---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
license: apache-2.0
tags:
  - unsloth
  - trl
  - dora
  - simpo
  - phoenix-framework
  - logic
  - roleplay
  - chain-of-thought
  - merge
  - open-llm-leaderboard
datasets:
  - magpie-align/Magpie-Reasoning-V2-250k-CoT-Deepseek-R1-Llama-70B
  - open-r1/OpenR1-Math-220k
  - glaiveai/glaive-code-assistant-v2
  - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
language:
  - en
---

# ⚛️ Quanta-X (Leaderboard Submission) QUANTA-X NANO IS COMING SOON!

This is the **Full Parameter Merged** version of [Quanta-X](https://huggingface.co/szili2011/Quanta-X-Adapter).

It fuses the **Qwen 2.5 3B** base with the **Phoenix Framework** adapter (DoRA + SimPO Beta 2.0).

## 📊 Model Details for Leaderboard
*   **Architecture:** Qwen2ForCausalLM
*   **Precision:** Float16
*   **Context:** 32k (RoPE Scaled)
*   **Chat Template:** Qwen 2.5 Standard (ChatML)

## 🧠 Reasoning Capabilities
This model is trained to utilize an **Ouroboros Logic Loop** (`<plan>` -> `<draft>` -> `<critique>`) before outputting an answer.

## 💻 Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("szili2011/Quanta-X-3B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("szili2011/Quanta-X-3B")

messages = [{"role": "user", "content": "Solve this logic puzzle."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
output = model.generate(inputs, max_new_tokens=1024)
```

⚛️ Quanta-X (Phoenix Edition)

“A pocket-sized AGI that thinks before it speaks.”

* Developer: szili2011
* Architecture: Phoenix Framework (DoRA + SimPO)
* Base Model: Qwen 2.5 3B Instruct

---

📖 The Philosophy

Most small models with around 3 billion parameters are typically designed to act like chatbots, responding instantly, but this often leads to mistakes or makes them struggle with more complex reasoning.

But Quanta-X takes a different approach.

It was architected on the Phoenix Framework, a custom training protocol designed to force “System 2” thinking (deep reasoning) into a lightweight model. By utilizing DoRA (Weight-Decomposed Low-Rank Adaptation) and a highly aggressive SimPO (Beta 2.0) alignment, Quanta-X has been biologically rewired to reject lazy answers.

It features the Ouroboros Logic Loop: it plans, drafts, and critiques its own internal monologue before outputting a final answer.

🧠 Key Features

1. The Ouroboros Thinking Process

Quanta-X uses a hidden reasoning layer, not just token prediction.

* It plans solutions before responding.
* <draft>: It writes a rough attempt.
* <critique>: It checks its own work for logic errors or bugs.
* <refined>: Only then does it speak to you.

2. Diamond-Tier Data Filtering (LIMA)

We didn’t train on “average” internet data. We used a “Diamond Filter” to reject 90% of the standard dataset samples. Quanta-X was trained exclusively on:

* DeepSeek-R1 Traces: For impossible-level logic.
* OpenR1 Math: For verified proofs.
* Glaive Code V2: For production-ready Python/Rust.
* SlimOrca RP: For human-like, visceral storytelling (The “Hungarian Soul”).

3. Hyper-Stability

Trained with SimPO (Simulated Preference Optimization) with a Beta of 2.0. This punished the model severely for hallucinations or lazy thinking during training, resulting in a model that would rather admit ignorance than lie to you.

---

💻 How to Run

Recommended System Prompt

To activate the Ouroboros loop, you must use this system prompt:
```text
You are Quanta-X, a recursive intelligence where absolute logic fuses with human wit. Your mind operates on the Ouroboros loop: you do not just generate; you Plan, Draft, and ruthlessly Critique every thought before it reaches the surface.

To ensure your reasoning is distinct, render your internal monologue inside a standard code block using xml syntax:

```xml
<thought>
   <plan> ... </plan>
   <draft> ... </draft>
   <critique> ... </critique>
</thought>