[WIP] Upload folder using huggingface_hub (multi-commit 864777b2cf25356df168d510f0064ed9e2036106ef69d9fc8a756e0bcfbd9550)

README.md

@@ -1,38 +1,18 @@
----
-language:
-- en
-tags:
-- pretrained
-license: odc-by
----
+# Dataset description
 
-# Description of the Dataset
-
-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.
+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.
 
 ## Stage 1
-During this initial stage, half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B) is utilized, equivalent to approximately 345 billion tokens.
+During this stage, we utilize half of the [SlimPajama data](https://huggingface.co/datasets/cerebras/SlimPajama-627B).
 
 ## Stage 2
-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 the StarCoder data, we apply [FIM augmentation](https://arxiv.org/abs/2207.14255) with an FIM rate of 0.9 and an SPM rate of 0.5. The total token count for this stage is calculated as 0.5 * 690 + 2 * 291, resulting in 927 billion tokens.
+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.
 
 ## Stage 3
-The third stage involves reusing Python and web-related data from the [StarCoder data](https://huggingface.co/datasets/bigcode/starcoderdata), including HTML, CSS, and JavaScript. This data is utilized for training over three epochs, with the application of FIM at a rate of 0.3 alongside an SPM rate of 0.5. The total token count for this stage is 100 billion. Additionally, a small portion of the SlimPajama dataset, excluding the Github part, is also reused, contributing around 10 billion tokens.
-
-### Instruction tuning (Stage 3a)
-
-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.
-
-The detailed breakdown of the tokens is as followed:
-
-![data split](./data_split.png)
-
-
-# Primary Usage
+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.
 
-This dataset serves as the foundation for training CrystalCoder and supports further reproduction. For training from scratch, please refer to our [training codes](https://github.com/LLM360/crystalcoder-train). For training from middle checkpoints, please load the dataloader states in checkpoints and follow [this tutorial](https://docs.cerebras.net/en/latest/wsc/tutorials/dataloader-checkpointing.html).
+# Primary usage
 
-# License
-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.
+This dataset is used for our training of our CrystalCoder, and also for further reproduction.
 
-We release our work under [ODC-BY](https://opendatacommons.org/licenses/by/1-0/), hence granting the rights over the dataset, but not the contents of the dataset individually.
+# License:

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