Instructions to use boboliu/glm-4-9b-chat-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use boboliu/glm-4-9b-chat-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="boboliu/glm-4-9b-chat-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("boboliu/glm-4-9b-chat-FP8", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use boboliu/glm-4-9b-chat-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "boboliu/glm-4-9b-chat-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "boboliu/glm-4-9b-chat-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/boboliu/glm-4-9b-chat-FP8

SGLang

How to use boboliu/glm-4-9b-chat-FP8 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 "boboliu/glm-4-9b-chat-FP8" \
    --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": "boboliu/glm-4-9b-chat-FP8",
		"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 "boboliu/glm-4-9b-chat-FP8" \
        --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": "boboliu/glm-4-9b-chat-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use boboliu/glm-4-9b-chat-FP8 with Docker Model Runner:
```
docker model run hf.co/boboliu/glm-4-9b-chat-FP8
```

GLM-4-9b-chat Quantized with AutoFP8

使用 m-a-p/COIG-CQIA 的 COIG_pc 集作为校准量化的 glm-4-9b-chat 模型。

主要为中文通常语言逻辑任务，为 vLLM 准备。

评估

使用 lm-evaluation-harness + vLLM 进行评估：

项目	THUDM/glm-4-9b-chat	此项目	Recovery
ceval-valid	71.84	70.36	97.94%
cmmlu	72.23	70.42	97.49%
agieval_logiqa_zh (5 shots)	44.24	39.32	88.88%
平均	62.77	60.03	95.63%

Downloads last month: 6

Safetensors

Model size

9B params

Tensor type

BF16

F8_E4M3