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<h1 align="center">Amber: the first model of LLM360</h1> <div align="center"> <img src="figs/amber_logo.png" alt="amber logo" width="300"><br><br> </div>
<p align="center"> <a href="https://github.com/LLM360/Analysis360/blob/dev/LICENSE"><img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="license"></a> </p> <p align="center"> šŸ¤— <a href="https://huggingface.co/LLM360/Amber">[Amber Download]</a> ā€¢ šŸ¤— <a href="https://huggingface.co/LLM360/AmberChat">[AmberChat Download]</a> ā€¢ šŸ“ˆ <a href="https://github.com/LLM360/Analysis360/blob/main/README.md#list-of-analysis-and-metrics ">[Analysis and Results]</a> ā€¢ šŸ“— <a href="https://huggingface.co/datasets/LLM360/AmberDatasets">Pretraining Dataset</a> </p>

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

Model Description

Amber is the first model in the LLM360 family. Amber is an 7B English language model with the LLaMA architecture.

Loading Amber

from transformers import LlamaTokenizer, LlamaForCausalLM

tokenizer = LlamaTokenizer.from_pretrained("LLM360/Amber", revision="ckpt_356")
model = LlamaForCausalLM.from_pretrained("LLM360/Amber", revision="ckpt_356")

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Amber Training Details

DataMix

SubsetTokens (Billion)
Arxiv30.00
Book28.86
C4197.67
Refined-Web665.01
StarCoder291.92
StackExchange21.75
Wikipedia23.90
Total1259.13

Hyperparameters

HyperparameterValue
Total Parameters6.7B
Hidden Size4096
Intermediate Size (MLPs)11008
Number of Attention Heads32
Number of Hidden Layers32
RMSNorm ɛ1e^-6
Max Seq Length2048
Vocab Size32000
Training Loss
<img src="figs/amber-training-loss.png" alt="loss curve" width="400"/>

Evaluation

ARCHellaSwag
<img src="figs/amber-arc-curve.png" alt="arc" width="400"/><img src="figs/amber-hellaswag-curve.png" alt="hellaswag" width="400"/>
MMLUTruthfulQA
<img src="figs/amber-mmlu-curve.png" alt="mmlu" width="400"/><img src="figs/amber-truthfulqa-curve.png" alt="truthfulqa" width="400"/>

Citation

BibTeX:

@misc{liu2023llm360,
      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},
      year={2023},
      eprint={2312.06550},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}