XGen-7B-8K-Inst
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
Models
Base models
- XGen-7B-4K-Base: XGen-7B model pre-trained under 4K sequence length.
- License: Apache-2.0
- XGen-7B-8K-Base: XGen-7B model pre-trained under 8K sequence length.
- License: Apache-2.0
Instruction-finetuned models
Supervised finetuned model on public domain instructional data. Released for research purpose only.
How to run
The training data for the models are tokenized with OpenAI Tiktoken library.
To use this model, install the package via pip:
pip install tiktoken
The 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-inst", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst", torch_dtype=torch.bfloat16)
header = (
"A chat between a curious human and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the human's questions.\n\n"
)
article = "" # insert a document here
prompt = f"### Human: Please summarize the following article.\n\n{article}.\n###"
inputs = tokenizer(header + prompt, return_tensors="pt")
sample = model.generate(**inputs, do_sample=True, max_new_tokens=2048, top_k=100, eos_token_id=50256)
output = tokenizer.decode(sample[0])
print(output.strip().replace("Assistant:", ""))
Ethical Considerations
This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
Citation
@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}
}