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
```

glm-5.3

File size: 2,405 Bytes

0f37c36

{
  "architectures": [
    "GlmMoeDsaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "dtype": "bfloat16",
  "eos_token_id": [
    154820,
    154827,
    154829
  ],
  "ep_size": 1,
  "first_k_dense_replace": 3,
  "head_dim": 192,
  "hidden_act": "silu",
  "hidden_size": 128,
  "index_head_dim": 128,
  "index_n_heads": 32,
  "index_share_for_mtp_iteration": true,
  "index_skip_topk_offset": 3,
  "index_topk": 128,
  "index_topk_freq": 4,
  "index_topk_pattern": null,
  "indexer_rope_interleave": true,
  "indexer_types": [
    "full",
    "full",
    "full",
    "shared",
    "shared",
    "shared",
    "full",
    "shared",
    "shared",
    "shared",
    "full"
  ],
  "initializer_range": 0.02,
  "intermediate_size": 128,
  "kv_lora_rank": 256,
  "max_position_embeddings": 1048576,
  "mlp_layer_types": [
    "dense",
    "dense",
    "dense",
    "sparse",
    "sparse",
    "sparse",
    "sparse",
    "sparse",
    "sparse",
    "sparse",
    "sparse"
  ],
  "model_type": "glm_moe_dsa",
  "moe_intermediate_size": 128,
  "moe_layer_freq": 1,
  "moe_router_dtype": "float32",
  "n_group": 1,
  "n_routed_experts": 32,
  "n_shared_experts": 1,
  "norm_topk_prob": true,
  "num_attention_heads": 64,
  "num_experts_per_tok": 8,
  "num_hidden_layers": 11,
  "num_key_value_heads": 64,
  "num_nextn_predict_layers": 1,
  "pad_token_id": 154820,
  "pretraining_tp": 1,
  "q_lora_rank": 128,
  "qk_head_dim": 256,
  "qk_nope_head_dim": 32,
  "qk_rope_head_dim": 32,
  "rms_norm_eps": 1e-05,
  "rope_interleave": true,
  "rope_parameters": {
    "rope_theta": 8000000,
    "rope_type": "default"
  },
  "routed_scaling_factor": 2.5,
  "scoring_func": "sigmoid",
  "tie_word_embeddings": false,
  "topk_group": 1,
  "topk_method": "noaux_tc",
  "transformers_version": "5.15.0",
  "use_cache": true,
  "v_head_dim": 256,
  "vocab_size": 154880,
  "quantization_config": {
    "activation_scheme": "dynamic",
    "fmt": "e4m3",
    "quant_method": "fp8",
    "weight_block_size": [
      32,
      32
    ],
    "modules_to_not_convert": [
      "lm_head",
      "model.embed_tokens",
      "model.layers.0.self_attn.indexer.weights_proj",
      "model.layers.1.self_attn.indexer.weights_proj",
      "model.layers.10.self_attn.indexer.weights_proj",
      "model.layers.2.self_attn.indexer.weights_proj",
      "model.layers.6.self_attn.indexer.weights_proj"
    ]
  }
}