Instructions to use axteam/GLM-4-9B-0414-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use axteam/GLM-4-9B-0414-FP8-dynamic with Transformers:

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

pipe = pipeline("text-generation", model="axteam/GLM-4-9B-0414-FP8-dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("axteam/GLM-4-9B-0414-FP8-dynamic")
model = AutoModelForCausalLM.from_pretrained("axteam/GLM-4-9B-0414-FP8-dynamic", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use axteam/GLM-4-9B-0414-FP8-dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "axteam/GLM-4-9B-0414-FP8-dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "axteam/GLM-4-9B-0414-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/axteam/GLM-4-9B-0414-FP8-dynamic

SGLang

How to use axteam/GLM-4-9B-0414-FP8-dynamic 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 "axteam/GLM-4-9B-0414-FP8-dynamic" \
    --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": "axteam/GLM-4-9B-0414-FP8-dynamic",
		"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 "axteam/GLM-4-9B-0414-FP8-dynamic" \
        --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": "axteam/GLM-4-9B-0414-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use axteam/GLM-4-9B-0414-FP8-dynamic with Docker Model Runner:
```
docker model run hf.co/axteam/GLM-4-9B-0414-FP8-dynamic
```

GLM-4-9B-0414 · FP8 (dynamic)

8-bit quantization of zai-org/GLM-4-9B-0414 for vLLM serving. Quantized by axteam.


Base model	`zai-org/GLM-4-9B-0414`
Method	FP8 (dynamic)
Format	compressed-tensors `FP8_DYNAMIC`
Weights / activations	W8A8 — FP8 E4M3 weights (per-channel), dynamic per-token FP8 activations
Architecture	Dense (GLM-4 · Glm4ForCausalLM) · 9.4B · text-only
Size on disk	~9.9 GB
Calibration	None (data-free)
License	mit

Quantization details

Scheme — W8A8 — FP8 E4M3 weights (per-channel), dynamic per-token FP8 activations, stored as compressed-tensors FP8_DYNAMIC.
Kept in BF16 — token embeddings, the LM head, and all RMSNorms. Only attention & MLP projections are quantized.
Post-training quantization (no retraining). Tokenizer / chat_template identical to the original.

Evaluation

Checked against the BF16 original (perplexity + top-1, EN/KO).

Usage (vLLM)

vllm serve axteam/GLM-4-9B-0414-FP8-dynamic

License

Quantized derivative of zai-org/GLM-4-9B-0414 (mit).

한국어 요약

zai-org/GLM-4-9B-0414 를 FP8 (dynamic) 로 양자화한 서빙용(8-bit) 모델입니다. 작성: axteam.

방식 — FP8 E4M3, 가중치 채널별 스케일, 활성화 동적 FP8. 형식 compressed-tensors FP8_DYNAMIC.
BF16 유지 — 임베딩·LM 헤드·모든 norm. attention·MLP projection 만 양자화.
PTQ(재학습 없음), 토크나이저·chat_template 원본 동일.
품질 — BF16 대비 perplexity·top-1 검증.
사용 — vllm serve axteam/GLM-4-9B-0414-FP8-dynamic. 라이선스 mit.

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Safetensors

Model size

9B params

Tensor type

BF16

F8_E4M3

Model tree for axteam/GLM-4-9B-0414-FP8-dynamic

Base model

zai-org/GLM-4-9B-0414

Quantized

(28)

this model