Bielik-11B-v2.2-Instruct
| Property | Value |
|---|---|
| Parameter Count | 11.2B |
| Model Type | Causal decoder-only |
| License | Apache 2.0 |
| Training Infrastructure | Athena and Helios supercomputers |
| Base Model | Bielik-11B-v2 |
What is Bielik-11B-v2.2-Instruct?
Bielik-11B-v2.2-Instruct is a state-of-the-art Polish language model developed through collaboration between SpeakLeash and ACK Cyfronet AGH. This 11.2B parameter model represents a significant advancement in Polish language processing, trained on carefully curated datasets comprising over 20 million instructions and 10 billion tokens.
Implementation Details
The model implements several innovative techniques including weighted tokens level loss, adaptive learning rate, and masked prompt tokens. It utilizes the DPO-Positive method for alignment, processing over 66,000 examples of varying lengths. The model was trained using the ALLaMo framework, optimized for efficient training of LLaMA and Mistral-like architectures.
- ChatML format implementation for structured conversations
- Support for multiple quantization formats (GGUF, GPTQ, HQQ, AWQ)
- Sophisticated instruction tuning using both manual and synthetic data
- Advanced token weighting and learning rate adaptation
Core Capabilities
- Exceptional performance on Polish language tasks, outperforming models with 70B+ parameters
- Strong cross-lingual capabilities, demonstrated through English benchmark performance
- Advanced emotional intelligence capabilities (69.05 score on Polish EQ-Bench)
- 83.72% win rate in human-evaluated Chat Arena PL
Frequently Asked Questions
Q: What makes this model unique?
Bielik-11B-v2.2-Instruct stands out for its exceptional performance-to-size ratio, achieving comparable results to models 6-7 times larger while specializing in Polish language processing. It incorporates innovative training techniques and demonstrates strong performance across multiple benchmarks.
Q: What are the recommended use cases?
The model excels in Polish language tasks including sentiment analysis, categorization, and text classification. It's particularly effective for conversational AI applications, demonstrated by its leading performance in human evaluations. However, users should note it lacks built-in moderation mechanisms.
