Instructions to use QuantFactory/LongWriter-glm4-9b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/LongWriter-glm4-9b-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuantFactory/LongWriter-glm4-9b-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/LongWriter-glm4-9b-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/LongWriter-glm4-9b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/LongWriter-glm4-9b-GGUF", filename="LongWriter-glm4-9b.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/LongWriter-glm4-9b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/LongWriter-glm4-9b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/LongWriter-glm4-9b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/LongWriter-glm4-9b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
- SGLang
How to use QuantFactory/LongWriter-glm4-9b-GGUF 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 "QuantFactory/LongWriter-glm4-9b-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/LongWriter-glm4-9b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "QuantFactory/LongWriter-glm4-9b-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/LongWriter-glm4-9b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use QuantFactory/LongWriter-glm4-9b-GGUF with Ollama:
ollama run hf.co/QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/LongWriter-glm4-9b-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/LongWriter-glm4-9b-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for QuantFactory/LongWriter-glm4-9b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/LongWriter-glm4-9b-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/LongWriter-glm4-9b-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/LongWriter-glm4-9b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/LongWriter-glm4-9b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LongWriter-glm4-9b-GGUF-Q4_K_M
List all available models
lemonade list
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 Dataset] • 💻 [Github Repo] • 📃 [LongWriter Paper]
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}
}
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