Instructions to use QuantTrio/GLM-5-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/GLM-5-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="QuantTrio/GLM-5-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantTrio/GLM-5-AWQ")
model = AutoModelForCausalLM.from_pretrained("QuantTrio/GLM-5-AWQ")
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 QuantTrio/GLM-5-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/GLM-5-AWQ"
# 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-5-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/GLM-5-AWQ

SGLang

How to use QuantTrio/GLM-5-AWQ 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-5-AWQ" \
    --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-5-AWQ",
		"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-5-AWQ" \
        --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-5-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/GLM-5-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/GLM-5-AWQ
```

JunHowie commited on Feb 28

Commit

a838c55

verified ·

1 Parent(s): 98c1be3

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -6,12 +6,12 @@ tags:
 - vLLM
 - AWQ
 base_model:
-  - ZhipuAI/GLM-5
 base_model_relation: quantized
 ---
 # GLM-5-AWQ
-Base model: [ZhipuAI/GLM-5](https://www.modelscope.cn/models/ZhipuAI/GLM-5)
 This repo quantizes the model using data-free quantization (no calibration dataset required).
@@ -47,7 +47,7 @@ export VLLM_USE_FLASHINFER_SAMPLER=0
 export OMP_NUM_THREADS=4
 vllm serve \
-    __YOUR_PATH__/tclf90/GLM-5-AWQ \
     --served-model-name MY_MODEL \
     --swap-space 16 \
     --max-num-seqs 32 \
@@ -77,8 +77,8 @@ vllm serve \
 ### 【Model Download】
 ```python
-from modelscope import snapshot_download
-snapshot_download('tclf90/GLM-5-AWQ', cache_dir="your_local_path")
 ```
 ### 【Overview】

 - vLLM
 - AWQ
 base_model:
+  - zai-org/GLM-5
 base_model_relation: quantized
 ---
 # GLM-5-AWQ
+Base model: [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5)
 This repo quantizes the model using data-free quantization (no calibration dataset required).
 export OMP_NUM_THREADS=4
 vllm serve \
+    __YOUR_PATH__/QuantTrio/GLM-5-AWQ \
     --served-model-name MY_MODEL \
     --swap-space 16 \
     --max-num-seqs 32 \
 ### 【Model Download】
 ```python
+from huggingface_hub import snapshot_download
+snapshot_download('QuantTrio/GLM-5-AWQ', cache_dir="your_local_path")
 ```
 ### 【Overview】