Instructions to use RedHatAI/GLM-5.2-NVFP4-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/GLM-5.2-NVFP4-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/GLM-5.2-NVFP4-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/GLM-5.2-NVFP4-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/GLM-5.2-NVFP4-FP8", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/GLM-5.2-NVFP4-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/GLM-5.2-NVFP4-FP8"
# 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-5.2-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/GLM-5.2-NVFP4-FP8

SGLang

How to use RedHatAI/GLM-5.2-NVFP4-FP8 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-5.2-NVFP4-FP8" \
    --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-5.2-NVFP4-FP8",
		"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-5.2-NVFP4-FP8" \
        --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-5.2-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/GLM-5.2-NVFP4-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/GLM-5.2-NVFP4-FP8
```

kylesayrs commited on Jul 7

Commit

411e3c2

verified ·

1 Parent(s): f6c4264

Update README.md

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README.md CHANGED Viewed

@@ -25,6 +25,8 @@ vllm serve RedHatAI/GLM-5.2-FP8-NVFP4 --tensor_parallel_size 4 --kv_cache_dtype=
 This model was created using [LLM Compressor](https://github.com/vllm-project/llm-compressor). The example script can be found in `examples/quantizing_moe/glm5_example.py` [[Example] GLM5.2 Example](https://github.com/vllm-project/llm-compressor/pull/2869). Quantizing the model with data parallelism and 6xA100 takes about 3 hours.
 ```python
 import torch
 from compressed_tensors.offload import init_dist
@@ -135,4 +137,11 @@ model.save_pretrained(SAVE_DIR, save_compressed=True)
 tokenizer.save_pretrained(SAVE_DIR)
 torch.distributed.destroy_process_group()
-```

 This model was created using [LLM Compressor](https://github.com/vllm-project/llm-compressor). The example script can be found in `examples/quantizing_moe/glm5_example.py` [[Example] GLM5.2 Example](https://github.com/vllm-project/llm-compressor/pull/2869). Quantizing the model with data parallelism and 6xA100 takes about 3 hours.
+<details><summary>LLM Compressor Creation Script</summary>
 ```python
 import torch
 from compressed_tensors.offload import init_dist
 tokenizer.save_pretrained(SAVE_DIR)
 torch.distributed.destroy_process_group()
+```
+</details>
+## Evaluation ##
+| Benchmark | `zai-org/GLM-5.2` | `RedHatAI/GLM-5.2-NVFP4-FP8` |
+| - | - | - |
+| GPQA-Diamond | 91.2 | 89.1 |