Instructions to use QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix
- SGLang
How to use QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix 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 "QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix" \ --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": "QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix", "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 "QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix" \ --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": "QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix with Docker Model Runner:
docker model run hf.co/QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix
Download tokenizer.json from QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix/resolve/main/tokenizer.json
- Command line
-
hf download hf://QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/QuantTrio/GLM-4.1V-9B-Thinking-GPTQ-Int4-Int8Mix/resolve/main/tokenizer.json
20 MB
- Xet hash:
- bddd927d3371769af4888167d01a92e40d9021ba0dce475fc497738ebaca4245
- Size of remote file:
- 20 MB
- SHA256:
- 76ebeac0d8bd7879ead7b43c16b44981f277e47225de2bd7de9ae1a6cc664a8c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.