Salesforce / xgen-7b-4k-base

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Total runs: 113
24-hour runs: 0
7-day runs: 2
30-day runs: -226
Model's Last Updated: February 01 2025
text-generation

Introduction of xgen-7b-4k-base

Model Details of xgen-7b-4k-base

XGen-7B-4K-Base

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-4k-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-4k-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]))
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}
}

Runs of Salesforce xgen-7b-4k-base on huggingface.co

113
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0
24-hour runs
6
3-day runs
2
7-day runs
-226
30-day runs

More Information About xgen-7b-4k-base huggingface.co Model

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xgen-7b-4k-base huggingface.co

xgen-7b-4k-base huggingface.co is an AI model on huggingface.co that provides xgen-7b-4k-base's model effect (), which can be used instantly with this Salesforce xgen-7b-4k-base model. huggingface.co supports a free trial of the xgen-7b-4k-base model, and also provides paid use of the xgen-7b-4k-base. Support call xgen-7b-4k-base model through api, including Node.js, Python, http.

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Salesforce xgen-7b-4k-base online free url in huggingface.co:

https://huggingface.co/Salesforce/xgen-7b-4k-base

xgen-7b-4k-base install

xgen-7b-4k-base is an open source model from GitHub that offers a free installation service, and any user can find xgen-7b-4k-base on GitHub to install. At the same time, huggingface.co provides the effect of xgen-7b-4k-base install, users can directly use xgen-7b-4k-base installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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