In recent years Large Language Models (LLMs) have increased the state of the
art on several natural language processing tasks. However, their accessibility
is often limited to paid API services, posing challenges for researchers in
conducting extensive investigations. On the other hand, while some open-source
models have been proposed by the community, they are typically multilingual and
not specifically tailored for the Italian language. In an effort to democratize
the available and open resources for the Italian language, in this paper we
introduce Camoscio: a language model specifically tuned to follow users'
prompts in Italian. Specifically, we finetuned the smallest variant of LLaMA
(7b) with LoRA on a corpus of instruction prompts translated to Italian via
ChatGPT. Results indicate that the model's zero-shot performance on various
downstream tasks in Italian competes favorably with existing models
specifically finetuned for those tasks. All the artifacts (code, dataset,
model) are released to the community at the following url:
this https URL

通过使用 LoRA 和 ChatGPT 翻译的指令提示的语料库，我们精调了最小的变体 LLaMA（7b），在意大利语中为用户的提示引入了专门定制的语言模型 Camoscio。结果表明，该模型在多个意大利语下游任务中的零 - shot 性能与专门为这些任务进行精调的现有模型相媲美。