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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.
---
## 🌪️ 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."
---
## 💻 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:
```python
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))
```
---
## 📊 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
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