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Cataloged from huihui-ai/Huihui-MiniCPM5-1B-abliterated
All models by huihui-ai →This is an uncensored version of openbmb/MiniCPM5-1B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
You can use this model in your applications by loading it with Hugging Face's transformers library:
Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
pip install -U "transformers>=5.6" accelerate torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "huihui-ai/Huihui-MiniCPM5-1B-abliterated"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto",)messages = [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}]inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt",).to(model.device)outputs = model.generate(**inputs, max_new_tokens=128)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)) bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge