RichardErkhov/THUDM_-_LongWriter-llama3.1-8b-gguf
Quantization made by Richard Erkhov.
LongWriter-llama3.1-8b - GGUF
- Model creator: https://huggingface.co/THUDM/
- Original model: https://huggingface.co/THUDM/LongWriter-llama3.1-8b/
Original model description: --- language:
- en
- zh library_name: transformers tags:
- Long Context
- chatglm
- llama datasets:
- THUDM/LongWriter-6k license: llama3.1 ---
LongWriter-llama3.1-8b
<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-llama3.1-8b is trained based on Meta-Llama-3.1-8B, and is capable of generating 10,000+ words at once.
Environment: transformers>=4.43.0
Please ahere to the prompt template (system prompt is optional): <<SYS>>\n{system prompt}\n<</SYS>>\n\n[INST]{query1}[/INST]{response1}[INST]{query2}[/INST]{response2}...
A simple demo for deployment of the model:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("THUDM/LongWriter-llama3.1-8b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("THUDM/LongWriter-llama3.1-8b", 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)You can also deploy the model with vllm, which allows 10,000+ words generation within a minute. Here is an example code:
model = LLM(
model= "THUDM/LongWriter-llama3.1-8b",
dtype="auto",
trust_remote_code=True,
tensor_parallel_size=1,
max_model_len=32768,
gpu_memory_utilization=0.5,
)
tokenizer = model.get_tokenizer()
generation_params = SamplingParams(
temperature=0.5,
top_p=0.8,
top_k=50,
max_tokens=32768,
repetition_penalty=1,
)
query = "Write a 10000-word China travel guide"
prompt = f"[INST]{query}[/INST]"
input_ids = tokenizer(prompt, truncation=False, return_tensors="pt").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: Llama-3.1 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}
}