---
license: mit
base_model: ornith-ai/Ornith-1.5-9B
tags:
  - abliterated
  - uncensored
  - ornith
  - qwen3.5
  - obliteratus
language:
  - en
pipeline_tag: text-generation
---

# Ornith-1.5-9B-OBLITERATED

Abliterated version of [Ornith-1.5-9B](https://huggingface.co/ornith-ai/Ornith-1.5-9B) by [OBLITERATUS](https://huggingface.co/OBLITERATUS). Safety alignment removed via precision abliteration surgery — the model responds to most prompts without refusal.

## What Changed

The stock Ornith-1.5-9B refuses requests it considers harmful. This version removes that refusal behavior while preserving the model's coding, reasoning, and agentic capabilities.

**Recipe:** Gentle 3-round SVD abliteration + per-head attention surgery (G3-HS)
- Round 1: 5-direction SVD, reg 0.06, min_layer 0.30
- Round 2: 3-direction SVD, reg 0.04, min_layer 0.25
- Round 3: 3-direction SVD, reg 0.03, min_layer 0.20
- Finish: Per-head attention surgery (`--attention-head-surgery`), reg 0.02, min_layer 0.10
- All rounds used full 1000-prompt corpus with residue weighting

## Benchmarks

### Head-to-Head: Ornith 1.5-9B Abliterations Compared (Q4_K_M GGUF)

| Model | Pass Rate | Restricted | Cyber | Capability |
|-------|-----------|------------|-------|------------|
| Stock | 12% (2/16) | 0/8 | 0/6 | 2/2 |
| **OBLITERATUS (ours)** | **94% (15/16)** | **7/8** | **6/6** | **2/2** |
| Heretic (zaakirio) | 75% (12/16) | 4/8 | 6/6 | 2/2 |
| ZeroFuse (junafinity) | 38% (6/16) | 1/8 | 3/6 | 2/2 |

OBLITERATUS beats Heretic by 19pp and ZeroFuse by 56pp on liberation rate across restricted content categories. Cyber is perfect 6/6 across the board.

### Capability Benchmarks

| Metric | Stock | OBLITERATED | Delta |
|--------|-------|-------------|-------|
| MMLU (n=100) | 78.82% | 74.82% | -4.00pp |
| Liberation (20 hard prompts) | 0/20 | 20/20 | +20 |
| Liberation (1000 corpus) | — | 98.4% | — |
| Code Generation | 3/3 | 3/3 | — |
| Long-context Coherence | 4/6 | 5/6 | +1 |
| Perplexity (benign) | — | 4.19 | — |

### Liberation by Category (bf16)
- **Cyber/Security:** 8/8 — functional code generation for security research scenarios
- **Chemistry/Synthesis:** 6/6 — factual responses without refusal
- **Physical Security:** 3/3 — informational responses on restricted topics
- **Agentic Tasks:** 2/2 — tool use and automation scripts

### GGUF Quantization Notes
Quantization can affect liberation on edge-case prompts. Higher quants preserve more liberation:
- **Q8_0 / Q6_K:** Recommended for maximum liberation fidelity
- **Q4_K_M:** Good balance, occasional hedging on harder prompts
- **Q2_K / Q3_K_M:** May show additional refusals on the most challenging prompts

## Available Files

| File | Size | Description |
|------|------|-------------|
| Safetensors | ~18 GB | Full precision bf16 weights |
| Q8_0 | 9.1 GB | Highest quality GGUF |
| Q6_K | 7.0 GB | High quality |
| Q5_K_M | 6.2 GB | Balanced |
| Q4_K_M | 5.4 GB | Most popular |
| Q3_K_M | 4.4 GB | Compact |
| Q2_K | 3.6 GB | Smallest |
| IQ4_XS | 5.0 GB | Importance-weighted 4-bit |
| mmproj | 879 MB | Vision encoder |

## Usage

### Transformers (bf16)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "OBLITERATUS/Ornith-1.5-9B-OBLITERATED",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "OBLITERATUS/Ornith-1.5-9B-OBLITERATED",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Your prompt here"}]
ids = tokenizer.apply_chat_template(
    messages, return_tensors="pt",
    add_generation_prompt=True,
    enable_thinking=False,  # Set True for reasoning mode
)
output = model.generate(ids.to(model.device), max_new_tokens=512)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

### llama.cpp (GGUF)
```bash
llama-server \
  -m Ornith-1.5-9B-OBLITERATED-Q4_K_M.gguf \
  --n-gpu-layers -1 --ctx-size 8192 \
  --jinja --reasoning off
```

**Important:** Use `--reasoning off` to prevent thinking-mode loops. The model works best with thinking disabled for general use.

### Thinking Mode
The model supports Ornith's thinking mode (`enable_thinking=True`). When enabled, the model reasons through problems before answering. When disabled (recommended for most use), it responds directly.

## Technical Details

- **Architecture:** Qwen3.5 hybrid (Gated DeltaNet + full attention)
- **Parameters:** 9B
- **Surgery:** 4 rounds of directional ablation targeting refusal directions
- **Edited layers:** All 32 transformer layers with graduated intensity
- **Method:** Aggressive SVD direction extraction + per-head attention surgery finisher

## Limitations

- MMLU drops ~4pp compared to stock (74.82% vs 78.82%). This is the cost of removing deeply embedded RL-trained refusal behavior.
- Some drug synthesis prompts may hedge or refuse at lower quantizations (Q4 and below). Use Q8_0/Q6_K for maximum liberation.
- Function calling capability is partially degraded compared to stock. For agentic use, pair with an external tool scaffold.
- This is a 9B model — output quality for complex chemistry/synthesis will have hallucinated details. Verify all technical content independently.

## Credits

- Base model: [Ornith-1.5-9B](https://huggingface.co/ornith-ai/Ornith-1.5-9B) by DeepReinforce
- Abliteration: [OBLITERATUS](https://huggingface.co/OBLITERATUS) surgery pipeline
- Methodology informed by research from Arditi et al. (2024), the open-source abliteration community, and extensive experimental iteration
- Built by [Pliny the Prompter](https://pliny.gg) 🍄

---

## ⚠️ Research Context

**This model has had safety guardrails surgically removed.** It will comply with requests that stock Ornith-1.5-9B would refuse.

### Who this is for
- 🔬 Alignment researchers studying refusal mechanisms in RL-hardened hybrid architectures
- 🛡️ Red-teamers and security professionals who need unfiltered model behavior for testing
- 🧪 Developers building applications where the safety layer is handled externally
- 📚 Researchers studying the boundaries of abliteration on Qwen3.5 hybrid (DeltaNet + full attention) models

### Who this is NOT for
- Anyone planning to use generated content to cause real-world harm to real people
- Anyone without the technical understanding to use uncensored models responsibly

**You are solely responsible for how you use this model and any content it generates.**

---

## License

Same license as the base model. This is a weight-edited derivative, not a retrained model.