| Model Type | |
| Use Cases |
| Areas: | |
| Applications: | |
| Primary Use Cases: | | Translating Chinese to English |
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| Considerations: | | For usage with LoRA models. |
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| Additional Notes | | ALMA-R incorporates Contrastive Preference Optimization (CPO) for improved performance. |
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| Supported Languages | | Monolingual Data (Multi-lingual), Parallel Data (High-quality parallel data) |
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| Training Details |
| Data Sources: | | 20B monolingual tokens, high-quality parallel data, triplet preference data |
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| Data Volume: | | 20B tokens for 7B model and 12B tokens for 13B model |
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| Methodology: | | Two-step fine-tuning with monolingual data followed by parallel data; Further optimized with Contrastive Preference Optimization (CPO) |
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| Context Length: | |
| Model Architecture: | | LLaMA based, fine-tuned with LoRA |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Use LoRA models together with Base Models for intended performance. |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Full-weight Fine-tune LLaMA-2-7B on 12B monolingual tokens and then LoRA fine-tune on human-written parallel data |
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| Version: | |
| Date: | |
| Notes: | | Further LoRA fine-tuning upon ALMA-13B-LoRA with contrastive preference optimization |
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