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| license: odc-by |
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| # K2 Dataset Card |
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| <!-- Provide a quick summary of the dataset. --> |
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| The following data mix was used to train [K2](https://huggingface.co/LLM360/K2) and achieve results in line with Llama 2 70B. |
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| ## Dataset Details |
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| K2 was trained on 1.4T tokens across two stages. The data sources and data mix for each stage are listed below. |
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| ### Dataset Description: Stage 1 |
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| <!-- Provide a longer summary of what this dataset is. --> |
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| | Dataset | Starting Tokens | Multiplier | Total Tokens |% of Total | |
| | ----------- | ----------- | ----------- | ----------- | ----------- | |
| | [dm-math](https://github.com/google-deepmind/mathematics_dataset) | 4.33B | 3x | 13B | 1% | |
| | pubmed-abstracts (from the Pile) | 4.77B | 3x | 14.3B | 1.1% | |
| | uspto (from the Pile) | 4.77B | 3x | 14.3B | 1.1% | |
| | pubmed-central (from the Pile) | 26B | 1x | 26B | 2% | |
| | [redpajama.arxiv](https://huggingface.co/datasets/cerebras/SlimPajama-627B) | 27.3B | 1x | 27.3B | 2.1% | |
| | [starcoder.spm](https://huggingface.co/datasets/bigcode/starcoderdata) | 67.6B | 0.5x | 33.8B | 2.6% | |
| | [starcoder.fim](https://huggingface.co/datasets/bigcode/starcoderdata) | 67.6B | 0.5x | 33.8B | 2.6% | |
| | [redpajama.stackexchange](https://huggingface.co/datasets/cerebras/SlimPajama-627B) | 61.1B | 1x | 61.1B | 4.7% | |
| | [starcoder](https://huggingface.co/datasets/bigcode/starcoderdata) | 132.6B | 0.5x | 66.3B | 5.1% | |
| | [pile-of-law](https://huggingface.co/datasets/pile-of-law/pile-of-law) | 76.7B | 1x | 76.7B | 5.9% | |
| | [redpajama.book](https://huggingface.co/datasets/cerebras/SlimPajama-627B) | 80.6B | 1x | 80.6B | 6.2% | |
| | [s2orc](https://allenai.org/data/s2orc) | 107.9B | 1x | 107.9B | 8.3% | |
| | [redpajama.wikipedia](https://huggingface.co/datasets/cerebras/SlimPajama-627B) | 22.1B | 6x | 132.6B | 10.2% | |
| | [refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 612.3B | 1x | 612.3B | 47.1% | |
| | Totals | - | - | 1.3T | 100% | |
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| ### Dataset Description: Stage 2 |
| | Dataset | Starting Tokens | Multiplier | Total Tokens |% of Total | |
| | ----------- | ----------- | ----------- | ----------- | ----------- | |
| | [open-web-math](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | 14.6B | 1x | 14.6B | 21% | |
| | [redpajama.arxiv](https://huggingface.co/datasets/cerebras/SlimPajama-627B) | 2B | 1x | 2B | 2.9% | |
| | [simple-wiki](https://huggingface.co/datasets/allenai/dolma) | 4.3B | 1x | 4.3B | 6.2% | |
| | [redpajama.book](https://huggingface.co/datasets/cerebras/SlimPajama-627B) | 2B | 1x | 2B | 2.9% | |
| | [algebraic-stack](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | 10.9B | 1x | 10.9B | 15.7% | |
| | [pile-of-law](https://huggingface.co/datasets/pile-of-law/pile-of-law) | 2B | 0.5x | 33.8B | 2.9% | |
| | books | 5.8B | 1x | 5.8B | 8.3% | |
| | [pes20](https://huggingface.co/datasets/allenai/peS2o) | 1.2B | 1x | 1.2B | 1.8% | |
| | [pubmed-central (from the Pile)](https://github.com/EleutherAI/pile-pubmedcentral) | 2B | 1x | 2B | 2.9% | |
| | [redpajama.wikipedia](https://huggingface.co/datasets/cerebras/SlimPajama-627B) | 2B | 1x | 2B | 2.9% | |
| | python | 20.5B | 1x | 20.5B | 29.6% | |
| | [s2orc](https://allenai.org/data/s2orc) | 2B | 1x | 2B | 2.9% | |
| | Totals | - | - | 69.4B* | 100% | |
| *rounding |
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| #### Data Collection and Processing |
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| <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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| A step-by-step tutorial for reproducing the K2's data preperation can be found in the [LLM360 Pretraining Suite here](https://www.llm360.ai/pretraining.html) |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
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| ## Citation |
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| **BibTeX:** |
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| ```bibtex |
| @misc{ |
| title={LLM360 K2-65B: Scaling Up Open and Transparent Language Models}, |
| author={The LLM360 Team}, |
| year={2024}, |
| } |
| ``` |
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