Model Type | |
Use Cases |
Areas: | |
Applications: | Translation services, Language learning |
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Additional Notes | Pretrained models should be used with LoRA models for optimal translation. |
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Training Details |
Data Sources: | human-written parallel data, triplet preference data, monolingual data |
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Data Volume: | 20B monolingual tokens for ALMA-7B and 12B monolingual tokens for ALMA-13B |
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Methodology: | Fine-tuning on monolingual data followed by parallel data optimization |
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Input Output |
Input Format: | Text prompt with source and target languages specified |
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Accepted Modalities: | |
Output Format: | |
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Release Notes |
Version: | |
Date: | |
Notes: | Fine-tuned on 20B monolingual tokens followed by parallel data optimization. |
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Version: | |
Date: | |
Notes: | LoRA fine-tuning on human-written parallel data. |
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Version: | |
Date: | |
Notes: | Further LoRA fine-tuning with contrastive preference optimization. |
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Version: | |
Date: | |
Notes: | Fine-tuned on 12B monolingual tokens followed by parallel data optimization. |
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Version: | |
Date: | |
Notes: | LoRA fine-tuning on human-written parallel data. |
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Version: | |
Date: | |
Notes: | Further LoRA fine-tuning with contrastive preference optimization. |
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