We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning of different instruction types, and Adaptive Learning Rate, which dynamically adjusts the learning rate based on training progress. To evaluate performance, we created the Open PL LLM Leaderboard and Polish MT-Bench, novel frameworks assessing various NLP tasks and conversational abilities. Bielik 7B v0.1 demonstrates significant improvements, achieving a 9 percentage point increase in average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench, particularly in Reasoning (6.15/10) and Role-playing (7.83/10) categories. This model represents a substantial advancement in Polish language AI, offering a powerful tool for diverse linguistic applications and setting new benchmarks in the field.

 本研究解决了波兰语言处理中的关键挑战，提出了Bielik 7B v0.1，一个具有70亿参数的生成文本模型。采用了创新技术，如加权指令交叉熵损失和自适应学习率，使得在各类NLP任务和对话能力评估中有显著提升，尤其在推理和角色扮演类别中表现优异。这一模型为波兰语言AI的应用及该领域的基准设定带来了重大进展。 

Bielik 7B v0.1：波兰语言模型的开发、见解与评估