Performance Comparison of aiXcoder-7B and 13B Models on HumanEval Python Benchmark

Large Language Models (LLMs) have been widely used in code completion, and researchers are focusing on scaling up LLMs to improve their accuracy. However, larger LLMs have lower inference efficiency, affecting developers' experience and productivity. In this paper, we propose a lightweight and effective LLM for code completion named aiXcoder-7B. Compared to existing LLMs, aiXcoder-7B achieves higher code completion accuracy while having smaller scales (i.e., 7 billion parameters). We attribute the superiority of aiXcoder-7B to three key factors: (1) Multi-objective training. We employ three tr

Research goal: How does the pass@1 metric of aiXcoder-7B compare to 13B parameter models on the HumanEval Python benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.