glm4/basic_demo

Basic Demo

In this demo, you will experience how to use the glm-4-9b open source model to perform basic tasks.

Please follow the steps in the document strictly to avoid unnecessary errors.

Device and dependency check

Related inference test data

**The data in this document are tested in the following hardware environment. The actual operating environment requirements and the video memory occupied by the operation are slightly different. Please refer to the actual operating environment. ** Test hardware information:

• OS: Ubuntu 22.04
• Memory: 512GB
• Python: 3.12.3
• CUDA Version: 12.3
• GPU Driver: 535.104.05
• GPU: NVIDIA A100-SXM4-80GB * 8

The stress test data of relevant inference are as follows:

All tests are performed on a single GPU, and all video memory consumption is calculated based on the peak value

Accuracy	Video memory usage	Prefilling / First ring	Decode Speed	Remarks
BF16	19047MiB	0.1554s	27.8193 tokens/s	Input length is 1000
BF16	20629MiB	0.8199s	31.8613 tokens/s	Input length is 8000
BF16	27779MiB	4.3554s	14.4108 tokens/s	Input length is 32000
BF16	57379MiB	38.1467s	3.4205 tokens/s	Input length is 128000
BF16	74497MiB	98.4930s	2.3653 tokens/s	Input length is 200000

Precision	Video Memory	Prefilling / First Sound	Decode Speed	Remarks
Int4	8251MiB	0.1667s	23.3903 tokens/s	Input length is 1000
Int4	9613MiB	0.8629s	23.4248 tokens/s	Input length is 8000
Int4	16065MiB	4.3906s	14.6553 tokens/s	Input length is 32000

Minimum hardware requirements

If you want to run the most basic code provided by the official (transformers backend) you need:

• Python >= 3.10
• Memory of at least 32 GB

If you want to run all the codes in this folder provided by the official, you also need:

• Linux operating system (Debian series is best)
• GPU device with more than 8GB video memory, supporting CUDA or ROCM and supporting BF16 reasoning (GPUs above A100, V100, 20 and older GPU architectures are not supported)

Install dependencies

pip install -r requirements.txt

Basic function calls

**Unless otherwise specified, all demos in this folder do not support advanced usage such as Function Call and All Tools **

Use transformers backend code

• Use the command line to communicate with the glm-4-9b model.

python trans_cli_demo.py

• Use the Gradio web client to communicate with the glm-4-9b model.

python trans_web_demo.py

• Use Batch inference.

python cli_batch_request_demo.py

Use VLLM backend code

• Use the command line to communicate with the glm-4-9b model.

python vllm_cli_demo.py

• Build the server by yourself and use the request format of OpenAI API to communicate with the glm-4-9b model. This demo supports Function Call and All Tools functions.

Start the server:

python openai_api_server.py

Client request:

python openai_api_request.py

Stress test

Users can use this code to test the generation speed of the model on the transformers backend on their own devices:

python trans_stress_test.py