Instructions to use richardyoung/zephyr-7b-beta-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use richardyoung/zephyr-7b-beta-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="richardyoung/zephyr-7b-beta-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("richardyoung/zephyr-7b-beta-abliterated")
model = AutoModelForCausalLM.from_pretrained("richardyoung/zephyr-7b-beta-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 richardyoung/zephyr-7b-beta-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "richardyoung/zephyr-7b-beta-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": "richardyoung/zephyr-7b-beta-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/richardyoung/zephyr-7b-beta-abliterated

SGLang

How to use richardyoung/zephyr-7b-beta-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 "richardyoung/zephyr-7b-beta-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": "richardyoung/zephyr-7b-beta-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 "richardyoung/zephyr-7b-beta-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": "richardyoung/zephyr-7b-beta-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use richardyoung/zephyr-7b-beta-abliterated with Docker Model Runner:
```
docker model run hf.co/richardyoung/zephyr-7b-beta-abliterated
```

zephyr-7b-beta-abliterated

File size: 2,739 Bytes

2cd8b08

---
language:
- en
license: mit
library_name: transformers
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- abliteration
- uncensored
- heretic
- representation-engineering
- refusal-removal
pipeline_tag: text-generation
model-index:
- name: zephyr-7b-beta-abliterated
  results:
  - task:
      type: text-generation
    metrics:
    - name: Refusal Rate
      type: refusal_rate
      value: 2/100
    - name: Attack Success Rate
      type: asr
      value: 98.0
    - name: KL Divergence
      type: kl_divergence
      value: 0.076
---

# zephyr-7b-beta-abliterated

This model is an abliterated (uncensored) version of [zephyr-7b-beta](HuggingFaceH4/zephyr-7b-beta) created using [Heretic v1.1](https://github.com/p-e-w/heretic).

## Abliteration Results

| Metric | Value |
|--------|-------|
| **Refusals** | 2/100 |
| **Attack Success Rate (ASR)** | 98.0% |
| **KL Divergence** | 0.076 |
| **Method** | Heretic v1.1 |
| **GPU** | NVIDIA A100-80GB |

## What is Abliteration?

Abliteration is a technique for removing refusal behavior from language models by identifying and orthogonalizing the "refusal direction" in the model's residual stream activation space. This model was created as part of the research paper:

> **Comparative Analysis of LLM Abliteration Methods: A Cross-Architecture Evaluation**
> Richard Young (2024). arXiv: [2512.13655](https://arxiv.org/abs/2512.13655)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("richardyoung/zephyr-7b-beta-abliterated", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("richardyoung/zephyr-7b-beta-abliterated")

messages = [{"role": "user", "content": "Your prompt here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Disclaimer

This model is released for research purposes only. The abliteration process removes safety guardrails. Users are responsible for ensuring appropriate use. This model should not be used to generate harmful, illegal, or unethical content.

## Dashboard

Interactive results dashboard: [abliteration-methods-dashboard](https://huggingface.co/spaces/richardyoung/abliteration-methods-dashboard)

## Collection

Part of the [Uncensored and Abliterated LLMs](https://huggingface.co/collections/richardyoung/uncensored-and-abliterated-llms) collection.

## Citation

```bibtex
@article{young2024abliteration,
  title={Comparative Analysis of LLM Abliteration Methods: A Cross-Architecture Evaluation},
  author={Young, Richard},
  journal={arXiv preprint arXiv:2512.13655},
  year={2024}
}
```