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

Libraries

How to use zai-org/GLM-5.3-Flash with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-5.3-Flash")
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("zai-org/GLM-5.3-Flash")
model = AutoModelForMultimodalLM.from_pretrained("zai-org/GLM-5.3-Flash", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-5.3-Flash with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zai-org/GLM-5.3-Flash

SGLang

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

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

zRzRzRzRzRzRzR commited on about 8 hours ago

Commit

c54b8d1

1 Parent(s): edfc283

update

Browse files

Files changed (1) hide show

config.json +56 -5

config.json CHANGED Viewed

@@ -5,7 +5,6 @@
   "text_config": {
     "attention_bias": false,
     "attention_dropout": 0.0,
-    "bos_token_id": null,
     "dtype": "bfloat16",
     "eos_token_id": [
       154820,
@@ -124,12 +123,64 @@
       "deepseek_sparse_attention",
       "linear_attention"
     ],
-    "linear_conv_kernel_dim": 4,
-    "linear_head_dim": 128,
-    "linear_lower_bound": -5.0,
-    "linear_num_heads": 64,
     "max_position_embeddings": 1048576,
     "mhc": true,
     "mlp_layer_types": [
       "dense",
       "dense",

   "text_config": {
     "attention_bias": false,
     "attention_dropout": 0.0,
     "dtype": "bfloat16",
     "eos_token_id": [
       154820,
       "deepseek_sparse_attention",
       "linear_attention"
     ],
+    "linear_attn_config": {
+      "num_heads": 64,
+      "gate_lower_bound": -5.0,
+      "head_dim": 128,
+      "short_conv_kernel_size": 4,
+      "kda_layers": [
+        0,
+        1,
+        2,
+        4,
+        5,
+        6,
+        8,
+        9,
+        10,
+        12,
+        13,
+        14,
+        16,
+        17,
+        18,
+        20,
+        21,
+        22,
+        24,
+        25,
+        26,
+        28,
+        29,
+        30,
+        32,
+        33,
+        34,
+        36,
+        37,
+        38,
+        40,
+        41,
+        42,
+        44
+      ],
+      "full_attn_layers": [
+        3,
+        7,
+        11,
+        15,
+        19,
+        23,
+        27,
+        31,
+        35,
+        39,
+        43
+      ]
+    },
     "max_position_embeddings": 1048576,
     "mhc": true,
+    "mla_use_nope": true,
     "mlp_layer_types": [
       "dense",
       "dense",