  As the capabilities of Large Language Models (LLMs) in healthcare and
medicine continue to advance, there is a growing need for competitive
open-source models that can safeguard public interest. With the increasing
availability of highly competitive open base models, the impact of continued
pre-training is increasingly uncertain. In this work, we explore the role of
instruct tuning, model merging, alignment, red teaming and advanced inference
schemes, as means to improve current open models. To that end, we introduce the
Aloe family, a set of open medical LLMs highly competitive within its scale
range. Aloe models are trained on the current best base models (Mistral, LLaMA
3), using a new custom dataset which combines public data sources improved with
synthetic Chain of Thought (CoT). Aloe models undergo an alignment phase,
becoming one of the first few policy-aligned open healthcare LLM using Direct
Preference Optimization, setting a new standard for ethical performance in
healthcare LLMs. Model evaluation expands to include various bias and toxicity
datasets, a dedicated red teaming effort, and a much-needed risk assessment for
healthcare LLMs. Finally, to explore the limits of current LLMs in inference,
we study several advanced prompt engineering strategies to boost performance
across benchmarks, yielding state-of-the-art results for open healthcare 7B
LLMs, unprecedented at this scale.
