Instructions to use QuantTrio/GLM-5.2-Int4-Int8Mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/GLM-5.2-Int4-Int8Mix with Transformers:

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

pipe = pipeline("text-generation", model="QuantTrio/GLM-5.2-Int4-Int8Mix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantTrio/GLM-5.2-Int4-Int8Mix")
model = AutoModelForCausalLM.from_pretrained("QuantTrio/GLM-5.2-Int4-Int8Mix")
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 Settings

vLLM

How to use QuantTrio/GLM-5.2-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-5.2-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-5.2-Int4-Int8Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/GLM-5.2-Int4-Int8Mix

SGLang

How to use QuantTrio/GLM-5.2-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-5.2-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-5.2-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-5.2-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-5.2-Int4-Int8Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/GLM-5.2-Int4-Int8Mix with Docker Model Runner:
```
docker model run hf.co/QuantTrio/GLM-5.2-Int4-Int8Mix
```

JunHowie commited on 21 days ago

Commit

ae7872f

verified ·

1 Parent(s): 8e7a305

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ base_model_relation: quantized
 ---
 # GLM-5.2-Int4-Int8Mix
-Base model: [ZhipuAI/GLM-5.2](https://www.modelscope.cn/models/ZhipuAI/GLM-5.2)
 This repo quantizes the model using a data-free quantization tool.
@@ -110,8 +110,8 @@ The first launch may spend several minutes on vLLM compilation and warmup.
 ### 【Model Download】
 ```python
-from modelscope import snapshot_download
-snapshot_download('tclf90/GLM-5.2-Int4-Int8Mix', cache_dir="your_local_path")
 ```
 ### 【Overview】

 ---
 # GLM-5.2-Int4-Int8Mix
+Base model: [zai-org/GLM-5.2](https://huggingface.co/zai-org/GLM-5.2)
 This repo quantizes the model using a data-free quantization tool.
 ### 【Model Download】
 ```python
+from huggingface_hub import snapshot_download
+snapshot_download('QuantTrio/GLM-5.2-Int4-Int8Mix', cache_dir="your_local_path")
 ```
 ### 【Overview】