QuantFactory/LongWriter-glm4-9b-GGUF
This is quantized version of THUDM/LongWriter-glm4-9b created using llama.cpp
Original Model Card
LongWriter-glm4-9b
LongWriter-glm4-9b is trained based on glm-4-9b, and is capable of generating 10,000+ words at once.
Environment: Same environment requirement as glm-4-9b-chat (transforemrs>=4.43.0).
A simple demo for deployment of the model:
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"
response, history = model.chat(tokenizer, query, history=[], max_new_tokens=32768, temperature=0.5)
print(response)
You can also deploy the model with vllm, which allows 10,000+ words generation within a minute. Here is an example code:
from vllm import LLM, SamplingParams
model = LLM(
model= "THUDM/LongWriter-glm4-9b",
dtype="auto",
trust_remote_code=True,
tensor_parallel_size=1,
max_model_len=32768,
gpu_memory_utilization=1,
)
tokenizer = model.get_tokenizer()
stop_token_ids = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), tokenizer.get_command("<|observation|>")]
generation_params = SamplingParams(
temperature=0.5,
top_p=0.8,
top_k=50,
max_tokens=32768,
repetition_penalty=1,
stop_token_ids=stop_token_ids
)
query = "Write a 10000-word China travel guide"
input_ids = tokenizer.build_chat_input(query, history=[], role='user').input_ids[0].tolist()
outputs = model.generate(
sampling_params=generation_params,
prompt_token_ids=[input_ids],
)
output = outputs[0]
print(output.outputs[0].text)
License: glm-4-9b 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}
}