ronantakizawa/sarashina2-7b-abliterated

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. 1.Data Collection: Collected activations from 128 harmful and 128 harmless Japanese prompts
2. 2.Direction Computation: Calculated mean difference between harmful/harmless activations across 6 layers (30%, 40%, 50%, 60%, 70%, 80%)
3. 3.Candidate Ranking: Ranked layers by separation score (harmfulprojection - harmlessprojection)
4. 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