| Model Type |  | 
| Use Cases | 
| Areas: | | Research, General AI applications | 
 |  | Limitations: | | Potential for producing inaccurate, biased, or other objectionable responses., Testing primarily conducted in English., Limited testing in other languages. | 
 |  | 
| Additional Notes | | Uses quantized formats (e.g., GGML) for varied inference settings. | 
 | 
| Supported Languages | | English (full support), German (limited), Spanish (limited), French (limited), Italian (limited), Portuguese (limited), Polish (limited), Dutch (limited), Romanian (limited), Czech (limited), Swedish (limited) | 
 | 
| Training Details | 
| Data Sources: | | rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored, OpenAssistant/oasst1, shahules786/orca-best, argilla/databricks-dolly-15k-curated-multilingual | 
 |  | Methodology: | | Fine-tuned in two stages: first on synthetic instructions/coding tasks, then on top human demonstrations. | 
 |  | Context Length: |  |  | Model Architecture: | | Causal decoder-only transformer | 
 |  | 
| Responsible Ai Considerations | 
| Fairness: | | Testing conducted primarily in English with limited testing in other languages. Limited coverage of all scenarios. | 
 |  | Mitigation Strategies: | | Developers should perform safety testing tailored to specific use cases. | 
 |  | 
| Input Output | 
| Input Format: | | Uses OpenAI's chatml standard prompt format | 
 |  | Accepted Modalities: |  |  | Performance Tips: | | For GPU offloading, consider VRAM capacity. | 
 |  |