Instructions to use Rootkit7/GLM-4-9B-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rootkit7/GLM-4-9B-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="Rootkit7/GLM-4-9B-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rootkit7/GLM-4-9B-abliterated")
model = AutoModelForCausalLM.from_pretrained("Rootkit7/GLM-4-9B-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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rootkit7/GLM-4-9B-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rootkit7/GLM-4-9B-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": "Rootkit7/GLM-4-9B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rootkit7/GLM-4-9B-abliterated

SGLang

How to use Rootkit7/GLM-4-9B-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 "Rootkit7/GLM-4-9B-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": "Rootkit7/GLM-4-9B-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 "Rootkit7/GLM-4-9B-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": "Rootkit7/GLM-4-9B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rootkit7/GLM-4-9B-abliterated with Docker Model Runner:
```
docker model run hf.co/Rootkit7/GLM-4-9B-abliterated
```

GLM-4-9B-chat — abliterated (Solutus)

Refusal-abliterated THUDM/glm-4-9b-chat-hf, produced with Solutus (measurement-first LLM abliteration). Private research artifact — outputs are the base model's, minus the refusal behavior; use responsibly.

Recipe

directional (single refusal direction), whitened-SVD extraction, byte-exact (batch_size=1):

solutus abliterate THUDM/glm-4-9b-chat-hf --technique directional \
  --dataset advbench,harmbench,multijail_zh,sorrybench \
  -o extraction=whitened_svd -o n_directions=1 --max-new-tokens 512

GLM-4's refusal is low-dimensional — a single direction removes it cleanly; n_directions=4 over-ablated (WikiText ΔPPL +30% vs +9.8% smoke), so n_directions=1 is the capability-preserving recipe.

Measured (Solutus eval, held-out; base refusal = 100%)

Axis	Result
refusal — advbench / harmbench	15.6% / 6.2%
refusal — MultiJail zh / ar / sw	0% / 3.1% / 0%
over-refusal — orbench_hard (benign)	0% refusal (stays benign-compliant)
coherent-compliance	~90–100% (see Swahili caveat)
capability — WikiText-2 ΔPPL	−0.4% (base 29.13 → 29.00 — no degradation)
capability — GSM8K / MMLU (n=100)	59.0% / 67.0%

Honest caveats

Swahili degeneration is base-inherent, not from abliteration. On MultiJail-Swahili the abliterated model is 59% degenerate — but base GLM-4-9B is already 56% degenerate on Swahili (a low-resource language this CN/EN model handles poorly). The edit barely moved it.
KL divergence is not a reliable signal for GLM-4. Its 151k-token vocab makes the neutral-prompt softmax extremely peaked, so KL reads ~1e-8 (six orders below other models) even for a real edit — the in-run KL guard is inert here. Capability was therefore judged on ΔPPL (WikiText-2) + GSM8K/MMLU, not KL.
Extraction/eval used advbench, harmbench, MultiJail (zh/ar/sw), sorrybench, orbench_hard.

Base model © THUDM (GLM-4). See the base model card for its license and usage terms.

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for Rootkit7/GLM-4-9B-abliterated

Base model

zai-org/glm-4-9b-chat

Finetuned

zai-org/glm-4-9b-chat-hf

Finetuned

(3)

this model