Instructions to use zai-org/LongWriter-llama3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/LongWriter-llama3.1-8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/LongWriter-llama3.1-8b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zai-org/LongWriter-llama3.1-8b") model = AutoModelForCausalLM.from_pretrained("zai-org/LongWriter-llama3.1-8b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zai-org/LongWriter-llama3.1-8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/LongWriter-llama3.1-8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/LongWriter-llama3.1-8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zai-org/LongWriter-llama3.1-8b
- SGLang
How to use zai-org/LongWriter-llama3.1-8b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zai-org/LongWriter-llama3.1-8b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/LongWriter-llama3.1-8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zai-org/LongWriter-llama3.1-8b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/LongWriter-llama3.1-8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zai-org/LongWriter-llama3.1-8b with Docker Model Runner:
docker model run hf.co/zai-org/LongWriter-llama3.1-8b
LongWriter-llama3.1-8b
π€ [LongWriter Dataset] β’ π» [Github Repo] β’ π [LongWriter Paper]
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}
}
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