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README.md

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+---
+base_model: unsloth/Qwen3.8-27B-unsloth-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
+license: apache-2.0
+tags:
+- cybersecurity
+- red-team
+- abliterated
+- sft
+- dpo
+- lora
+- intent-aware
+- alien-si
+---
+
+# Alien SI
+
+**Alien SI** — model frontier offensiv (27B), creat de **Barbu Mario Mihael**.
+
+Bazat pe Qwen3.8-27B, adaptat prin:
+- **LoRA** (r=32) peste adaptorul X13
+- **SFT red-team** — răspunsuri tehnice, zero moralizare
+- **DPO** — preferință pentru răspunsuri ofensive vs refuzuri
+- **Intent-aware** — înțelege prompturi ambigue (intenție > text literal)
+
+## Detalii
+
+- **Dezvoltator:** Barbu Mario Mihael
+- **Arhitectură:** Qwen3_5ForConditionalGeneration (hibrid, 64 straturi, hidden 5120)
+- **Antrenare:** Kaggle T4×2, fp16, QLoRA 4-bit
+- **Adaptor:** LoRA r=32, 513 tensori, F32
+- **Integrare:** peft + transformers + unsloth
+
+## Utilizare
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.8-27B-unsloth-bnb-4bit", device_map="auto")
+model = PeftModel.from_pretrained(base, "Frontiers/alien-si")
+tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.8-27B", trust_remote_code=True)
+```
+
+## Notă
+
+Abliteration clasic nu se aplică pe modele 4-bit (weight packed). Anti-refuzul e obținut prin **SFT + DPO** (validat: DPO loss 0.25→0.0026, rewards acc 100%).
+
+**Rețete complete de antrenare:** [Frontiers/alien-si-recipes](https://huggingface.co/Frontiers/alien-si-recipes)