add magpie dataset

README.md

@@ -30,21 +30,22 @@ The goal of general-purpose datasets is to transform base models into versatile
 
 | Dataset | # | Authors | Date | Notes |
 | ------------------------------------------------------------------------------------------------------------- | ----- | ---------------------------- | -------- | --------------------------------------------------------------------------------- |
-| 🆕 [Buzz](https://huggingface.co/datasets/H-D-T/Buzz) | 31.2M | Alignment Lab AI  | May 2024 | Huge collection of 435 datasets with data augmentation, deduplication, and other techniques. |
-| 🆕 [WebInstructSub](https://huggingface.co/datasets/chargoddard/WebInstructSub-prometheus) | 2.39M | Yue et al.  | May 2024 | Instructions created by retrieving document from Common Crawl, extracting QA pairs, and refining them. See the [MAmmoTH2 paper](https://arxiv.org/abs/2405.03548) (this is a subset). |
-| [Bagel](https://github.com/jondurbin/bagel) | >2M? | Jon Durbin  | Jan 2024 | Collection of datasets decontaminated with cosine similarity. |
-| [Hercules v4.5](https://huggingface.co/datasets/Locutusque/hercules-v4.5) | 1.72M | Sebastian Gabarain  | Apr 2024 | Large-scale general-purpose dataset with math, code, RP, etc. See [v4](https://huggingface.co/datasets/Locutusque/hercules-v4.0) for the list of datasets. |
+| [Buzz](https://huggingface.co/datasets/H-D-T/Buzz) | 31.2M | Alignment Lab AI | May 2024 | Huge collection of 435 datasets with data augmentation, deduplication, and other techniques. |
+| [WebInstructSub](https://huggingface.co/datasets/chargoddard/WebInstructSub-prometheus) | 2.39M | Yue et al. | May 2024 | Instructions created by retrieving document from Common Crawl, extracting QA pairs, and refining them. See the [MAmmoTH2 paper](https://arxiv.org/abs/2405.03548) (this is a subset). |
+| [Bagel](https://github.com/jondurbin/bagel) | >2M? | Jon Durbin | Jan 2024 | Collection of datasets decontaminated with cosine similarity. |
+| [Hercules v4.5](https://huggingface.co/datasets/Locutusque/hercules-v4.5) | 1.72M | Sebastian Gabarain | Apr 2024 | Large-scale general-purpose dataset with math, code, RP, etc. See [v4](https://huggingface.co/datasets/Locutusque/hercules-v4.0) for the list of datasets. |
 | [Dolphin-2.9](https://huggingface.co/datasets/cognitivecomputations/Dolphin-2.9) | 1.39M | Cognitive Computations | Apr 2023 | Large-scale general-purpose dataset used by the Dolphin models. |
-| [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) | 1.04M | Zhao et al. | May 2023 | Real conversations between human users and GPT-3.5/4, including metadata. See the [WildChat paper](https://arxiv.org/abs/2405.01470). |
-| [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) | 1M | Teknium | Nov 2023 | Another large-scale dataset used by the OpenHermes models. |
-| [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) | 518k | Lian et al. | Sep 2023 | Curated subset of [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) using GPT-4-as-a-judge to remove wrong answers. |
-| [Tulu V2 Mix](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) | 326k | Ivison et al. | Nov 2023 | Mix of high-quality datasets. See [Tulu 2 paper](https://arxiv.org/abs/2311.10702). |
-| [UltraInteract SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) | 289k | Yuan et al. | Apr 2024 | Focus on math, coding, and logic tasks with step-by-step answers. See [Eurus paper](https://arxiv.org/abs/2404.02078). |
-| [NeurIPS-LLM-data](https://huggingface.co/datasets/upaya07/NeurIPS-LLM-data) | 204k | Jindal et al. | Nov 2023 | Winner of [NeurIPS LLM Efficiency Challenge](https://llm-efficiency-challenge.github.io/), with an interesting data preparation strategy. |
-| [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | 200k | Tunstall et al., Ding et al. | Oct 2023 | Heavily filtered version of the [UItraChat](https://github.com/thunlp/UltraChat) dataset, consisting of 1.4M dialogues generated by ChatGPT. |
-| [WizardLM_evol_instruct_V2](https://huggingface.co/datasets/mlabonne/WizardLM_evol_instruct_v2_196K-ShareGPT) | 143k | Xu et al. | Jun 2023 | Latest version of Evol-Instruct applied to Alpaca and ShareGPT data. See [WizardLM paper](https://arxiv.org/abs/2304.12244). |
-| [sft_datablend_v1](https://huggingface.co/datasets/nvidia/sft_datablend_v1) | 128k | NVIDIA | Jan 2024 | Blend of publicly available datasets: OASST, CodeContests, FLAN, T0, Open_Platypus, and GSM8K and others (45 total). |
-| [Synthia-v1.3](https://huggingface.co/datasets/migtissera/Synthia-v1.3) | 119k | Migel Tissera | Nov 2023 | High-quality synthetic data generated using GPT-4. |
+| [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) | 1.04M | Zhao et al. | May 2023 | Real conversations between human users and GPT-3.5/4, including metadata. See the [WildChat paper](https://arxiv.org/abs/2405.01470). |
+| [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) | 1M | Teknium | Nov 2023 | Another large-scale dataset used by the OpenHermes models. |
+| [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) | 518k | Lian et al. | Sep 2023 | Curated subset of [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) using GPT-4-as-a-judge to remove wrong answers. |
+| [Tulu V2 Mix](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture) | 326k | Ivison et al. | Nov 2023 | Mix of high-quality datasets. See [Tulu 2 paper](https://arxiv.org/abs/2311.10702). |
+| 🆕 [Magpie-Pro](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered) | 300k | Xu et al. | Jun 2024 | High-quality samples directly extracted from Llama 3 70B Instruct via a new technique. See [Magpie paper](https://arxiv.org/abs/2406.08464). |
+| [UltraInteract SFT](https://huggingface.co/datasets/openbmb/UltraInteract_sft) | 289k | Yuan et al. | Apr 2024 | Focus on math, coding, and logic tasks with step-by-step answers. See [Eurus paper](https://arxiv.org/abs/2404.02078). |
+| [NeurIPS-LLM-data](https://huggingface.co/datasets/upaya07/NeurIPS-LLM-data) | 204k | Jindal et al. | Nov 2023 | Winner of [NeurIPS LLM Efficiency Challenge](https://llm-efficiency-challenge.github.io/), with an interesting data preparation strategy. |
+| [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | 200k | Tunstall et al., Ding et al. | Oct 2023 | Heavily filtered version of the [UItraChat](https://github.com/thunlp/UltraChat) dataset, consisting of 1.4M dialogues generated by ChatGPT. |
+| [WizardLM_evol_instruct_V2](https://huggingface.co/datasets/mlabonne/WizardLM_evol_instruct_v2_196K-ShareGPT) | 143k | Xu et al. | Jun 2023 | Latest version of Evol-Instruct applied to Alpaca and ShareGPT data. See [WizardLM paper](https://arxiv.org/abs/2304.12244). |
+| [sft_datablend_v1](https://huggingface.co/datasets/nvidia/sft_datablend_v1) | 128k | NVIDIA | Jan 2024 | Blend of publicly available datasets: OASST, CodeContests, FLAN, T0, Open_Platypus, and GSM8K and others (45 total). |
+| [Synthia-v1.3](https://huggingface.co/datasets/migtissera/Synthia-v1.3) | 119k | Migel Tissera | Nov 2023 | High-quality synthetic data generated using GPT-4. |
 | [FuseChat-Mixture](https://huggingface.co/datasets/FuseAI/FuseChat-Mixture) | 95k | Wan et al. | Feb 2024 | Selection of samples from high-quality datasets. See [FuseChat paper](https://arxiv.org/abs/2402.16107). |
 | [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) | 84.4k | Köpf et al. | Mar 2023 | Human-generated assistant-style conversation corpus in 35 different languages. See [OASST1 paper](https://arxiv.org/abs/2304.07327) and [oasst2](https://huggingface.co/datasets/OpenAssistant/oasst2). |
 | [WizardLM_evol_instruct_70k](https://huggingface.co/datasets/mlabonne/WizardLM_evol_instruct_70k-ShareGPT) | 70k | Xu et al. | Apr 2023 | Evol-Instruct applied to Alpaca and ShareGPT data. See [WizardLM paper](https://arxiv.org/abs/2304.12244). |