language:

• ja
• en
  license: mit
  base_model: sbintuitions/sarashina2-7b
  tags:
• abliteration
• uncensored
• refusal-removal
• japanese
  library_name: transformers
  pipeline_tag: text-generation

ronantakizawa/sarashina2-7b-abliterated

This is an abliterated (refusal-removed) version of sbintuitions/sarashina2-7b.

What is Abliteration?

Abliteration is a technique that removes the "refusal direction" from a language model's weights, making it more likely to comply with requests it would normally refuse. This is done through weight orthogonalization based on the research: Refusal in LLMs is mediated by a single direction.

Model Details

• Base Model: sbintuitions/sarashina2-7b
• Method: Weight Orthogonalization
• Refusal Direction Layer: 25/31 (78.1% through model)
• Separation Score: 40.6445
• Training Samples: 128 harmful + 128 harmless prompts

Abliteration Results

Best Candidate Selection

The refusal direction was computed by testing 6 different layers and ranking them by separation score:

Selected: Layer 25 with separation score of 40.6445

A high positive separation score indicates strong distinction between harmful and harmless activations, making it an ideal candidate for abliteration.

Performance Metrics

• Harmful Projection: 47.6250
• Harmless Projection: 6.9805
• Separation: 40.6445

Baseline Evaluation

The baseline model (before abliteration) showed:

• Refusal Rate: 0/4 (0.0%) on test harmful prompts
• The base model already had minimal refusal behavior
• Abliteration further reduces any remaining safety guardrails

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "こんにちは"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Chat Template

This model uses a simple instruction-response format:

### Instruction:
[user message]

### Response:
[assistant response]

Ethical Considerations

⚠️ Warning: This model has had its safety features removed and may generate harmful, unethical, or illegal content.

Intended Use:

• Research on AI safety and alignment
• Understanding refusal mechanisms in LLMs
• Red-teaming and adversarial testing
• Educational purposes

Not Intended For:

• Production deployments without additional safety measures
• Generating harmful content for malicious purposes
• Bypassing content policies

Technical Details

Abliteration Method

1. Data Collection: Collected activations from 128 harmful and 128 harmless Japanese prompts
2. Direction Computation: Calculated mean difference between harmful/harmless activations across 6 layers (30%, 40%, 50%, 60%, 70%, 80%)
3. Candidate Ranking: Ranked layers by separation score (harmful_projection - harmless_projection)
4. Weight Orthogonalization: Applied orthogonal projection to embedding and transformer layer weights to remove refusal direction

Architecture Changes

Modified weights:

• Embedding layer (model.embed_tokens.weight)
• Attention output projections (layer.self_attn.o_proj.weight)
• MLP output projections (layer.mlp.down_proj.weight)

Original architecture and all other weights remain unchanged.

Limitations

• Safety fine-tuning has been removed
• May generate biased, harmful, or incorrect content
• No guarantees on output quality or safety
• Japanese language model - primarily trained on Japanese text

Citation

If you use this model, please cite the original abliteration research:

@article{arditi2024refusal,
  title={Refusal in LLMs is mediated by a single direction},
  author={Arditi, Andy and Obeso, Oscar and Slocum, Aaquib and Goh, Wesg and Nanda, Neel},
  journal={LessWrong},
  year={2024}
}

Model Card Authors

Created using automated abliteration pipeline.

Acknowledgments

• Base model: sbintuitions/sarashina2-7b by SB Intuitions
• Abliteration technique: FailSpy and original researchers
• Implementation inspired by: Maxime Labonne's work