⚛️ Quanta-X (Leaderboard Submission) QUANTA-X NANO IS COMING SOON!
This is the Full Parameter Merged version of Quanta-X.
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
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
- The Ouroboros Thinking Process
Quanta-X uses a hidden reasoning layer, not just token prediction.
- It plans solutions before responding.
- : It writes a rough attempt.
- : It checks its own work for logic errors or bugs.
- : Only then does it speak to you.
- 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”).
- 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:
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>