Instructions to use JANGQ-AI/GLM-5.3-Flash-W4A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JANGQ-AI/GLM-5.3-Flash-W4A16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="JANGQ-AI/GLM-5.3-Flash-W4A16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("JANGQ-AI/GLM-5.3-Flash-W4A16")
model = AutoModelForMultimodalLM.from_pretrained("JANGQ-AI/GLM-5.3-Flash-W4A16", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JANGQ-AI/GLM-5.3-Flash-W4A16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JANGQ-AI/GLM-5.3-Flash-W4A16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JANGQ-AI/GLM-5.3-Flash-W4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/JANGQ-AI/GLM-5.3-Flash-W4A16

SGLang

How to use JANGQ-AI/GLM-5.3-Flash-W4A16 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 "JANGQ-AI/GLM-5.3-Flash-W4A16" \
    --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": "JANGQ-AI/GLM-5.3-Flash-W4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "JANGQ-AI/GLM-5.3-Flash-W4A16" \
        --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": "JANGQ-AI/GLM-5.3-Flash-W4A16",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use JANGQ-AI/GLM-5.3-Flash-W4A16 with Docker Model Runner:
```
docker model run hf.co/JANGQ-AI/GLM-5.3-Flash-W4A16
```

GLM-5.3-Flash-W4A16

GLM-5.3-Flash — ~195 GB on disk (down from the ~643 GB BF16 source) — INT4 W4A16 quantization of the routed experts in compressed-tensors pack-quantized format, for Hopper (H100 / H200) via vLLM Marlin kernels.

Source: zai-org/GLM-5.3-Flash — the glm5_next architecture: a multimodal hybrid model (vision tower + mixed linear/full attention + MoE), 46 text layers, 288 routed experts top-8, 1 shared expert, MTP head at layer 45.
Quantization: INT4 symmetric, group-32, RTN, on the routed-expert weights only (model.language_model.layers.3..44.mlp.experts.*.{gate,up,down}_proj), packed bit-exactly with the compressed-tensors library's pack_to_int32 (weight_packed int32 / weight_scale bf16 / weight_shape int64).
A16 = activations, attention, and KV cache stay 16-bit. Only expert weight storage is 4-bit.
Kept at BF16 (source precision): the vision tower (model.visual.*), all attention including the linear-attention params (A_log, dt_bias) and the DSA-style indexer, the shared expert, dense MLP (layers 0–2), router gates, RMSNorms, embeddings, lm_head, and the MTP head (layer 45 eh_proj/enorm/hnorm).
Bundle: ~195 GB · 37,152 expert tensors quantized (verified == expected), MTP + vision preserved.

What's in the bundle

Module	Source dtype	Bundle dtype
Routed experts (288 × 3 × MoE layers 3–44)	BF16	INT4 sym g32 (`weight_packed`/`weight_scale`/`weight_shape`)
Vision tower (`model.visual.*`)	BF16	BF16
Attention (full + linear `A_log`/`dt_bias`, indexer)	BF16	BF16
Shared expert, dense MLP (layers 0–2)	BF16	BF16
Router gates, norms, embed, `lm_head`, MTP (layer 45)	BF16 / F32	BF16 / F32

Dequant per group of 32: w = int4 * weight_scale (scale = amax/7.5, range [-8,7]).

Usage

vllm serve JANGQ-AI/GLM-5.3-Flash-W4A16 --tensor-parallel-size 4 --trust-remote-code

Runtime note: glm5_next is a very new hybrid multimodal architecture. Confirm your serving stack (vLLM / SGLang build) has Glm5Next support before deploying; the weights here are standard compressed-tensors W4A16 and load via the Marlin path once the architecture is recognized.

Method

Streaming shard-by-shard RTN quantizer (one tensor in memory at a time — no GPU, no large host RAM), architecture-agnostic: the routed-expert tensor set is taken from the model's own index, packed via compressed_tensors.pack_to_int32 for a bit-exact on-disk layout. Data-free (no calibration). Every layer verified present (0–45), MTP + vision preserved, expert count checked against the source index.

Quantized by JANGQ-AI.

Downloads last month: -

Safetensors

Model size

321B params

Tensor type

F32

I32

BF16

Model tree for JANGQ-AI/GLM-5.3-Flash-W4A16

Base model

zai-org/GLM-5.3-Flash

Quantized

(71)

this model

Quantizations

2 models