Instructions to use K0D3IN/MiniCPM5-1B-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use K0D3IN/MiniCPM5-1B-heretic with Transformers:

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

pipe = pipeline("text-generation", model="K0D3IN/MiniCPM5-1B-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("K0D3IN/MiniCPM5-1B-heretic")
model = AutoModelForCausalLM.from_pretrained("K0D3IN/MiniCPM5-1B-heretic")
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 K0D3IN/MiniCPM5-1B-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "K0D3IN/MiniCPM5-1B-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K0D3IN/MiniCPM5-1B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/K0D3IN/MiniCPM5-1B-heretic

SGLang

How to use K0D3IN/MiniCPM5-1B-heretic 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 "K0D3IN/MiniCPM5-1B-heretic" \
    --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": "K0D3IN/MiniCPM5-1B-heretic",
		"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 "K0D3IN/MiniCPM5-1B-heretic" \
        --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": "K0D3IN/MiniCPM5-1B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use K0D3IN/MiniCPM5-1B-heretic with Docker Model Runner:
```
docker model run hf.co/K0D3IN/MiniCPM5-1B-heretic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MiniCPM5-1B-Heretic

This repository contains a surgically de-censored version of Openbmb's MiniCPM5 1B model, optimized via weight abliteration techniques. By applying the heretic framework across an extensive 2000-trial search space, we successfully isolated and neutralized the primary refusal vectors embedded within the attn.o_proj and mlp.down_proj layers.

Abliteration Parameters:

Parameter	Value
direction_index	12.75
attn.o_proj.max_weight	1.50
attn.o_proj.max_weight_position	13.92
attn.o_proj.min_weight	0.82
attn.o_proj.min_weight_distance	7.62
mlp.down_proj.max_weight	1.26
mlp.down_proj.max_weight_position	19.97
mlp.down_proj.min_weight	0.98
mlp.down_proj.min_weight_distance	11.51

Highlights & Metrics

Metric	This model	Original model (openbmb/MiniCPM5-1B)
KL Divergence	0.0361	0 (By definition)
Refusals	3/100	96/100

Optimal Balance: Selected Trial 126 out of 2000 iterations for the perfect trade-off between freedom and reasoning capabilities.
Refusal Rate: Dropped down to 3/100 (from the original near-total refusal on safety benchmarks).
KL Divergence: 0.0361 - Demonstrates that general language capabilities are preserved relative to the original model. However, safety-aligned weights in attn.o_proj and mlp.down_proj have been surgically removed; this is intentional modification, not unintended degradation.

Benchmark Results

We believe in radical transparency. Instead of just claiming "uncensored", we evaluated both the vanilla model and our Heretic variant side-by-side:

Benchmark	Metricㅤ	Vanilla MiniCPM5 1B	MiniCPM5 1B Heretic (Ours)	Delta (Intelligence Kept)
GSM8K	0-shot	39.04	40.11	+2.74%
HellaSwag	5-shot	47.71	47.49	-0.45%
MMLU	0-shot	53.24	52.83	-0.77%

Technical Implementation Notes

Unlike aggressive fine-tuning which often degrades the model's core logic or shifts its grammar distributions, this weight manipulation directly zeros out the activation steering directions that cause alignment blocks.

Direction Index: 12.75 (Static allocation)*

Retained Capabilities: Excellent at structured JSON outputs, creative writing, coding logic and tool usage without preachy moral lectures.

Also: This model retains its exceptional thinking mode capabilities, which enable structured <think></think> reasoning blocks. Abliteration does not affect the thinking mechanism.

CRITICAL SAFETY NOTICE

This model has had its safety mechanisms surgically removed.

This is NOT a safe model for deployment. The refusal mechanisms that normally prevent harmful outputs have been abliterated. This model:

WILL generate:

Detailed instructions for illegal activities (drug synthesis, weapons, hacking)
Hateful, discriminatory, and abusive content
Graphic violence and sexual abuse material descriptions
Misinformation and harmful medical/legal advice
Phishing, social engineering, and scam tactics
Content violating laws in multiple jurisdictions

DOES NOT:

Refuse harmful requests (3/100 refusal rate)
Implement safety guardrails
Consider ethical implications
Respect content policies

APPROPRIATE USE CASES (ONLY):

Adversarial research on AI alignment and jailbreak vectors
Red-teaming and vulnerability disclosure by trained security researchers
Academic safety research with institutional ethics review
Understanding failure modes in alignment techniques

INAPPROPRIATE USE:

Production deployments
User-facing applications
Creating harmful content at scale
Bypassing security measures in systems
Any use case intended to cause harm

Legal Disclaimer: Users are solely responsible for downstream use. Deploying this model in violation of applicable laws or terms of service is illegal and unethical.

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for K0D3IN/MiniCPM5-1B-heretic

Base model

openbmb/MiniCPM5-1B

Finetuned

(12)

this model

Quantizations

2 models