Instructions to use tiny-random/glm-5.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/glm-5.3 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/glm-5.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiny-random/glm-5.3")
model = AutoModelForCausalLM.from_pretrained("tiny-random/glm-5.3", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/glm-5.3 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tiny-random/glm-5.3

SGLang

How to use tiny-random/glm-5.3 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 "tiny-random/glm-5.3" \
    --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": "tiny-random/glm-5.3",
		"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 "tiny-random/glm-5.3" \
        --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": "tiny-random/glm-5.3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/glm-5.3 with Docker Model Runner:
```
docker model run hf.co/tiny-random/glm-5.3
```

yujiepan commited on 10 days ago

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@@ -22,7 +22,7 @@ Structural comparison with `zai-org/GLM-5.3`:
 | MTP | One DSA/MoE layer with shared embedding/head | Same |
 | Checkpoint precision | BF16 plus E4M3 block-FP8 and FP32 scales | Same; 32x32 blocks for reduced axes |
-The indexer head remains 128-wide: vLLM's GLM DSA cache and fused-indexer path use a 128-wide cache layout. The source E4M3 format is retained with reduced 32x32 blocks; the TP=4 example selects vLLM's Triton block-FP8 GEMM because H20's automatic Humming kernel requires an output scale group of at least 64.
 Tokenizer and generation assets are copied unchanged from the pinned source revision.
 | File path | Size |

 | MTP | One DSA/MoE layer with shared embedding/head | Same |
 | Checkpoint precision | BF16 plus E4M3 block-FP8 and FP32 scales | Same; 32x32 blocks for reduced axes |
+The indexer head remains 128-wide: vLLM's GLM DSA cache and fused-indexer path use a 128-wide cache layout. The source E4M3 format is retained with reduced 32x32 blocks; the TP=4 example selects vLLM's Triton block-FP8 GEMM because H20's automatic Humming kernel requires an output scale group of at least 64. With that unchanged 32-wide FP8 K block, vLLM TP=4 partitions residual-width linear inputs by four. Both 32-wide and 64-wide candidates were generated and rejected at model load with `input_size_per_partition = 8` and `16` respectively, neither divisible by `block_k = 32`; 128 is therefore the smallest executable residual width for this required checkpoint/backend/TP combination.
 Tokenizer and generation assets are copied unchanged from the pinned source revision.
 | File path | Size |