AmberDatasets /
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license: odc-by
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  - pretrained


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This dataset contains the fully prepared data sequence used to train Amber, an LLM360 model.

About LLM360

LLM360 is an initiative for comprehensive and fully open-sourced LLMs, where all training details, model checkpoints, intermediate results, and additional analyses are made available to the community. Our goal is to advance the field by inviting the community to deepen the understanding of LLMs together. As the first step of the project LLM360, we release all intermediate model checkpoints, our fully-prepared pre-training dataset, all source code and configurations, and training details. We are committed to continually pushing the boundaries of LLMs through this open-source effort.

Get access now at LLM360 site

Data Description

  • Data Format: 360 tokenized data chunks, each instance has 2049 token indexes.
  • License: Apache 2.0
  • Resources for more information:


The amber dataset uses the following data mix.

Subset Tokens (Billion)
Arxiv 30.00
Book 28.86
C4 197.67
Refined-Web 665.01
StarCoder 291.92
StackExchange 21.75
Wikipedia 23.90
Total 1259.13

Loading Amber's Pretraining Data

Below is an example of how to download, sample, and detokenize any subset of AmberDatasets corresponding to an Amber checkpoint. Just set the CHECKPOINT_NUM to the subset you are interested in (0-359) and point CHECKPOINT_PATH to the local checkpoint folder.

import random
from transformers import AutoTokenizer
from datasets import load_dataset

CHECKPOINT_NUM = 0  # Pretraining dataset for checkpoint
NUM_SAMPLES = 10  # Number of random samples to decode
CHECKPOINT_PATH = "/path/to/ckpt_000/"  # Local path to a Amber checkpoint

dataset = load_dataset(

tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH)
samples = set(random.choices(range(len(dataset["train"])), k=NUM_SAMPLES))

for i, line in enumerate(dataset["train"]):
    if i in samples:
        tokens = line["token_ids"]


We release our work under ODC-BY, hence granting the rights over the dataset, but not the contents of the dataset individually.


To cite LLM360, you can cite the following:

      title={LLM360: Towards Fully Transparent Open-Source LLMs}, 
      author={Zhengzhong Liu and Aurick Qiao and Willie Neiswanger and Hongyi Wang and Bowen Tan and Tianhua Tao and Junbo Li and Yuqi Wang and Suqi Sun and Omkar Pangarkar and Richard Fan and Yi Gu and Victor Miller and Yonghao Zhuang and Guowei He and Haonan Li and Fajri Koto and Liping Tang and Nikhil Ranjan and Zhiqiang Shen and Xuguang Ren and Roberto Iriondo and Cun Mu and Zhiting Hu and Mark Schulze and Preslav Nakov and Tim Baldwin and Eric P. Xing},

If you only uses the original dataset, please cite the original datasets.