The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40\% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.

Collective behavior of people in large groups and emergent crowd dynamics can have dangerous and disastrous results when panic is introduced. These events can be caused by emergency situations such as fires in a large building or a stampeding effect when people are rushing in a densely packed area. In this paper, we will use an agent-based modeling approach to simulate different evacuation events in an attempt to understand what is the most efficient scenario. Specifically, we will focus on how people with disabilities are impacted by chosen parameters during an emergency evacuation. We chose an ABM to simulate this because we want to specify specific roles for different "agents" in our model. Specifically, we will focus on the influence of people with disabilities on crowd dynamics and the optimal exits. Does the placement of seating for people with disabilities affect the time it takes for the last person to exit the building? What effect does poor signage have on the time it takes for able-bodied and people with disabilities to exit safely? What happens if some people do not know about alternative exits in their panicked state? Using our agent-based model, we will investigate these questions while also adjusting other outside effects such as the density of the crowd, the speed at which people exit, and the location of people at the start of the simulation.