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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
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:", ""))
Citation
@misc{XGen,
title={Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length},
author={Erik Nijkamp, Hiroaki Hayashi, Tian Xie, Congying Xia, Bo Pang, Rui Meng, Wojciech Kryscinski, Lifu Tu, Meghana Bhat, Semih Yavuz, Chen Xing, Jesse Vig, Lidiya Murakhovs'ka, Jason Wu, Yingbo Zhou, Shafiq Rayhan Joty, Caiming Xiong},
howpublished={Salesforce AI Research Blog},
year={2023},
url={https://blog.salesforceairesearch.com/xgen}
}