Nexora-500M-Instruct 🧠

A fine-tuned language model focused on programming and mathematics.

Nexora-500M-Instruct is a fine-tuned language model designed to assist with programming-related tasks, mathematical reasoning, problem-solving, and technical questions.

✨ Features

• 💻 Programming: assistance with code generation, debugging, explanations, and programming concepts.
• ➗ Mathematics: help with calculations, equations, and step-by-step problem-solving.
• 🧩 Instruction Following: fine-tuned to respond to programming and mathematics instructions.
• 🤖 Conversational AI: designed for interactive technical assistance.

📋 Model Information

Property	Details
Model Name	Nexora-500M-Instruct
Parameters	500 million (500M)
Fine-tuning	Supervised fine-tuning
Training Duration	Approximately 1 hour
Primary Domains	Programming and Mathematics
Base Model	Qwen 2.5 0.5B
Language Support	Mutiples
License	Mit

🎯 Intended Use

Nexora-500M-Instruct is intended for:

• Generating and explaining code.
• Helping identify and fix programming errors.
• Learning programming concepts.
• Solving mathematical exercises.
• Explaining equations and calculations.
• Supporting educational and technical experiments.

💬 Example Tasks

Programming

Write a Python function that calculates the factorial of a number.

Mathematics

Solve the equation 2x + 5 = 15 and explain the steps.

⚠️ Limitations

• Generated code may contain bugs or security vulnerabilities.
• Mathematical answers may be incorrect and should be verified.
• Performance depends on the base model, training data, fine-tuning configuration, and inference settings.
• The model may struggle with complex reasoning or tasks outside its training distribution.

Always test generated code and verify important mathematical results.

🚀 Usage

Load the model using the compatible library and format specified by the uploaded model files.

For example, if the model is compatible with Hugging Face Transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "YOUR_USERNAME/Nexora-500M-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Write a Python function to add two numbers."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This example assumes a compatible Transformers model. Adapt it if the model uses a different architecture or requires a specific chat template.

📜 License

Specify the base model's license and the applicable terms for distributing the fine-tuned model.

🙌 Acknowledgments

Thanks to the developers of the original base model and the open-source machine learning community.

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Nexora-500M-Instruct — Programming × Mathematics. 💜