README.md

---
license: apache-2.0
datasets:
- Magpie-Align/Magpie-Pro-300K-Filtered
- mlabonne/FineTome-100k
- unsloth/OpenMathReasoning-mini
- prithivMLmods/Grade-Math-18K
language:
- en
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- math
- code
- moe
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/s65vynroXyhAS6Y_3nLxE.png)
 
# Magpie-Qwen-CortexDual-0.6B

> **Magpie-Qwen-CortexDual-0.6B** is a specialized, general-purpose model designed for **math**, **code**, and **structured reasoning**. Built with **CortexDual thinking mode**, it dynamically adapts to the complexity of a problem, automatically shifting into a stepwise reasoning mode for intricate logic or math tasks. This 0.6B parameter model leverages **80% of the Magpie Pro 330k dataset** and a modular blend of datasets for general-purpose proficiency and domain versatility.

> \[!note]
> GGUF : [https://huggingface.co/prithivMLmods/Magpie-Qwen-CortexDual-0.6B-GGUF](https://huggingface.co/prithivMLmods/Magpie-Qwen-CortexDual-0.6B-GGUF)

---

## Key Features

1. **Adaptive Reasoning via CortexDual**
   Automatically switches into a deeper thinking mode for complex problems, simulating trace-style deduction for higher-order tasks in math and code.

2. **Efficient and Compact**
   At 0.6B parameters, it is optimized for deployment in constrained environments while retaining high fidelity in logic, computation, and structural formatting.

3. **Magpie-Driven Data Synthesis**
   Trained using 80% of **Magpie Pro 330k**—a high-quality alignment and reasoning dataset—complemented with curated modular datasets for enhanced general-purpose capabilities.

4. **Mathematical Precision**
   Fine-tuned for arithmetic, algebra, calculus, and symbolic logic; ideal for STEM learning platforms, math solvers, and step-by-step tutoring.

5. **Lightweight Code Assistance**
   Understands and generates code in Python, JavaScript, and other common languages with contextual accuracy and explanation support.

6. **Structured Output Generation**
   Specializes in Markdown, JSON, and table outputs, suitable for technical documentation, instruction generation, and structured reasoning.

7. **Multilingual Competence**
   Supports over 20 languages with reasoning and translation support, expanding its reach for global educational and development use.

---

## Quickstart with Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Magpie-Qwen-CortexDual-0.6B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function to check if a number is prime. Explain each step."

messages = [
    {"role": "system", "content": "You are an AI tutor skilled in both math and code."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

---

## Demo Inference

> [!warning]
non-thinking (direct, reactive, retrieval-based responses)

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/i6be8JWvXZMA1Zu14yqMR.png)

> [!warning]
thinking (reasoning, planning, deeper analysis)

![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/c1vy029GVOo2PUBA_XfR6.png)
![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vDlsd1UDN_I0jiS_uwd7X.png)

--- 

## Intended Use

* General-purpose problem solving in math, logic, and code
* Interactive STEM tutoring and reasoning explanation
* Compact assistant for technical documentation and structured data tasks
* Multilingual applications with a focus on accurate technical reasoning
* Efficient offline deployment on low-resource devices

---

## Limitations

* Lower creativity and open-domain generation due to reasoning-focused tuning
* Limited context window size due to compact model size
* May produce simplified logic paths in highly abstract domains
* Trade-offs in diversity and expressiveness compared to larger instruction-tuned models

---

## References

1. [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://arxiv.org/pdf/2406.08464)
2. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
3. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)