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

Libraries

How to use Mapika/GLM-5.2-NVFP4 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mapika/GLM-5.2-NVFP4")
model = AutoModelForCausalLM.from_pretrained("Mapika/GLM-5.2-NVFP4")
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]:]))

TensorRT

How to use Mapika/GLM-5.2-NVFP4 with TensorRT:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mapika/GLM-5.2-NVFP4 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Mapika/GLM-5.2-NVFP4

SGLang

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

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

Mapika commited on about 1 month ago

Commit

5f9c62a

verified ·

1 Parent(s): 615783d

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +10 -2

README.md CHANGED Viewed

@@ -68,8 +68,16 @@ docker run --runtime=nvidia --gpus '"device=0,1,2,3"' --ipc=host --shm-size=32g
     --tool-call-parser auto --trust-remote-code --host 0.0.0.0 --port 30000
 ```
-Fits on 4× ≥80 GB GPUs at TP=4 (~110 GB/GPU). Use a generous `max_tokens` at inference — GLM-5.2 is a
-reasoning model and its `<think>` chains can be long.
 ## Notes

     --tool-call-parser auto --trust-remote-code --host 0.0.0.0 --port 30000
 ```
+**GPU memory.** The weights are ~410 GB, so per-GPU footprint depends on TP:
+| Tensor parallel | Weights / GPU | Suitable GPUs |
+|---|---|---|
+| `--tp 4` | ~110 GB | ≥128 GB cards — H200 (141 GB, tight KV), B200 / B300, MI300X (192 GB) |
+| `--tp 8` | ~55 GB | 80 GB cards — 8× H100 or A100-80GB |
+So **80 GB GPUs need `--tp 8`, not `--tp 4`** (110 GB of weights can't fit in an 80 GB card). Lower
+`--mem-fraction-static` if KV-cache space is tight. Use a generous `max_tokens` at inference — GLM-5.2 is
+a reasoning model and its `<think>` chains can be long.
 ## Notes