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Model: szili2011/Quanta-X-3B
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---
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 didnt 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>

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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"use_cache": true,
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"vocab_size": 151936
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