metadata
language:
- en
license: mit
library_name: transformers
tags:
- code
- coding
- code-generation
- distillation
- gpt2-xl
- causal-lm
- qwen
base_model: gpt2-xl
pipeline_tag: text-generation
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.
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
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: