Model Type | text-generation, conversational, instruction following, reasoning, function calling |
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Use Cases |
Areas: | research, commercial applications |
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Applications: | instruction-following, knowledge-driven QA, reasoning, truthful answer generation, function calling |
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Primary Use Cases: | Conversational AI, Function Calling |
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Additional Notes | Model merging was done using SLERP method with Llama-Spark model. |
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Supported Languages | en (Proficient), de (Proficient), fr (Proficient), it (Proficient), pt (Proficient), hi (Proficient), es (Proficient), th (Proficient) |
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Training Details |
Data Sources: | |
Data Volume: | |
Methodology: | Self-Curation and Spectrum-based targeted fine-tuning |
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Model Architecture: | |
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Input Output |
Input Format: | Transformed user queries using chat-template |
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Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use bfloat16 model type for optimal performance. |
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