| Model Type | |
| Use Cases |
| Areas: | |
| Primary Use Cases: | | assistant-like chat, natural language generation tasks |
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| Limitations: | | Use in languages other than English, Violations of laws or regulations |
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| Considerations: | | Follow specific formatting for inputs |
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| Additional Notes | | The fine-tuning data includes publicly available instruction datasets and human-annotated examples. |
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| Training Details |
| Data Sources: | | publicly available datasets |
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| Data Volume: | |
| Methodology: | | Supervised Fine-tuning and Reinforcement Learning with Human Feedback |
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| Hardware Used: | | Meta's Research Super Cluster, third-party cloud compute |
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| Model Architecture: | | auto-regressive language model with optimized transformer architecture |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | |
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| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Llama 2 models ranging from 7 billion to 70 billion parameters released, optimized for dialogue and alignment with human preferences. |
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