Instructions to use zai-org/LongWriter-glm4-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/LongWriter-glm4-9b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zai-org/LongWriter-glm4-9b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zai-org/LongWriter-glm4-9b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zai-org/LongWriter-glm4-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/LongWriter-glm4-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/LongWriter-glm4-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/LongWriter-glm4-9b

SGLang

How to use zai-org/LongWriter-glm4-9b 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-glm4-9b" \
    --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": "zai-org/LongWriter-glm4-9b",
		"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 "zai-org/LongWriter-glm4-9b" \
        --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": "zai-org/LongWriter-glm4-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/LongWriter-glm4-9b with Docker Model Runner:
```
docker model run hf.co/zai-org/LongWriter-glm4-9b
```

TypeError: BFloat16 is not supported on MPS

#11

by hiepsiga - opened Nov 2, 2024

Discussion

hiepsiga

Nov 2, 2024

Hi there,

I appreciate your project.

I tried to install and run the example code as below

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)

But it shows error TypeError: BFloat16 is not supported on MPS

I think it might have something to do with the version of MacOS I'm running, which is Monterey 12.7.2. It could also have something to do with the Intel x86 CPU, since we still use iMacs with Intel chips. Because of those limitations, I was only allowed to install torch 2.2.2, since 2.3 and above do not run on x86 architecture (https://github.com/pytorch/pytorch/issues/114602)

Do you have any way I can get past this?

Below is the specific log of the error:

Traceback (most recent call last):
  File "/Users/hongquan2512/Downloads/LongWriter-main/THUDM/LongWriter-glm4-9b/demo.py", line 4, in <module>
    model = AutoModelForCausalLM.from_pretrained("THUDM/LongWriter-glm4-9b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/hongquan2512/miniforge3/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py", line 559, in from_pretrained
    return model_class.from_pretrained(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/hongquan2512/miniforge3/lib/python3.12/site-packages/transformers/modeling_utils.py", line 4225, in from_pretrained
    ) = cls._load_pretrained_model(
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/hongquan2512/miniforge3/lib/python3.12/site-packages/transformers/modeling_utils.py", line 4728, in _load_pretrained_model
    new_error_msgs, offload_index, state_dict_index = _load_state_dict_into_meta_model(
                                                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/hongquan2512/miniforge3/lib/python3.12/site-packages/transformers/modeling_utils.py", line 993, in _load_state_dict_into_meta_model
    set_module_tensor_to_device(model, param_name, param_device, **set_module_kwargs)
  File "/Users/hongquan2512/miniforge3/lib/python3.12/site-packages/accelerate/utils/modeling.py", line 329, in set_module_tensor_to_device
    new_value = value.to(device)
                ^^^^^^^^^^^^^^^^
TypeError: BFloat16 is not supported on MPS

Kind regards,

ZHANGYUXUAN-zR

Z.ai org Dec 4, 2024

You can use fp16 for inference, but the effect may not be as good as BF16.

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