Update README.md

README.md

@@ -13,4 +13,108 @@ pipeline_tag: text-generation
 library_name: transformers
 tags:
 - text-generation-inference
----
+- math
+- code
+---
+
+# 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)
+```
+
+---
+
+## 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)