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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](https://blog.salesforceairesearch.com/xgen/)

*Authors*: [Erik Nijkamp](https://eriknijkamp.com)\*, Tian Xie\*, [Hiroaki Hayashi](https://hiroakih.me/)\*, [Bo Pang](https://scholar.google.com/citations?user=s9fNEVEAAAAJ&hl=en)\*, Congying Xia\*, Chen Xing, Rui Meng, Wojciech Kryscinski, Lifu Tu, Meghana Bhat, Semih Yavuz, Jesse Vig, Lidiya Murakhovs'ka, [Chien-Sheng Wu](https://jasonwu0731.github.io/), [Yingbo Zhou](https://scholar.google.com/citations?user=H_6RQ7oAAAAJ&hl=en), [Shafiq Rayhan Joty](https://raihanjoty.github.io/), [Caiming Xiong](http://cmxiong.com/), Silvio Savarese.

(* indicates equal contribution).

## Models

### Base models
* [XGen-7B-4K-Base](https://huggingface.co/Salesforce/xgen-7b-4k-base): XGen-7B model pre-trained under 4K sequence length.
  * License: Apache-2.0
* [XGen-7B-8K-Base](https://huggingface.co/Salesforce/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.

* [XGen-7B-8K-Inst](https://huggingface.co/Salesforce/xgen-7b-8k-inst)

## How to run

The training data for the models are tokenized with OpenAI Tiktoken library.
To use this model, install the package via `pip`:

```sh
pip install tiktoken
```

The models can be used as auto-regressive samplers as follows:

```python
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

```bibtex
@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, Rui Meng, Wojciech Kryscinski, Lifu Tu, Meghana Bhat, Semih Yavuz, Jesse Vig, Lidiya Murakhovs'ka, Chien-Sheng Wu, Yingbo Zhou, Shafiq Rayhan Joty, Caiming Xiong, Silvio Savarese},
  howpublished={Salesforce AI Research Blog},
  year={2023},
  url={https://blog.salesforceairesearch.com/xgen}
}
```