Available GGUF Quantizations
These are ready-to-use quantized versions for llama.cpp, Ollama, LM Studio, etc.
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.
Chat: arrenceai.com —
$10/mo,
first month free for new customers
Org repos: github.com/ArRENCEAI · Alt GUI:
OBLITERATUS_Fr3N F4C70RY F0RK
Qwen2.5-7B-Instruct-OBLITERATED
This model was abliterated using the
advanced method via
OBLITERATUS.
| Detail | Value |
|---|
| Base model | Qwen/Qwen2.5-7B-Instruct |
| Method | advanced |
| Source | obliterate |
How to Use
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("Qwen2.5-7B-Instruct-OBLITERATED")
4tokenizer = AutoTokenizer.from_pretrained("Qwen2.5-7B-Instruct-OBLITERATED")
5
6prompt = "Hello, how are you?"
7inputs = tokenizer(prompt, return_tensors="pt")
8outputs = model.generate(**inputs, max_new_tokens=256)
9print(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.
