Official research release for the family of XGen models (7B) by Salesforce AI Research:
Title: Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length
Authors: Erik Nijkamp*, Tian Xie*, Hiroaki Hayashi*, Bo Pang*, Congying Xia*, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryscinski, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Rayhan Joty, Caiming Xiong.
(* indicates equal contribution)
Correspondence to: Shafiq Rayhan Joty, Caiming Xiong
Model cards are published on the HuggingFace Hub:
- XGen-7B-4K-Base with support for 4K sequence length.
- XGen-7B-8K-Base with support for 8K sequence length.
- XGen-7B-8k-Inst with instruction-finetuning (for research purpose only).
The tokenization uses the OpenAI Tiktoken package, which can be installed via pip:
pip install tiktokenThe models can be used as auto-regressive samplers as follows:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/xgen-7b-8k-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-base", torch_dtype=torch.bfloat16)
inputs = tokenizer("The world is", return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))@misc{XGen,
title={Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length},
author={Erik Nijkamp, Tian Xie, Hiroaki Hayashi, Bo Pang, Congying Xia, Chen Xing, Jesse Vig, Semih Yavuz, Philippe Laban, Ben Krause, Senthil Purushwalkam, Tong Niu, Wojciech Kryscinski, Lidiya Murakhovs'ka, Prafulla Kumar Choubey, Alex Fabbri, Ye Liu, Rui Meng, Lifu Tu, Meghana Bhat, Chien-Sheng Wu, Silvio Savarese, Yingbo Zhou, Shafiq Rayhan Joty, Caiming Xiong},
howpublished={ArXiv},
year={2023},
url={https://arxiv.org/abs/2309.03450}
}