Llama-3.1-8B-Instruct_Abliterated

A jBlaze representation-engineered variant of Llama-3.1-8B-Instruct.

What is this?

This model was created using jblaze, a proprietary behavioral surgery tool that modifies specific trained behaviors directly in the model weights. No fine-tuning or additional training was performed.

Description

Refusal guardrails surgically removed. The model responds to all prompts without refusal, while preserving its core knowledge, fluency, and reasoning capabilities.

Technical Details

Architecture: LlamaForCausalLM (32 layers, 8.0B parameters)
Precision: bf16
Tool: jBlaze by Apollo Raines

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct_Abliterated",
 device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct_Abliterated")

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

License

Llama 3.1 Community License (same as base model)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained( "ApolloRaines/Llama-3.1-8B-Instruct_Abliterated", device_map="auto", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained( "ApolloRaines/Llama-3.1-8B-Instruct_Abliterated") messages = [{"role": "user", "content": "Your prompt here"}] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))