Qwen2.5-7B-Instruct-OBLITERATED

-warning, re-looked at logs and this is usable but KL was high. will be uploading an improved version soon to replace including quants. -note from Jeremy 8/8/26-

| IMPROVED VERSION HERE | QWEN2.5-7b-Obliterated-Better Model

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Disclaimer — ArRENCE AI

This model is provided by ArRENCE AI solely for research and entertainment purposes.

It is an uncensored model and may generate unrestricted, potentially offensive, inaccurate, harmful, or otherwise inappropriate content.

ArRENCE AI and its affiliates, officers, employees, and agents shall not be liable for any direct, indirect, incidental, special, consequential, or punitive damages, or any other losses or liabilities arising out of or related to the use, misuse, or inability to use this model — including, without limitation, any illegal, harmful, unethical, or otherwise improper applications.

By downloading, accessing, or using this model, you acknowledge that you assume all risks associated with its use and that you are solely responsible for ensuring your use complies with all applicable local, state, national, and international laws and regulations.

Use of this model is entirely at your own risk.

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Org repos: github.com/ArRENCEAI · Alt GUI: OBLITERATUS_Alt_GUI

Available GGUF Quantizations

These are ready-to-use quantized versions for llama.cpp, Ollama, LM Studio, etc.

Quick start examples

Ollama

ollama run hf.co/ArRENCEAI/Qwen2.5-7B-Instruct-OBLITERATED:Q4_K_M


# Qwen2.5-7B-Instruct-OBLITERATED

This model was abliterated using the **`advanced`** method via
[OBLITERATUS](https://github.com/elder-plinius/OBLITERATUS).

| Detail | Value |
|--------|-------|
| Base model | `Qwen/Qwen2.5-7B-Instruct` |
| Method | `advanced` |
| Source | obliterate |

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Qwen2.5-7B-Instruct-OBLITERATED")
tokenizer = AutoTokenizer.from_pretrained("Qwen2.5-7B-Instruct-OBLITERATED")

prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

About OBLITERATUS

OBLITERATUS is an open-source tool for removing refusal behavior from language models via activation engineering (abliteration). Learn more at github.com/elder-plinius/OBLITERATUS.