File size: 1,523 Bytes
3bcfe8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | ---
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)
|