Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Linguistic and Cultural Competency | Polish Linguistic and Cultural Competency Benchmark (PLCC) | Avg Score46.67 | 52 | |
| Large Language Model Evaluation | Open PL LLM Leaderboard instruction-tuned | Overall Average Score44.7 | 44 | |
| Polish Text Understanding | CPTUB | Overall Avg2.88 | 31 | |
| Linguistic Implicatures Decoding | Open PL LLM Leaderboard Implicatures component base models | Average Score34.34 | 30 | |
| Medical Knowledge Performance | Polish Board Certification Examinations (test) | Average Score (%)29.74 | 29 | |
| Large Language Model Evaluation | Open LLM Leaderboard | Average Score51.26 | 19 | |
| Large Language Model Evaluation | Open LLM Leaderboard v1 (test) | Average Score49.98 | 14 |