Model Type | |
Use Cases |
Primary Use Cases: | Custom LLM system evaluation tasks. |
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Limitations: | Performance in specialized task domains like arithmetic or code evaluation may be limited., Support in non-English contexts has not been rigorously tested., Handling long context inputs or parsing structured data formats like JSON is limited. |
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Additional Notes | Model is quantized using Flow AI's AWQ safetensors quant. |
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Supported Languages | languages_supported (English), proficiency (Full support) |
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Training Details |
Data Sources: | Synthetically generated datasets |
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Methodology: | Supervised Fine-Tuning (SFT), RSLoRa fine-tuning, synthetic dataset generation |
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Context Length: | |
Model Architecture: | |
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Input Output |
Input Format: | Text prompt format with or without user inputs |
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Accepted Modalities: | |
Output Format: | Structured evaluation outputs including verbal feedback with <tags> and numeric scores. |
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Performance Tips: | Ensure modern GPU use and adherence to prompt templates for efficient evaluations. |
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