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This model card describes the Llama-3-8B-Instruct-abliterated-v2 model, which is an orthogonalized version of the meta-llama/Llama-3-8B-Instruct model, and an improvement upon the previous generation Llama-3-8B-Instruct-abliterated. This variant has had certain weights manipulated to inhibit the model's ability to express refusal. The methodology used to generate this model is described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' This model may come with interesting quirks, as the methodology is still new and untested. The code used to generate the model is available in the Python notebook ortho_cookbook.ipynb. Please note that the model may still refuse to answer certain requests, even after the weights have been manipulated to inhibit refusal.
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View details ↗openchat-3.5-0106-laser
QuixiAIHere, we are controlling layers checking which ones have lower signal to noise ratios (which are more subject to noise), to apply Laser interventions, still using Machenko Pastur to calculate this ratio. We intend to be the first of a family of experimentations being carried out @ Cognitive Computations.
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View details ↗Qwen3-235B-A22B-AWQ
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View details ↗Qwen3-30B-A3B-AWQ
QuixiAItokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto" ) prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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View details ↗Qwen3-58B-Embiggened-gguf
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View details ↗WestLake-7B-v2-laser
QuixiAIHere, we are controlling layers checking which ones have lower signal to noise ratios (which are more subject to noise), to apply Laser interventions, still using Machenko Pastur to calculate this ratio. We intend to be the first of a family of experimentations being carried out @ Cognitive Computations.
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View details ↗Wizard-Vicuna-30B-Uncensored
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View details ↗Wizard-Vicuna-7B-Uncensored
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View details ↗WizardLM-13B-Uncensored
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View details ↗WizardLM-33B-V1.0-Uncensored
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View details ↗WizardLM-7B-Uncensored
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