Instructions to use 0xSero/GLM-5-381B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/GLM-5-381B with Transformers:

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

pipe = pipeline("text-generation", model="0xSero/GLM-5-381B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0xSero/GLM-5-381B")
model = AutoModelForCausalLM.from_pretrained("0xSero/GLM-5-381B")
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

vLLM

How to use 0xSero/GLM-5-381B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/GLM-5-381B"
# 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-5-381B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/GLM-5-381B

SGLang

How to use 0xSero/GLM-5-381B 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-5-381B" \
    --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-5-381B",
		"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-5-381B" \
        --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-5-381B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/GLM-5-381B with Docker Model Runner:
```
docker model run hf.co/0xSero/GLM-5-381B
```

Support this work → · X · GitHub · REAP paper · Cerebras REAP

GLM-5-381B

REAP-pruned zai-org/GLM-5.

At a glance


Base model	zai-org/GLM-5
Format	BF16
Total params	381B
Active / token	—
Experts / layer	128
Layers	78
Hidden size	6144
Context	202,752
On-disk size	1147 GB

Which variant should I pick?

Variant	Format	Link
`GLM-5-381B` (this)	BF16	link
`GLM-5-381B-GGUF-BF16`	GGUF	link
`GLM-5-381B-GGUF-IQ2_M`	GGUF	link
`GLM-5-381B-GGUF-IQ2_XXS`	GGUF	link
`GLM-5-381B-GGUF-Q3_K_M`	GGUF	link
`GLM-5-381B-W3A16`	W3A16	link
`glm5-reap-observations`	BF16	link

This repository now hosts the BF16 GLM-5 checkpoint produced by a 50% REAP prune. The actual checkpoint contents are the BF16 files described below.

Checkpoint

Base model: GLM-5-BF16
Architecture: GlmMoeDsaForCausalLM
Method: refusal_contrast_reap
Compression ratio: 0.50
Seed: 42
Router renormalization: true
Parameters: 381,464,351,232
Total safetensors size: 762,928,740,864 bytes
Shards: 17
Precision: BF16

Provenance

Observation run: glm5-grouped-22k-20260331T172330Z
Calibration dataset: combined
Prune output directory: /data0/external_research/glm5-layerwise-reap-artifacts/GLM-5-BF16/combined/pruned_models/layerwise_refusal_contrast_reap-renorm_true-seed_42-0.50

Files

model-00001-of-00017.safetensors through model-00017-of-00017.safetensors
model.safetensors.index.json
config.json
generation_config.json
chat_template.jinja
tokenizer.json
tokenizer_config.json
reap_layerwise_args.yaml

Notes

This upload replaces the older multi-shard checkpoint previously hosted in this repo.
The metadata above reflects the actual checkpoint contents as of 2026-04-05.

License & citation

License inherited from the base model.

@misc{lasby2025reap,
  title  = {REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression},
  author = {Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}

Model tree for 0xSero/GLM-5-381B

Base model

zai-org/GLM-5

Finetuned

(37)

this model

Finetunes

1 model

Space using 0xSero/GLM-5-381B 1

Paper for 0xSero/GLM-5-381B

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 20

0xSero
/

GLM-5-381B