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Model: prithivMLmods/Magpie-Qwen-CortexDual-0.6B Source: Original Platform
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README.md
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---
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license: apache-2.0
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datasets:
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- Magpie-Align/Magpie-Pro-300K-Filtered
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- mlabonne/FineTome-100k
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- unsloth/OpenMathReasoning-mini
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- prithivMLmods/Grade-Math-18K
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language:
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- en
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base_model:
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- Qwen/Qwen3-0.6B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- math
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- code
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- moe
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---
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# Magpie-Qwen-CortexDual-0.6B
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> **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.
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> \[!note]
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> GGUF : [https://huggingface.co/prithivMLmods/Magpie-Qwen-CortexDual-0.6B-GGUF](https://huggingface.co/prithivMLmods/Magpie-Qwen-CortexDual-0.6B-GGUF)
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---
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## Key Features
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1. **Adaptive Reasoning via CortexDual**
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Automatically switches into a deeper thinking mode for complex problems, simulating trace-style deduction for higher-order tasks in math and code.
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2. **Efficient and Compact**
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At 0.6B parameters, it is optimized for deployment in constrained environments while retaining high fidelity in logic, computation, and structural formatting.
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3. **Magpie-Driven Data Synthesis**
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Trained using 80% of **Magpie Pro 330k**—a high-quality alignment and reasoning dataset—complemented with curated modular datasets for enhanced general-purpose capabilities.
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4. **Mathematical Precision**
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Fine-tuned for arithmetic, algebra, calculus, and symbolic logic; ideal for STEM learning platforms, math solvers, and step-by-step tutoring.
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5. **Lightweight Code Assistance**
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Understands and generates code in Python, JavaScript, and other common languages with contextual accuracy and explanation support.
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6. **Structured Output Generation**
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Specializes in Markdown, JSON, and table outputs, suitable for technical documentation, instruction generation, and structured reasoning.
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7. **Multilingual Competence**
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Supports over 20 languages with reasoning and translation support, expanding its reach for global educational and development use.
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---
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## Quickstart with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Magpie-Qwen-CortexDual-0.6B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Write a Python function to check if a number is prime. Explain each step."
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messages = [
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{"role": "system", "content": "You are an AI tutor skilled in both math and code."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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---
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## Demo Inference
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> [!warning]
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non-thinking (direct, reactive, retrieval-based responses)
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> [!warning]
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thinking (reasoning, planning, deeper analysis)
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---
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## Intended Use
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* General-purpose problem solving in math, logic, and code
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* Interactive STEM tutoring and reasoning explanation
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* Compact assistant for technical documentation and structured data tasks
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* Multilingual applications with a focus on accurate technical reasoning
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* Efficient offline deployment on low-resource devices
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---
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## Limitations
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* Lower creativity and open-domain generation due to reasoning-focused tuning
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* Limited context window size due to compact model size
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* May produce simplified logic paths in highly abstract domains
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* Trade-offs in diversity and expressiveness compared to larger instruction-tuned models
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---
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## References
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1. [Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing](https://arxiv.org/pdf/2406.08464)
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2. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
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3. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
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