Instructions to use 0xSero/GLM-5.1-555B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/GLM-5.1-555B with Transformers:

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

pipe = pipeline("text-generation", model="0xSero/GLM-5.1-555B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0xSero/GLM-5.1-555B")
model = AutoModelForCausalLM.from_pretrained("0xSero/GLM-5.1-555B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 0xSero/GLM-5.1-555B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/GLM-5.1-555B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xSero/GLM-5.1-555B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/GLM-5.1-555B

SGLang

How to use 0xSero/GLM-5.1-555B 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 "0xSero/GLM-5.1-555B" \
    --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": "0xSero/GLM-5.1-555B",
		"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 "0xSero/GLM-5.1-555B" \
        --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": "0xSero/GLM-5.1-555B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/GLM-5.1-555B with Docker Model Runner:
```
docker model run hf.co/0xSero/GLM-5.1-555B
```

GLM-5.1-555B / prune_metadata.json

0xSero

Upload prune_metadata.json with huggingface_hub

8ac3d31 verified 5 months ago

Raw

History Blame

540 Bytes

	{
	"source_model": "zai-org/GLM-5.1",
	"compression_ratio": 0.25,
	"prune_method": "reap",
	"experts_pruned_per_layer": 64,
	"total_experts_per_layer": 256,
	"observer_batches": "6144/6999",
	"observer_samples": "7707/22000",
	"observer_tokens": 85851690,
	"params_before_B": 743.91,
	"params_after_B": 562.69,
	"prune_time_s": 1145.3,
	"WARNING": "UNVERIFIED - pruned from partial observations (35% samples). Not benchmarked. May produce incoherent output. Must validate before any use.",
	"saved_at": "2026-04-13T23:05:14Z"
	}