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Use Cases |
Primary Use Cases: | Instruction-based coding in Python, based on instructions written in English or Russian |
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Limitations: | Model is not aligned to human preferences for safety, No moderation mechanisms, Trained on code based instruction so may produce problematic outputs without filtering |
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Considerations: | Users should be aware of risks, biases, and limitations of the model. |
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Additional Notes | This adapter model was trained using `bitsandbytes` quantization config: 4-bit load, nf4 quant type, float16 compute dtype. |
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Supported Languages | ru (high), en (high), Python (high) |
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Training Details |
Data Sources: | zelkame/ru-stackoverflow-py, MexIvanov/Vezora-Tested-22k-Python-Alpaca-ru, MexIvanov/CodeExercise-Python-27k-ru |
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Methodology: | Special training methods or approaches used, such as fine-tuning techniques. |
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Model Architecture: | A LoRA (Peft) adapter model trained on a mix of publicly available data and machine-translated synthetic python coding datasets. |
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Responsible Ai Considerations |
Mitigation Strategies: | Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. |
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Input Output |
Input Format: | <|system|> ~~ <|user|> {prompt}~~ <|assistant|> |
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Accepted Modalities: | |
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