4.8 KiB
license, datasets, language, base_model, pipeline_tag, library_name, tags
| license | datasets | language | base_model | pipeline_tag | library_name | tags | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
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text-generation | transformers |
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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
Key Features
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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.
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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.
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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.
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Mathematical Precision 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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Lightweight Code Assistance Understands and generates code in Python, JavaScript, and other common languages with contextual accuracy and explanation support.
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Structured Output Generation Specializes in Markdown, JSON, and table outputs, suitable for technical documentation, instruction generation, and structured reasoning.
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Multilingual Competence Supports over 20 languages with reasoning and translation support, expanding its reach for global educational and development use.
Quickstart with Transformers
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)
Warning
thinking (reasoning, planning, deeper analysis)
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