Instructions to use RedHatAI/GLM-5.3-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/GLM-5.3-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/GLM-5.3-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/GLM-5.3-NVFP4")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/GLM-5.3-NVFP4", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/GLM-5.3-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/GLM-5.3-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/GLM-5.3-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/GLM-5.3-NVFP4

SGLang

How to use RedHatAI/GLM-5.3-NVFP4 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 "RedHatAI/GLM-5.3-NVFP4" \
    --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": "RedHatAI/GLM-5.3-NVFP4",
		"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 "RedHatAI/GLM-5.3-NVFP4" \
        --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": "RedHatAI/GLM-5.3-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/GLM-5.3-NVFP4 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/GLM-5.3-NVFP4
```

dsikka commited on Sep 23

Commit

ea2f938

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1 Parent(s): 6c27b83

Update README.md

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README.md CHANGED Viewed

@@ -126,11 +126,9 @@ seeds; AIME used eight seeds. Recovery is relative to the [base model](https://h
   </tbody>
 </table>
-Per-seed NVFP4 results:
-- GSM8K: 96.36%, 96.86%, 96.86%
-- IFEval: 85.95%, 85.95%, 85.21%
-- MATH-500: 96.00%, 96.60%, 96.20%
-- AIME 2025: 90.00%, 86.67%, 90.00%, 90.00%, 93.33%, 90.00%, 86.67%, 90.00%
-- GPQA Diamond: 90.40%, 91.41%, 89.39%

   </tbody>
 </table>
+## Performance Evaluation
+Performance evaluation for RedHatAI/GLM-5.3-NVFP4 with specualtive decoding. In the graph, interactivity is the median per-request output-token rate, aggregate throughput is total completed output tokens divided by measurement duration, and each label is completed requests divided by measurement duration.
+![glm53_math_reasoning](https://cdn-uploads.huggingface.co/production/uploads/650a157669739cd310382f88/yDDJe_95-w5NWjCaFxumL.png)