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A newer version of the Gradio SDK is available:
4.42.0
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 GPU 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: 3.10.12 (recommend) / 3.12.3 have been tested
- 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 GPU memory consumption is calculated based on the peak value
GLM-4-9B-Chat
Dtype | GPU Memory | Prefilling | Decode Speed | Remarks |
---|---|---|---|---|
BF16 | 19 GB | 0.2s | 27.8 tokens/s | Input length is 1000 |
BF16 | 21 GB | 0.8s | 31.8 tokens/s | Input length is 8000 |
BF16 | 28 GB | 4.3s | 14.4 tokens/s | Input length is 32000 |
BF16 | 58 GB | 38.1s | 3.4 tokens/s | Input length is 128000 |
Dtype | GPU Memory | Prefilling | Decode Speed | Remarks |
---|---|---|---|---|
INT4 | 8 GB | 0.2s | 23.3 tokens/s | Input length is 1000 |
INT4 | 10 GB | 0.8s | 23.4 tokens/s | Input length is 8000 |
INT4 | 17 GB | 4.3s | 14.6 tokens/s | Input length is 32000 |
GLM-4-9B-Chat-1M
Dtype | GPU Memory | Prefilling | Decode Speed | Remarks |
---|---|---|---|---|
BF16 | 74497MiB | 98.4s | 2.3653 tokens/s | Input length is 200000 |
If your input exceeds 200K, we recommend that you use the vLLM backend with multi gpus for inference to get better performance.
GLM-4V-9B
Dtype | GPU Memory | Prefilling | Decode Speed | Remarks |
---|---|---|---|---|
BF16 | 28 GB | 0.1s | 33.4 tokens/s | Input length is 1000 |
BF16 | 33 GB | 0.7s | 39.2 tokens/s | Input length is 8000 |
Dtype | GPU Memory | Prefilling | Decode Speed | Remarks |
---|---|---|---|---|
INT4 | 10 GB | 0.1s | 28.7 tokens/s | Input length is 1000 |
INT4 | 15 GB | 0.8s | 24.2 tokens/s | Input length is 8000 |
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 GPU memory, supporting CUDA or ROCM and supporting
BF16
reasoning (FP16
precision cannot be finetuned, and there is a small probability of problems in infering)
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 # GLM-4-9B-Chat
python trans_cli_vision_demo.py # GLM-4V-9B
- Use the Gradio web client to communicate with the GLM-4-9B-Chat 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-Chat 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