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 3 days ago

Commit

8b1b9cf

verified ·

1 Parent(s): f104289

Add checkpoint parameter and size comparison

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -12,6 +12,8 @@ Structural comparison with `zai-org/GLM-5.3`:
 |---|---:|---:|
 | Architecture / input | GLM-MoE-DSA; text | Same |
 | Vocabulary rows / tied embeddings | 154,880 / false | Same |
 | Backbone layers | 78 | 11: three dense, then two complete shared/shared/shared/full cycles |
 | Hidden size | 6,144 | 128 |
 | Attention heads / NoPE / RoPE / value dimensions | 64 / 192 / 64 / 256 | 64 / 32 / 32 / 256 |

 |---|---:|---:|
 | Architecture / input | GLM-MoE-DSA; text | Same |
 | Vocabulary rows / tied embeddings | 154,880 / false | Same |
+| Checkpoint tensor elements | 753.33B | 146.57M, including 104.21K FP32 scale elements |
+| Safetensors weight files | 755.63 GB across shards | 186.88 MB (`model.safetensors`) |
 | Backbone layers | 78 | 11: three dense, then two complete shared/shared/shared/full cycles |
 | Hidden size | 6,144 | 128 |
 | Attention heads / NoPE / RoPE / value dimensions | 64 / 192 / 64 / 256 | 64 / 32 / 32 / 256 |