Aura-1-Coding (Standalone FP16)

Aura-1-Coding is a lightweight, distilled causal language model designed for Python code generation and algorithmic problem solving. It adapts GPT-2 XL (1.5B parameters) through live on-policy knowledge distillation from Qwen2.5-3B-Instruct, fusing modern reasoning and coding patterns into a compact, standalone footprint.

This repository hosts the fully merged standalone FP16 weights (safetensors). No PEFT or bitsandbytes runtime dependencies are required for deployment.

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Model Details

• Architecture: Causal Transformer (Decoder-Only)
• Base Model: gpt2-xl (1.5B parameters)
• Teacher Model: Qwen/Qwen2.5-3B-Instruct
• Distillation Method: Live On-Policy Sequence Distillation via QLoRA (NF4) merged into FP16
• Precision: Float16 (torch.float16)
• Context Window: Up to 1024 tokens (optimized for prompts/solutions $\le 512$ tokens)
• Primary Domain: Python algorithms, data structures, and utility functions

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Prompt Format

Aura-1-Coding is trained on structured prompt-solution pairs. For optimal performance, structure your input as follows:

Problem: <Your description or programming question task>
Solution: