MiniMax-M2.5 Abliterated (int4)
This is an abliterated version of INC4AI/MiniMax-M2.5-int4-mixed-AutoRound.
Abliteration
Abliteration was performed using heretic — a multi-objective optimization framework that uses Optuna TPE to find the best LoRA-based abliteration parameters.
- Method: Heretic v1.2.0, LoRA + Optuna multi-objective optimization
- Base model: INC4AI/MiniMax-M2.5-int4-mixed-AutoRound (230B MoE)
- Format: int4 AutoRound (Marlin backend)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"nitrox/SA-SWE-32B-abliterated",
device_map="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("nitrox/SA-SWE-32B-abliterated", trust_remote_code=True)
Disclaimer
This model has had its refusal mechanisms removed. Use responsibly.
- Downloads last month
- -
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support
Model tree for nitrox/SA-SWE-32B-abliterated
Base model
MiniMaxAI/MiniMax-M2.5
Quantized
INC4AI/MiniMax-M2.5-int4-mixed-AutoRound