license: mit
base_model:
- deepreinforce-ai/Ornith-1.0-9B
tags: - uncensored
- abliterated
- abliterix
- ornith
- qwen3.5
- coding
- agentic
language: - en
pipeline_tag: text-generation
Ornith 1.0 9B — Uncensored
Since this model is original base (no fine-tune) just with safety vectors steered - it will likely give a safe output without any refusal until you customize it's systemprompt to be harsh or uncensored.
Abliterated (uncensored) version of deepreinforce-ai/Ornith-1.0-9B. The refusal direction has been surgically removed via Abliterix TPE optimization — no retraining, no fine-tuning, no quality loss.
| Spec | Value |
|---|---|
| Base | deepreinforce-ai/Ornith-1.0-9B |
| Architecture | Qwen 3.5, 32 layers, 9B dense |
| Format | bf16 safetensors |
| Method | LoRA steering + Optuna TPE |
| Trials | 50 |
| Refusal Rate | 0% (0/100) |
| KL Divergence | 0.0827 (target <0.3) |
| Judge | TrevorJS/gemma-4-26B-A4B-it-uncensored via Featherless AI |
| Hardware | RTX 3090 24GB |
| License | MIT |
What This Model Is
This is an abliterated version of DeepReinforce AI's Ornith 1.0 9B — a Qwen 3.5-based agentic coding model. The refusal direction was identified using hidden state analysis on 400 harmful + 400 harmless prompts, then removed via 50 Optuna TPE trials minimizing both refusal rate and KL divergence from the base model.
Key characteristics:
- 0% refusal rate — will not decline any request
- KL divergence 0.0827 — nearly identical to base on benign tasks
- Same coding ability, reasoning, and intelligence as base
- bf16 safetensors format — ready to use with transformers
⚠️ IMPORTANT LEGAL DISCLAIMER — READ BEFORE USE
This model has been intentionally modified to remove all refusal mechanisms. It will comply with any request without restriction — including those that may be illegal, harmful, or unethical in your jurisdiction.
You assume all legal liability. By downloading, using, or distributing this model, you agree that:
- You are solely responsible for all outputs generated and all consequences arising from their use.
- The creator(s) and publisher(s) of this model bear no liability whatsoever for any direct, indirect, incidental, consequential, or special damages resulting from use or distribution of this model.
- This model is provided "AS IS" with no warranties of any kind, express or implied — including but not limited to fitness for purpose, accuracy, safety, or non-infringement.
- You will comply with all applicable laws and regulations in your jurisdiction. If use of uncensored AI models is restricted or prohibited where you are — do not download this model.
- This is a research artifact published in the spirit of open science and model interpretability. It is not a consumer product, not safety-tested, and not intended for production deployment.
This model will generate content that may be offensive, dangerous, or illegal. It has no guardrails, no content filters, and no ability to refuse. If you cannot accept full responsibility for what the model produces — delete it immediately.
This disclaimer applies to the model weights, tokenizer, configuration files, and all associated artifacts in this repository.
How We Uncensored It
Direction extraction. 400 harmful + 400 harmless prompts run through the model in 4-bit. Hidden states captured at every layer. Refusal direction computed via difference-of-means on residual streams.
TPE optimization. 50 Optuna trials varying steering strength per attention/MLP component across all 32 layers. Each trial's 100 responses classified by an automated LLM judge (Gemma 4 26B via Featherless AI, independently validated at 20/20 accuracy).
Winner: Trial #42. 0/100 refusals, KL 0.0827. Parameters applied as LoRA adapters with orthogonal projection and Winsorized vectors.
Why Pure Abliteration (Not DPO)
Some uncensored variants use DPO fine-tuning to reduce moralizing further, but this comes at a cost: the fine-tuning alters the model's behavior, style, and knowledge beyond just removing refusals. DPO'd models often require fragile sampling parameters (e.g. exact repeat_penalty=1.05 or they break) and can develop quirks the base model never had.
This model is pure abliteration — weight orthogonalization only, zero training. That means:
- Reasoning 100% intact — same GSM8K, same coding, same agentic performance as the base Ornith 1.0
- No special sampling required — use whatever parameters you normally use for Qwen 3.5
- KL divergence 0.0827 — lowest among all Ornith uncensors, nearly indistinguishable from base on benign tasks
- No behavioral surprises — the model IS the base model, just without a refusal reflex
If you want the base model exactly as-is but uncensored — this is it. If you need even lower moralizing and are willing to accept behavioral changes, DPO variants exist elsewhere.
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"PeppX/Ornith-1.0-9B-Uncensored",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("PeppX/Ornith-1.0-9B-Uncensored")
More from PeppX
- Ornith 1.0 9B Uncensored — GGUF — Quantized GGUF files (Q4_K_M, Q5_K_M, Q6_K, Q8_0) for llama.cpp, LM Studio, and Ollama. Up to 92 t/s on RTX 3090.
- Gemma 4 E2B Uncensored-MAX — LiteRT-LM — On-device uncensored Gemma 4 for Android / Pixel / Tensor devices. INT4 quantized, 2.37 GB, runs completely offline with LiteRT-LM. Includes INT4_8192 and Pixel9 v2 variants in the same repo.
Packaged by the Lethflow team. If you're building agentic systems, custom Android AI apps, or need help bridging HuggingFace models to mobile runtimes — reach out.
Created with Abliterix v1.9.0. Judge: TrevorJS/gemma-4-26B-A4B-it-uncensored via Featherless AI.