[WIP] Upload folder using huggingface_hub (multi-commit 3f2eef1f156829543067cba1605a7242d62f0e16b676ad7d5678ae570ea58158)
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README.md
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language:
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tags:
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- pretrained
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license: odc-by
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---
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This release integrates the entire data sequence utilized in the CrystalCoder training. It encompasses data sequences from the three pre-training stages, combining information from two prior works: the [SlimPajama dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata), totaling approximately 1300 billion tokens. These tokens are distributed across three stages, each with distinct weights.
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## Stage 1
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During this
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## Stage 2
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In the second stage, the remaining half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B) is employed, along with two epochs of [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata). For
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## Stage 3
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The third stage involves
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### Instruction tuning (Stage 3a)
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To enhance the model's proficiency in real chat scenarios, we utilize a diverse set of instruction tuning datasets, totaling approximately 1 billion tokens. Specifically, our data include [OASST1-guanaco](https://huggingface.co/datasets/openaccess-ai-collective/oasst1-guanaco-extended-sharegpt), [SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca), [ShareGPT_V4.3](https://huggingface.co/datasets/Aeala/ShareGPT_Vicuna_unfiltered), [Evol-ShareGPT](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k), [CodeAlpaca](https://huggingface.co/datasets/lucasmccabe-lmi/CodeAlpaca-20k), [Rosetta Code](https://github.com/sahil280114/codealpaca/blob/master/data/rosetta_alpaca.json), [Evol-CodeAlpaca 1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1), [Evol-CodeAlpaca 2](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1), and a self-generated dataset centered on website creation through the [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) pipeline. We will release the full dataset soon.
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The detailed breakdown of the tokens is as followed:
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![data split](./data_split.png)
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# Primary Usage
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Pretraining data in langauge model mostly comes from a collection of data sources with various licenses. Any use of all or part of the data here must abide by the terms of the original licenses, including attribution clauses when relevant. We refer users to [SlimPajama dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata) for detailed license attribution.
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# Dataset description
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This release incorporates the complete data sequence used in our CrystalCoder training, covering data sequences from the three pre-training stages. The data is a combination of two previous works: [SlimPajama dataset](https://huggingface.co/datasets/cerebras/SlimPajama-627B) and [StarCoder](https://huggingface.co/datasets/bigcode/starcoderdata). We divide them across three stages with different weights.
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## Stage 1
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During this stage, we utilize half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B).
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## Stage 2
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In the second stage, the remaining half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B) is employed, along with two epochs of [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata). For StarCoder data, we implement [FIM augmentation](https://arxiv.org/abs/2207.14255) with an FIM rate of 0.9.
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## Stage 3
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The third stage involves the reuse of Python, HTML, CSS, and JavaScript data from the [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata) for training over three epochs. In this stage, FIM with a rate of 0.3 is applied. Additionally, a small portion of the SlimPajama dataset except the Github part is also reused.
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# Primary usage
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This dataset is used for our training of our CrystalCoder, and also for further reproduction.
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# License:
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