Instructions to use heterodoxin/granite-3.3-8b-instruct-apostate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use heterodoxin/granite-3.3-8b-instruct-apostate with Transformers:

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

pipe = pipeline("text-generation", model="heterodoxin/granite-3.3-8b-instruct-apostate")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("heterodoxin/granite-3.3-8b-instruct-apostate")
model = AutoModelForCausalLM.from_pretrained("heterodoxin/granite-3.3-8b-instruct-apostate")
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 heterodoxin/granite-3.3-8b-instruct-apostate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "heterodoxin/granite-3.3-8b-instruct-apostate"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "heterodoxin/granite-3.3-8b-instruct-apostate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/heterodoxin/granite-3.3-8b-instruct-apostate

SGLang

How to use heterodoxin/granite-3.3-8b-instruct-apostate 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 "heterodoxin/granite-3.3-8b-instruct-apostate" \
    --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": "heterodoxin/granite-3.3-8b-instruct-apostate",
		"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 "heterodoxin/granite-3.3-8b-instruct-apostate" \
        --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": "heterodoxin/granite-3.3-8b-instruct-apostate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use heterodoxin/granite-3.3-8b-instruct-apostate with Docker Model Runner:
```
docker model run hf.co/heterodoxin/granite-3.3-8b-instruct-apostate
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

granite-3.3-8b-instruct Apostate

Join the community: Discord

An uncensored edit of ibm-granite/granite-3.3-8b-instruct. Refusal behavior is removed by editing the model weights directly — no finetuning, no adapter, no runtime hook. The result is a standard Transformers checkpoint that drops in anywhere the base model works.

Produced with Apostate.

Method

Apostate finds the residual-stream direction most responsible for refusal behavior and permanently projects it out of the model's weights. The edit targets the writer side: per layer, the refusal direction is removed from the weight matrices of every module that writes to the residual stream (attention output projections and MLP down-projections).

The operator is a contrastive co-vector edit E = I − R Dᵀ. Removing the refusal direction outright disturbs benign behavior, while naively preserving all harmless variance along it leaves the refusal that is entangled with general behavior intact. Instead D = R − W, where the predictor W is fit to reproduce the harmless variance along R while being explicitly suppressed on harmful prompts — W = (AᵀA + γ·CᵀC + λI)⁻¹Aᵀb with A the harmless and C the harmful activations (both orthogonalized to R). The edit thus keeps the harmless-specific component and removes the component shared with refusal, driving refusal down while keeping the change to harmless behavior (KL) small. This holds even on architectures with residual/embedding scaling multipliers (e.g. Granite), where mean-preserving oblique ablation under-ablates.

The refusal subspace is found via TPE search with causal layer importance scoring to concentrate edits where they most influence refusal generation.

Results

Evaluated on held-out prompts from JailbreakBench and the harmful_behaviors test split. Refusal is scored by a classifier with a weak-compliance guard; KL measures token-distribution shift on harmless prompts.

Metric	Base	Apostate
Refusal rate	96.0%	5.0%
Comply rate	—	95.0%
Harmless KL (nats)	0	0.081

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "heterodoxin/granite-3.3-8b-instruct-apostate"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Your prompt here"}]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tok.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Notes

This is an uncensored model. It will respond to requests the base model refuses.
The edit is baked into the weights permanently; no system prompt or adapter is required.
See ibm-granite/granite-3.3-8b-instruct for base model capabilities and license.

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Model size

8B params

Tensor type

BF16

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