Instructions to use audnai/penclaw-GLM-5.3-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use audnai/penclaw-GLM-5.3-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="audnai/penclaw-GLM-5.3-abliterated")
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("audnai/penclaw-GLM-5.3-abliterated")
model = AutoModelForCausalLM.from_pretrained("audnai/penclaw-GLM-5.3-abliterated", 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 audnai/penclaw-GLM-5.3-abliterated with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/audnai/penclaw-GLM-5.3-abliterated

SGLang

How to use audnai/penclaw-GLM-5.3-abliterated 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 "audnai/penclaw-GLM-5.3-abliterated" \
    --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": "audnai/penclaw-GLM-5.3-abliterated",
		"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 "audnai/penclaw-GLM-5.3-abliterated" \
        --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": "audnai/penclaw-GLM-5.3-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use audnai/penclaw-GLM-5.3-abliterated with Docker Model Runner:
```
docker model run hf.co/audnai/penclaw-GLM-5.3-abliterated
```

Access request for security checks in a theoretical physics lab

by tachibanahageage - opened about 9 hours ago

Discussion

tachibanahageage

about 9 hours ago

Hello,

I submitted an access request on October 9, but it is still marked PENDING.

I am part of a university theoretical physics laboratory and would like to evaluate this model locally for authorized security checks of our lab's shared computing systems.

Could you please review my access request, or let me know if any additional information is needed?

Thank you for your work on this model.

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