amber-train

amber-train

Pre-training code for Amber 7B LLM

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Amber is the first model in the LLM360 family, an initiative for comprehensive and fully open-sourced LLMs. It is a 7B English language model with the LLaMA architecture. The model type is a language model with the same architecture as LLaMA-7B. It is licensed under Apache 2.0. The resources available include training code, data preparation, metrics, and fully processed Amber pretraining data. The model has been trained on various datasets like Arxiv, Book, C4, Refined-Web, StarCoder, StackExchange, and Wikipedia. The hyperparameters include a total of 6.7B parameters, hidden size of 4096, intermediate size of 11008, 32 attention heads, 32 hidden layers, RMSNorm ε of 1e^-6, max sequence length of 2048, and a vocabulary size of 32000.

README:

Amber: the first model of LLM360

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🤗 [Amber Download] • 🤗 [AmberChat Download] • 📈 [Analysis and Results] • 📗 Pretraining Dataset

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

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

Hyperparameters

Hyperparameter Value
Total Parameters 6.7B
Hidden Size 4096
Intermediate Size (MLPs) 11008
Number of Attention Heads 32
Number of Hidden Layers 32
RMSNorm ɛ 1e^-6
Max Seq Length 2048
Vocab Size 32000
Training Loss
loss curve

Evaluation

ARC HellaSwag
arc hellaswag
MMLU TruthfulQA
mmlu truthfulqa

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}
}

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