Datasets:

H-D-T
/

Buzz-slice-3-10-V1.2

	---
	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](https://cdn-uploads.huggingface.co/production/uploads/6436279eaaef013d1af225c9/fWaQucBWfabfnMsAFN8hv.png)

	# Buzz: Advancing Efficiency through Iterative Fine-Tuning

	## Introduction

	- [Alignment Lab AI](https://AlignmentLab.ai) is pleased to introduce our latest research efforts with:

	Buzz, a highly curated pretraining scale assistant dataset, unifying RL and SFT, developed in collaboration with [Hive Digital Technologies](https://www.hivedigitaltechnologies.com/).

	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

	- [The Buzz Dataset](https://huggingface.co/datasets/H-D-T/Buzz)
	- [Buzz-2.5b-Small](https://huggingface.co/tempbuzz/Lab-AI/Buzz-3b-Small-v0.5)
	- [Buzz-5b-Medium](https://huggingface.co/tempbuzz/Lab-AI/Buzz-5B-Medium-v0.5)
	- [Buzz-8B-Large](https://huggingface.co/tempbuzz/Lab-AI/Buzz-8B-Large-v0.5)

	## 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](https://huggingface.co/datasets/Open-Orca/OpenOrca)
	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:

	- [Simple and Scalable Strategies to Continually Pre-train Large Language Models](https://arxiv.org/abs/2403.08763)
	- [NEFTune: Noisy Embeddings Improve Instruction Finetuning](https://arxiv.org/abs/2310.05914)
	- [An Optimistic Acceleration of AMSGrad for Nonconvex Optimization](https://arxiv.org/abs/1903.01435)
	- [Improving Generalization Performance by Switching from Adam to SGD](https://arxiv.org/abs/1712.07628)
	- [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://arxiv.org/abs/2306.02707v1)

	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](https://github.com/winglian), the lead developer of the [Axolotl](https://github.com/OpenAccess-AI-Collective/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](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE) and [llama-3-8b-256k-PoSE](https://huggingface.co/winglian/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](https://hivedigitaltechnologies.com/) 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](https://hivedigitaltechnologies.com/) for providing compute, advice, and meaningful research insight.

	to [Meta](https://llama.meta.com) 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](https://github.com/OpenAccess-AI-Collective) for developing [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), assisting with research, and generally being geniuses.

	to [Thomas Capelle](https://wandb.ai/capecape) et al. working on [LLM_Surgery](https://wandb.ai/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](https://huggingface.co/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}
	}
	```