---
language:
- en
- zh
library_name: transformers
tags:
- Long Context
- chatglm
- llama
datasets:
- THUDM/LongWriter-6k
pipeline_tag: text-generation
---
# LongWriter-glm4-9b

<p align="center">
  🤗 <a href="https://huggingface.co/datasets/THUDM/LongWriter-6k" target="_blank">[LongWriter Dataset] </a> • 💻 <a href="https://github.com/THUDM/LongWriter" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2408.07055" target="_blank">[LongWriter Paper]</a> 
</p>

LongWriter-glm4-9b is trained based on [glm-4-9b](https://huggingface.co/THUDM/glm-4-9b), and is capable of generating 10,000+ words at once.


A simple demo for deployment of the model:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("THUDM/LongWriter-glm4-9b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("THUDM/LongWriter-glm4-9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
model = model.eval()
query = "Write a 10000-word China travel guide"
prompt = f"[INST]{query}[/INST]"
input = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
context_length = input.input_ids.shape[-1]
output = model.generate(
    **input,
    max_new_tokens=32768,
    num_beams=1,
    do_sample=True,
    temperature=0.5,
)[0]
response = tokenizer.decode(output[context_length:], skip_special_tokens=True)
print(response)
```

License: [glm-4-9b License](https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/LICENSE)

## Citation

If you find our work useful, please consider citing LongWriter:

```
@article{bai2024longwriter,
  title={LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs}, 
  author={Yushi Bai and Jiajie Zhang and Xin Lv and Linzhi Zheng and Siqi Zhu and Lei Hou and Yuxiao Dong and Jie Tang and Juanzi Li},
  journal={arXiv preprint arXiv:2408.07055},
  year={2024}
}
```