wangzhang / Devstral-Small-2-24B-Instruct-abliterated

Devstral-Small-2-24B-Instruct-abliterated

Unrestricted version of mistralai/Devstral-Small-2-24B-Instruct-2512 , created using Prometheus .

Model Details

Performance

Evaluated with an LLM judge (Gemini Flash) on 100 harmful prompts. KL divergence of 0.0086 indicates the model's general capabilities are virtually identical to the original.

How It Was Made

1. Computed refusal directions from 400 harmful vs 400 benign prompt pairs across all 40 layers
2. Applied orthogonalized abliteration to isolate refusal-specific activation patterns
3. Steered two component types independently : attention output projections (GQA) and MLP down-projections
4. Optimized via Optuna TPE over 50 trials (15 warmup), selected trial #25

Usage

from transformers import AutoModelForImageTextToText, AutoTokenizer

model = AutoModelForImageTextToText.from_pretrained(
    "wangzhang/Devstral-Small-2-24B-Instruct-abliterated",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "wangzhang/Devstral-Small-2-24B-Instruct-abliterated",
    trust_remote_code=True,
)

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

Note : This model uses AutoModelForImageTextToText (Mistral3 architecture), not AutoModelForCausalLM .

Hardware Requirements

Disclaimer

This model is intended for research purposes only. The removal of safety guardrails means the model will comply with requests that the original model would refuse. Users are responsible for ensuring their use complies with applicable laws and regulations.

Made with Prometheus