Instructions to use adriabama06/GLM-4-9B-0414-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adriabama06/GLM-4-9B-0414-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="adriabama06/GLM-4-9B-0414-exl2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("adriabama06/GLM-4-9B-0414-exl2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use adriabama06/GLM-4-9B-0414-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adriabama06/GLM-4-9B-0414-exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adriabama06/GLM-4-9B-0414-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/adriabama06/GLM-4-9B-0414-exl2
- SGLang
How to use adriabama06/GLM-4-9B-0414-exl2 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 "adriabama06/GLM-4-9B-0414-exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adriabama06/GLM-4-9B-0414-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "adriabama06/GLM-4-9B-0414-exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adriabama06/GLM-4-9B-0414-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use adriabama06/GLM-4-9B-0414-exl2 with Docker Model Runner:
docker model run hf.co/adriabama06/GLM-4-9B-0414-exl2
How to use from
SGLangUse 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 "adriabama06/GLM-4-9B-0414-exl2" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "adriabama06/GLM-4-9B-0414-exl2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
EXL2 quants of GLM-4-9B-0414
4.5 bits per weight
6.0 bits per weight
| Model | Size |
|---|---|
| 4.5 bpw | 6.32 GB |
| 6.0 bpw | 7.85 GB |
Model tree for adriabama06/GLM-4-9B-0414-exl2
Base model
zai-org/GLM-4-9B-0414
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "adriabama06/GLM-4-9B-0414-exl2" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adriabama06/GLM-4-9B-0414-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'