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

Libraries

How to use modal-labs/GLM-5.3-NVFP4 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modal-labs/GLM-5.3-NVFP4")
model = AutoModelForCausalLM.from_pretrained("modal-labs/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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/modal-labs/GLM-5.3-NVFP4

SGLang

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

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

GLM-5.3-NVFP4

NVFP4 quantization of Z.ai's zai-org/GLM-5.3, produced from the BF16 release (zai-org/GLM-5.3-BF16) with NVIDIA Model Optimizer. Run it on Modal.

Base model: zai-org/GLM-5.3-BF16 (revision 861dfa23)
Quantization: NVFP4 weights and activations on the routed expert linears, FP8 KV cache
Producer: Model Optimizer 0.47.0.dev (upstream 5500999d0), general/ptq/nvfp4_experts_only-kv_fp8_cast recipe
Size: 465 GB (47 shards)

Quantization Method

Only the linear operators of the routed MoE experts (gate_proj, up_proj, down_proj in model.layers.{3..77}.mlp.experts.*) are quantized, to NVFP4 (4-bit float, group size 16, two-level scaling: per-16-element FP8 block scales plus a per-tensor global scale). Expert activations use NVFP4 with static, max-calibrated per-expert input scales.

Kept in BF16: all attention (MLA projections and the DSA indexer), shared experts, the first three dense-MLP layers (model.layers.{0..2}), the MTP/NextN layer (model.layers.78, usable for speculative decoding), lm_head, and embeddings. The KV cache is FP8 (E4M3) in cast mode (unit scale, no data-driven KV calibration). The full per-layer quantizer map is in .quant_summary.txt. The exclusion list is in hf_quant_config.json.

Calibration

Calibrated with max calibration on 1,024 samples at sequence length 1,024. The calibration set covers general text, chat, code, software engineering, agentic tool use, and math: cnn_dailymail (256 samples), Nemotron-SFT-Instruction-Following-Chat-v2 (192), OpenCodeReasoning (256), Nemotron-SFT-SWE-v2 (128), Nemotron-SFT-Agentic-v2 (128), and Nemotron-Math-v2 (64). Chat data was formatted with the model's chat template, with tool definitions included.

Deployment

Run it on Modal. Use the model's recommended sampling (temperature=1.0, top_p=0.95).

License

Quantized version of GLM-5.3, distributed under Z.ai's GLM-5.3 License, inherited from the base model.

Downloads last month: 20

Safetensors

Model size

381B params

Tensor type

F32

BF16

Model tree for modal-labs/GLM-5.3-NVFP4

Base model

zai-org/GLM-5.3-BF16

Quantized

(8)

this model