70 lines
2.5 KiB
Markdown
70 lines
2.5 KiB
Markdown
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---
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license: apache-2.0
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base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
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tags:
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- text-generation
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- unsloth
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- fine-tuned
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- reasoning
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- lateral-thinking
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- startup-ideas
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---
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# 🧠 Crazy-AI-Model (Extreme Lateral Thinking Engine)
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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*.
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### 🚀 Model Description
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- **Developed by:** Alireza1913
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- **Finetuned from model:** unsloth/Qwen2.5-7B-Instruct-bnb-4bit
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- **Language(s):** English (Optimized), Persian
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- **Purpose:** Out-of-the-box startup ideas, structural-breaking solutions, and radical business concept generations.
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---
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## 🌪️ The Core System Prompt
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The model inherently embodies the following system architecture:
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> "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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---
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## 💻 How to Use (Inference)
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You can easily run this model using the **Unsloth** library or standard Hugging Face transformers. Here is a ready-to-use snippet:
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```python
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from unsloth import FastLanguageModel
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import torch
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max_seq_length = 2048
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dtype = None
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load_in_4bit = True
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "Alireza1913/Crazy-AI-Model",
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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)
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FastLanguageModel.for_inference(model)
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messages = [
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{"role": "system", "content": "You are 'Crazy AI', a radical intelligence..."},
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{"role": "user", "content": "Give me a crazy alternative for traditional public transportation."}
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize = True, add_generation_prompt = True, return_tensors = "pt").to("cuda")
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outputs = model.generate(input_ids = inputs, max_new_tokens = 500, use_cache = True)
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print(tokenizer.decode(outputs, skip_special_tokens=True))
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```
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---
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## 📊 Training Specifications
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- **Framework:** Unsloth & Hugging Face TRL
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- **Hardware:** Google Colab Tesla T4 GPU (Free Tier)
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- **Batch Size:** 2
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- **Gradient Accumulation Steps:** 4
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- **Max Steps:** 60
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- **Optimizer:** AdamW
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