Instructions to use GizzmoShifu/glm-4-9b-chat-hf-fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GizzmoShifu/glm-4-9b-chat-hf-fp8 with Transformers:

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

pipe = pipeline("text-generation", model="GizzmoShifu/glm-4-9b-chat-hf-fp8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GizzmoShifu/glm-4-9b-chat-hf-fp8")
model = AutoModelForCausalLM.from_pretrained("GizzmoShifu/glm-4-9b-chat-hf-fp8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use GizzmoShifu/glm-4-9b-chat-hf-fp8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GizzmoShifu/glm-4-9b-chat-hf-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": "GizzmoShifu/glm-4-9b-chat-hf-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GizzmoShifu/glm-4-9b-chat-hf-fp8

SGLang

How to use GizzmoShifu/glm-4-9b-chat-hf-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 "GizzmoShifu/glm-4-9b-chat-hf-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": "GizzmoShifu/glm-4-9b-chat-hf-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 "GizzmoShifu/glm-4-9b-chat-hf-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": "GizzmoShifu/glm-4-9b-chat-hf-fp8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GizzmoShifu/glm-4-9b-chat-hf-fp8 with Docker Model Runner:
```
docker model run hf.co/GizzmoShifu/glm-4-9b-chat-hf-fp8
```

Built with glm-4

This repository provides a quantised version of the GLM-4-9b-Chat-HF model.

Licensing and Usage

This model is distributed under the glm-4-9b License.

Attribution: This model is "Built with glm-4".
Naming: The model name includes the required "glm-4" prefix.
Commercial Use: Users wishing to use this model for commercial purposes must complete the registration here.
Restrictions: Usage for military or illegal purposes is strictly prohibited.

Quantisation Overview

This model was quantised to FP8_DYNAMIC (Dynamic 8-bit Floating Point) to optimize performance while maintaining chat capabilities.

Methodology: Quantised using the llmcompressor library with a one-shot calibration process.
Calibration Data: A custom-curated dataset was used, pulling from ultrachat_200k and LongAlign-10k to ensure the model handles both short-form and long-form context effectively.
Architecture: The process preserved the original 40-layer dense structure (layers 0-39) required for the 9B model architecture.

Quantisation Details

Scheme: FP8_DYNAMIC (Dynamic 8-bit Floating Point).
Format: compressed-tensors.
Calibration: One-shot calibration using llmcompressor.
Calibration Data: Custom distribution weighted toward production-typical prompt lengths:
- 256–1024 tokens: 90%
- 1024–2048 tokens: 10%
Precision: FP8_DYNAMIC (Dynamic 8-bit Floating Point)

Usage

This model is designed for runtimes compatible with the FP8_DYNAMIC format, such as vLLM.

An example docker-compose is as follows assuming the model is stored in /models/glm-4-9b-chat-hf-fp8:

services:
  glm4-9b-fp8:
    image: nvcr.io/nvidia/vllm:26.01-py3
    container_name: glm4-9b-fp8
    ipc: host
    shm_size: "32gb"
    ports:
      - "8080:8080"
    volumes:
      - /models:/mnt/models:ro
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    command: >
      python3 -m vllm.entrypoints.openai.api_server
      --model /mnt/models/glm-4-9b-chat-hf-fp8
      --served-model-name glm-4-9b-chat
      --port 8080
      --quantization compressed-tensors
      --kv-cache-dtype fp8
      --max-model-len 32768
      --max-num-seqs 64
      --gpu-memory-utilization 0.75

For questions regarding the original model license, contact license@zhipuai.cn.

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Safetensors

Model size

9B params

Tensor type

BF16

F8_E4M3

Model tree for GizzmoShifu/glm-4-9b-chat-hf-fp8

Base model

zai-org/glm-4-9b-chat

Finetuned

zai-org/glm-4-9b-chat-hf

Quantized

(4)

this model