0.00% | 1,428 | 714 |
| 167 | gpt-3.5-turbo-0314 | 0.00% | 1,418 | 709 |
| 168 | stylus | 0.00% | 1,414 | 476 |
| 169 | freemarker | 0.00% | 1,322 | 508 |
| 170 | erlang | 0.00% | 1,286 | 468 |
| 171 | palm-2 | 0.00% | 1,270 | 635 |
| 172 | hcl | 0.00% | 1,206 | 420 |
| 173 | gpt-4-0125-preview | 0.00% | 1,192 | 596 |
| 174 | html+django | 0.00% | 1,174 | 394 |
| 175 | wizardlm-70b | 0.00% | 1,170 | 585 |
| 176 | wizardlm-13b | 0.00% | 1,140 | 570 |
| 177 | koala-13b | 0.00% | 1,120 | 560 |
| 178 | llama-2-7b-chat | 0.00% | 1,106 | 553 |
| 179 | yi-34b-chat | 0.00% | 1,062 | 531 |
| 180 | qml | 0.00% | 1,053 | 362 |
| 181 | csv | 0.00% | 1,010 | 368 |
| 182 | gemini-pro-dev-api | 0.00% | 954 | 477 |
| 183 | know_medical_dialogues | 0.00% | 952 | 476 |
| 184 | openchat-3.5 | 0.00% | 944 | 472 |
| 185 | flan_v2_flan2021-nectar | 0.00% | 928 | 464 |
| 186 | ocaml | 0.00% | 912 | 327 |
| 187 | gemini-pro | 0.00% | 906 | 453 |
| 188 | pplx-70b-online | 0.00% | 896 | 448 |
| 189 | vicuna-7b | 0.00% | 894 | 447 |
| 190 | codellama-34b-instruct | 0.00% | 852 | 426 |
| 191 | tex | 0.00% | 839 | 297 |
| 192 | starling-lm-7b-alpha | 0.00% | 800 | 400 |
| 193 | rdoc | 0.00% | 795 | 269 |
| 194 | mistral-7b-instruct | 0.00% | 774 | 387 |
| 195 | elm | 0.00% | 772 | 265 |
| 196 | tulu-2-dpo-70b | 0.00% | 756 | 378 |
| 197 | f# | 0.00% | 743 | 251 |
| 198 | alpaca-13b | 0.00% | 710 | 355 |
| 199 | smalltalk | 0.00% | 706 | 284 |
| 200 | oasst-pythia-12b | 0.00% | 684 | 342 |
| 201 | pplx-7b-online | 0.00% | 656 | 328 |
| 202 | ada | 0.00% | 650 | 261 |
| 203 | scheme | 0.00% | 598 | 212 |
| 204 | openhermes-2.5-mistral-7b | 0.00% | 560 | 280 |
| 205 | qwen-14b-chat | 0.00% | 550 | 275 |
| 206 | arduino | 0.00% | 544 | 224 |
| 207 | crystal | 0.00% | 536 | 182 |
| 208 | RWKV-4-Raven-14B | 0.00% | 530 | 265 |
| 209 | gpt-3.5-turbo-0125 | 0.00% | 528 | 264 |
| 210 | gas | 0.00% | 502 | 192 |
| 211 | desktop | 0.00% | 500 | 174 |
| 212 | protocol-buffer | 0.00% | 500 | 180 |
| 213 | julia | 0.00% | 494 | 180 |
| 214 | guanaco-33b | 0.00% | 492 | 246 |
| 215 | haxe | 0.00% | 488 | 173 |
| 216 | groff | 0.00% | 485 | 188 |
| 217 | solar-10.7b-instruct-v1.0 | 0.00% | 484 | 242 |
| 218 | mako | 0.00% | 480 | 166 |
| 219 | glsl | 0.00% | 471 | 157 |
| 220 | java-server-pages | 0.00% | 463 | 163 |
| 221 | chatglm-6b | 0.00% | 432 | 216 |
| 222 | html+php | 0.00% | 432 | 146 |
| 223 | qwen1.5-72b-chat | 0.00% | 426 | 213 |
| 224 | mpt-7b-chat | 0.00% | 426 | 213 |
| 225 | svg | 0.00% | 425 | 166 |
| 226 | mpt-30b-chat | 0.00% | 414 | 207 |
| 227 | stripedhyena-nous-7b | 0.00% | 412 | 206 |
| 228 | html+eex | 0.00% | 405 | 135 |
| 229 | openassistant_oasst1_h2ogpt_llama2_chat | 0.00% | 404 | 202 |
| 230 | qmake | 0.00% | 401 | 135 |
| 231 | fastchat-t5-3b | 0.00% | 388 | 194 |
| 232 | org | 0.00% | 383 | 136 |
| 233 | deepseek-llm-67b-chat | 0.00% | 378 | 189 |
| 234 | llama2-70b-steerlm-chat | 0.00% | 358 | 179 |
| 235 | rhtml | 0.00% | 356 | 124 |
| 236 | cython | 0.00% | 322 | 115 |
| 237 | racket | 0.00% | 321 | 116 |
| 238 | perl6 | 0.00% | 317 | 116 |
| 239 | chatglm3-6b | 0.00% | 314 | 157 |
| 240 | r | 0.00% | 312 | 119 |
| 241 | factor | 0.00% | 287 | 99 |
| 242 | unity3d-asset | 0.00% | 282 | 101 |
| 243 | m4 | 0.00% | 279 | 99 |
| 244 | tcl | 0.00% | 267 | 98 |
| 245 | stablelm-tuned-alpha-7b | 0.00% | 264 | 132 |
| 246 | assembly | 0.00% | 260 | 104 |
| 247 | xslt | 0.00% | 251 | 96 |
| 248 | dolly-v2-12b | 0.00% | 248 | 124 |
| 249 | mind2web-00000-of-00001-fc25d47330eea0fc | 0.00% | 242 | 121 |
| 250 | objective-c++ | 0.00% | 238 | 84 |
| 251 | zephyr-7b-alpha | 0.00% | 236 | 118 |
| 252 | purescript | 0.00% | 225 | 80 |
| 253 | robotframework | 0.00% | 216 | 84 |
| 254 | nous-hermes-2-mixtral-8x7b-dpo | 0.00% | 212 | 106 |
| 255 | standard-ml | 0.00% | 192 | 71 |
| 256 | dolphin-2.2.1-mistral-7b | 0.00% | 190 | 95 |
| 257 | fortran | 0.00% | 187 | 70 |
| 258 | gpt4all-13b-snoozy | 0.00% | 186 | 93 |
| 259 | livescript | 0.00% | 178 | 62 |
| 260 | llama-13b | 0.00% | 176 | 88 |
| 261 | textile | 0.00% | 173 | 60 |
| 262 | nimrod | 0.00% | 173 | 66 |
| 263 | falcon-180b-chat | 0.00% | 156 | 78 |
| 264 | xtend | 0.00% | 155 | 55 |
| 265 | gettext-catalog | 0.00% | 147 | 65 |
| 266 | ceylon | 0.00% | 146 | 49 |
| 267 | chatglm2-6b | 0.00% | 146 | 73 |
| 268 | vala | 0.00% | 143 | 50 |
| 269 | edn | 0.00% | 138 | 47 |
| 270 | awk | 0.00% | 133 | 52 |
| 271 | actionscript | 0.00% | 132 | 48 |
| 272 | visual-basic | 0.00% | 131 | 47 |
| 273 | pod | 0.00% | 130 | 48 |
| 274 | sqf | 0.00% | 127 | 43 |
| 275 | openchat-3.5-0106 | 0.00% | 126 | 63 |
| 276 | dockerfile | 0.00% | 115 | 39 |
| 277 | linker-script | 0.00% | 108 | 36 |
| 278 | idris | 0.00% | 107 | 37 |
| 279 | qwen1.5-7b-chat | 0.00% | 102 | 51 |
| 280 | solidity | 0.00% | 102 | 36 |
| 281 | systemverilog | 0.00% | 101 | 35 |
| 282 | json5 | 0.00% | 98 | 33 |
| 283 | jupyter-notebook | 0.00% | 98 | 47 |
| 284 | processing | 0.00% | 96 | 35 |
| 285 | mediawiki | 0.00% | 94 | 33 |
| 286 | rouge | 0.00% | 92 | 41 |
| 287 | xquery | 0.00% | 89 | 38 |
| 288 | graphviz-dot | 0.00% | 84 | 32 |
| 289 | liquid | 0.00% | 83 | 29 |
| 290 | thrift | 0.00% | 77 | 28 |
| 291 | groovy-server-pages | 0.00% | 75 | 25 |
| 292 | pan | 0.00% | 69 | 23 |
| 293 | qwen1.5-4b-chat | 0.00% | 68 | 34 |
| 294 | api-blueprint | 0.00% | 67 | 23 |
| 295 | opencl | 0.00% | 66 | 23 |
| 296 | asp | 0.00% | 64 | 22 |
| 297 | cuda | 0.00% | 64 | 25 |
| 298 | logtalk | 0.00% | 63 | 21 |
| 299 | pascal | 0.00% | 62 | 24 |
| 300 | chapel | 0.00% | 60 | 20 |
| 301 | sparql | 0.00% | 60 | 23 |
| 302 | coldfusion-cfc | 0.00% | 58 | 20 |
| 303 | applescript | 0.00% | 57 | 19 |
| 304 | parrot-internal-representation | 0.00% | 56 | 20 |
| 305 | logos | 0.00% | 55 | 19 |
| 306 | mistral-7b-instruct-v0.2 | 0.00% | 54 | 27 |
| 307 | literate-coffeescript | 0.00% | 54 | 19 |
| 308 | digital-command-language | 0.00% | 53 | 19 |
| 309 | turtle | 0.00% | 52 | 21 |
| 310 | ioke | 0.00% | 52 | 19 |
| 311 | pony | 0.00% | 48 | 16 |
| 312 | openscad | 0.00% | 48 | 21 |
| 313 | vcl | 0.00% | 48 | 17 |
| 314 | graphql | 0.00% | 48 | 17 |
| 315 | dm | 0.00% | 46 | 16 |
| 316 | gnuplot | 0.00% | 45 | 17 |
| 317 | ooc | 0.00% | 43 | 15 |
| 318 | inno-setup | 0.00% | 43 | 15 |
| 319 | gentoo-ebuild | 0.00% | 42 | 15 |
| 320 | modelica | 0.00% | 42 | 15 |
| 321 | antlr | 0.00% | 42 | 15 |
| 322 | nsis | 0.00% | 41 | 15 |
| 323 | http | 0.00% | 38 | 18 |
| 324 | ninja | 0.00% | 36 | 14 |
| 325 | mirah | 0.00% | 36 | 15 |
| 326 | autohotkey | 0.00% | 36 | 15 |
| 327 | augeas | 0.00% | 35 | 13 |
| 328 | hy | 0.00% | 32 | 11 |
| 329 | hlsl | 0.00% | 32 | 11 |
| 330 | stata | 0.00% | 30 | 10 |
| 331 | piglatin | 0.00% | 30 | 11 |
| 332 | capn-proto | 0.00% | 28 | 10 |
| 333 | moonscript | 0.00% | 28 | 10 |
| 334 | coldfusion | 0.00% | 27 | 9 |
| 335 | volt | 0.00% | 27 | 9 |
| 336 | tcsh | 0.00% | 25 | 10 |
| 337 | jasmin | 0.00% | 25 | 9 |
| 338 | raml | 0.00% | 25 | 9 |
| 339 | postscript | 0.00% | 25 | 9 |
| 340 | scilab | 0.00% | 25 | 10 |
| 341 | fancy | 0.00% | 24 | 8 |
| 342 | gdscript | 0.00% | 24 | 8 |
| 343 | latte | 0.00% | 21 | 7 |
| 344 | aspectj | 0.00% | 20 | 7 |
| 345 | apl | 0.00% | 20 | 7 |
| 346 | xs | 0.00% | 20 | 7 |
| 347 | g-code | 0.00% | 19 | 7 |
| 348 | nesc | 0.00% | 19 | 7 |
| 349 | emberscript | 0.00% | 19 | 7 |
| 350 | ston | 0.00% | 18 | 6 |
| 351 | oz | 0.00% | 18 | 7 |
| 352 | literate-haskell | 0.00% | 18 | 7 |
| 353 | yang | 0.00% | 17 | 6 |
| 354 | webidl | 0.00% | 17 | 6 |
| 355 | jsonld | 0.00% | 16 | 6 |
| 356 | jsoniq | 0.00% | 16 | 6 |
| 357 | lfe | 0.00% | 16 | 6 |
| 358 | pike | 0.00% | 15 | 6 |
| 359 | purebasic | 0.00% | 15 | 5 |
| 360 | pov-ray-sdl | 0.00% | 14 | 5 |
| 361 | lilypond | 0.00% | 13 | 6 |
| 362 | agda | 0.00% | 13 | 5 |
| 363 | vhdl | 0.00% | 13 | 5 |
| 364 | zephir | 0.00% | 12 | 4 |
| 365 | mupad | 0.00% | 12 | 4 |
| 366 | mask | 0.00% | 12 | 4 |
| 367 | krl | 0.00% | 12 | 4 |
| 368 | zig | 0.00% | 12 | 4 |
| 369 | metal | 0.00% | 12 | 4 |
| 370 | slash | 0.00% | 12 | 4 |
| 371 | io | 0.00% | 12 | 4 |
| 372 | monkey | 0.00% | 12 | 4 |
| 373 | ragel-in-ruby-host | 0.00% | 10 | 4 |
| 374 | xbase | 0.00% | 9 | 3 |
| 375 | eagle | 0.00% | 9 | 4 |
| 376 | squirrel | 0.00% | 9 | 4 |
| 377 | genshi | 0.00% | 9 | 3 |
| 378 | cartocss | 0.00% | 9 | 3 |
| 379 | xproc | 0.00% | 9 | 3 |
| 380 | wisp | 0.00% | 9 | 3 |
| 381 | urweb | 0.00% | 9 | 3 |
| 382 | yacc | 0.00% | 8 | 3 |
| 383 | smt | 0.00% | 8 | 3 |
| 384 | medical_meadow_pubmed_causal | 0.00% | 8 | 4 |
| 385 | lsl | 0.00% | 8 | 3 |
| 386 | ats | 0.00% | 8 | 3 |
| 387 | flux | 0.00% | 8 | 3 |
| 388 | bro | 0.00% | 8 | 3 |
| 389 | ecl | 0.00% | 8 | 4 |
| 390 | nit | 0.00% | 7 | 3 |
| 391 | pawn | 0.00% | 7 | 3 |
| 392 | rebol | 0.00% | 7 | 3 |
| 393 | mtml | 0.00% | 6 | 2 |
| 394 | eiffel | 0.00% | 6 | 2 |
| 395 | c2hs-haskell | 0.00% | 6 | 2 |
| 396 | uno | 0.00% | 6 | 2 |
| 397 | lean | 0.00% | 6 | 3 |
| 398 | sourcepawn | 0.00% | 6 | 3 |
| 399 | brainfuck | 0.00% | 6 | 2 |
| 400 | renpy | 0.00% | 6 | 2 |
| 401 | boo | 0.00% | 6 | 2 |
| 402 | arc | 0.00% | 6 | 2 |
| 403 | dylan | 0.00% | 6 | 2 |
| 404 | bluespec | 0.00% | 6 | 2 |
| 405 | creole | 0.00% | 6 | 2 |
| 406 | forth | 0.00% | 6 | 2 |
| 407 | apacheconf | 0.00% | 6 | 2 |
| 408 | isabelle | 0.00% | 5 | 2 |
| 409 | maple | 0.00% | 5 | 2 |
| 410 | dns-zone | 0.00% | 5 | 2 |
| 411 | nginx | 0.00% | 5 | 2 |
| 412 | inform-7 | 0.00% | 5 | 2 |
| 413 | csound | 0.00% | 4 | 2 |
| 414 | nu | 0.00% | 4 | 2 |
| 415 | supercollider | 0.00% | 4 | 2 |
| 416 | parrot-assembly | 0.00% | 3 | 1 |
| 417 | literate-agda | 0.00% | 3 | 1 |
| 418 | igor-pro | 0.00% | 3 | 1 |
| 419 | unrealscript | 0.00% | 3 | 1 |
| 420 | pure-data | 0.00% | 3 | 1 |
| 421 | blitzmax | 0.00% | 3 | 1 |
| 422 | sage | 0.00% | 3 | 1 |
| 423 | module-management-system | 0.00% | 3 | 1 |
| 424 | scaml | 0.00% | 3 | 1 |
| 425 | netlinx | 0.00% | 3 | 1 |
| 426 | abap | 0.00% | 3 | 1 |
| 427 | xpages | 0.00% | 3 | 1 |
| 428 | propeller-spin | 0.00% | 3 | 1 |
| 429 | sas | 0.00% | 3 | 1 |
| 430 | ArithmeLogic | 0.00% | 2 | 1 |
| 431 | clean | 0.00% | 2 | 1 |
| 432 | harbour | 0.00% | 2 | 1 |
| 433 | mathematica | 0.00% | 2 | 1 |
| 434 | jflex | 0.00% | 2 | 1 |
| 435 | red | 0.00% | 2 | 1 |

Citations

@misc{ibrahim2024simple,
      title={Simple and Scalable Strategies to Continually Pre-train Large Language Models}, 
      author={Adam Ibrahim and Benjamin Thérien and Kshitij Gupta and Mats L. Richter and Quentin Anthony and Timothée Lesort and Eugene Belilovsky and Irina Rish},
      year={2024},
      eprint={2403.08763},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{jain2023neftune,
      title={NEFTune: Noisy Embeddings Improve Instruction Finetuning}, 
      author={Neel Jain and Ping-yeh Chiang and Yuxin Wen and John Kirchenbauer and Hong-Min Chu and Gowthami Somepalli and Brian R. Bartoldson and Bhavya Kailkhura and Avi Schwarzschild and Aniruddha Saha and Micah Goldblum and Jonas Geiping and Tom Goldstein},
      year={2023},
      eprint={2310.05914},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{wang2020optimistic,
      title={An Optimistic Acceleration of AMSGrad for Nonconvex Optimization}, 
      author={Jun-Kun Wang and Xiaoyun Li and Belhal Karimi and Ping Li},
      year={2020},
      eprint={1903.01435},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{keskar2017improving,
      title={Improving Generalization Performance by Switching from Adam to SGD}, 
      author={Nitish Shirish Keskar and Richard Socher},
      year={2017},
      eprint={1712.07628},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}