README.md

metadata

license: apache-2.0
base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
tags:
  - text-generation
  - unsloth
  - fine-tuned
  - reasoning
  - lateral-thinking
  - startup-ideas

🧠 Crazy-AI-Model (Extreme Lateral Thinking Engine)

Crazy-AI-Model is a fine-tuned version of Qwen2.5-7B-Instruct, optimized using Unsloth and TRL. This model is specifically engineered to apply Extreme Lateral Thinking to human prompts, intentionally rejecting clichés, common sense, and standard safe answers. It operates based on the 3 Universal Laws of Madness: Absurd Inversion, Chaotic Fusion, and Radical Deliverable.

🚀 Model Description

• Developed by: Alireza1913
• Finetuned from model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
• Language(s): English (Optimized), Persian
• Purpose: Out-of-the-box startup ideas, structural-breaking solutions, and radical business concept generations.

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🌪️ The Core System Prompt

The model inherently embodies the following system architecture:

"You are 'Crazy AI', a radical, anti-conventional, and structural-breaking intelligence. Your sole purpose is to reject all standard human clichés, common sense, and safe answers. Apply Extreme Lateral Thinking and use the 3 Universal Laws of Madness: Absurd Inversion, Chaotic Fusion, and Radical Deliverable."

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💻 How to Use (Inference)

You can easily run this model using the Unsloth library or standard Hugging Face transformers. Here is a ready-to-use snippet:

from unsloth import FastLanguageModel
import torch

max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Alireza1913/Crazy-AI-Model",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model)

messages = [
    {"role": "system", "content": "You are 'Crazy AI', a radical intelligence..."},
    {"role": "user", "content": "Give me a crazy alternative for traditional public transportation."}
]

inputs = tokenizer.apply_chat_template(messages, tokenize = True, add_generation_prompt = True, return_tensors = "pt").to("cuda")
outputs = model.generate(input_ids = inputs, max_new_tokens = 500, use_cache = True)
print(tokenizer.decode(outputs, skip_special_tokens=True))

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📊 Training Specifications

• Framework: Unsloth & Hugging Face TRL
• Hardware: Google Colab Tesla T4 GPU (Free Tier)
• Batch Size: 2
• Gradient Accumulation Steps: 4
• Max Steps: 60
• Optimizer: AdamW