| Model Type | |
| Use Cases |
| Areas: | | chatbot applications, text generation |
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| Primary Use Cases: | | interactive chatbots, text completion |
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| Additional Notes | | This model is compact and designed for efficient computation and memory footprint. |
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| Supported Languages | |
| Training Details |
| Data Sources: | | cerebras/SlimPajama-627B, bigcode/starcoderdata, OpenAssistant/oasst_top1_2023-08-25 |
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| Data Volume: | |
| Methodology: | | Pretraining with optimization, same architecture and tokenizer as Llama 2, further aligned with TRL's DPOTrainer on UltraFeedback dataset |
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| Training Time: | |
| Hardware Used: | |
| Model Architecture: | |
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| Input Output |
| Input Format: | | JSON-like structured input for messages |
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| Accepted Modalities: | |
| Output Format: | |
| Performance Tips: | | Use proper transformers and accelerate installation for optimal performance |
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