Instructions to use alecccdd/GLM-4.6V-Flash-W8A8-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alecccdd/GLM-4.6V-Flash-W8A8-INT8 with Transformers:

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

pipe = pipeline("image-text-to-text", model="alecccdd/GLM-4.6V-Flash-W8A8-INT8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("alecccdd/GLM-4.6V-Flash-W8A8-INT8")
model = AutoModelForImageTextToText.from_pretrained("alecccdd/GLM-4.6V-Flash-W8A8-INT8")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alecccdd/GLM-4.6V-Flash-W8A8-INT8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alecccdd/GLM-4.6V-Flash-W8A8-INT8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alecccdd/GLM-4.6V-Flash-W8A8-INT8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/alecccdd/GLM-4.6V-Flash-W8A8-INT8

SGLang

How to use alecccdd/GLM-4.6V-Flash-W8A8-INT8 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 "alecccdd/GLM-4.6V-Flash-W8A8-INT8" \
    --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": "alecccdd/GLM-4.6V-Flash-W8A8-INT8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "alecccdd/GLM-4.6V-Flash-W8A8-INT8" \
        --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": "alecccdd/GLM-4.6V-Flash-W8A8-INT8",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use alecccdd/GLM-4.6V-Flash-W8A8-INT8 with Docker Model Runner:
```
docker model run hf.co/alecccdd/GLM-4.6V-Flash-W8A8-INT8
```

GLM-4.6V-Flash GPTQ W8A8 INT8

Model Details

Quantization Details

Quantization Method: GPTQ
Bits: 8
Block Size: 128
Calibration Dataset: 5CD-AI/LLaVA-CoT-o1-Instruct
Quantization Tool: llm-compressor

Memory Usage

Type	GLM-4.6V-Flash	GLM-4.6V-Flash-W8A8-INT8
Memory Size	19.2 GB	15.8 GB
KV Cache per Token	40.0 kB	20.0 kB
KV Cache per Context	5.0 GB	2.5 GB

Inference

Prerequisite

pip install vllm>=0.12.0
pip install --upgrade git+https://github.com/huggingface/transformers.git

Basic Usage

vllm serve alecccdd/GLM-4.6V-Flash-W8A8-INT8 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--allowed-local-media-path / \
--mm-encoder-tp-mode data  \
--mm-processor-cache-type shm \
--gpu-memory-utilization 0.95

Citation

If you use this model, please cite the following paper from the original model:

@misc{vteam2025glm45vglm41vthinkingversatilemultimodal,
      title={GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning}, 
      author={V Team and Wenyi Hong and Wenmeng Yu and Xiaotao Gu and Guo Wang and Guobing Gan and Haomiao Tang and Jiale Cheng and Ji Qi and Junhui Ji and Lihang Pan and Shuaiqi Duan and Weihan Wang and Yan Wang and Yean Cheng and Zehai He and Zhe Su and Zhen Yang and Ziyang Pan and Aohan Zeng and Baoxu Wang and Bin Chen and Boyan Shi and Changyu Pang and Chenhui Zhang and Da Yin and Fan Yang and Guoqing Chen and Jiazheng Xu and Jiale Zhu and Jiali Chen and Jing Chen and Jinhao Chen and Jinghao Lin and Jinjiang Wang and Junjie Chen and Leqi Lei and Letian Gong and Leyi Pan and Mingdao Liu and Mingde Xu and Mingzhi Zhang and Qinkai Zheng and Sheng Yang and Shi Zhong and Shiyu Huang and Shuyuan Zhao and Siyan Xue and Shangqin Tu and Shengbiao Meng and Tianshu Zhang and Tianwei Luo and Tianxiang Hao and Tianyu Tong and Wenkai Li and Wei Jia and Xiao Liu and Xiaohan Zhang and Xin Lyu and Xinyue Fan and Xuancheng Huang and Yanling Wang and Yadong Xue and Yanfeng Wang and Yanzi Wang and Yifan An and Yifan Du and Yiming Shi and Yiheng Huang and Yilin Niu and Yuan Wang and Yuanchang Yue and Yuchen Li and Yutao Zhang and Yuting Wang and Yu Wang and Yuxuan Zhang and Zhao Xue and Zhenyu Hou and Zhengxiao Du and Zihan Wang and Peng Zhang and Debing Liu and Bin Xu and Juanzi Li and Minlie Huang and Yuxiao Dong and Jie Tang},
      year={2025},
      eprint={2507.01006},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.01006}, 
}

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Safetensors

Model size

10B params

Tensor type

BF16

Model tree for alecccdd/GLM-4.6V-Flash-W8A8-INT8

Base model

zai-org/GLM-4.6V-Flash

Quantized

(43)

this model

Dataset used to train alecccdd/GLM-4.6V-Flash-W8A8-INT8

Papers for alecccdd/GLM-4.6V-Flash-W8A8-INT8

GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Paper • 2507.01006 • Published Jul 1, 2025 • 256

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Paper • 2210.17323 • Published Oct 31, 2022 • 10