| Model Type | | text generation, instruction tuned |
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| Use Cases |
| Areas: | | Commercial use, Research use, Assistant-like chat |
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| Applications: | | Natural language generation tasks, Dialogue-focused applications |
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| Primary Use Cases: | | Chatbots, Text generation in English |
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| Limitations: | | Use in languages other than English requires compliance with license |
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| Considerations: | | Developers should perform safety testing tailored to specific applications |
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| Additional Notes | | Fine-tuning recommended for specific language support |
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| Supported Languages | |
| Training Details |
| Data Sources: | | Publicly available online data, Instruction datasets, Human-annotated examples |
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| Data Volume: | |
| Methodology: | | Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) |
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| Context Length: | |
| Training Time: | |
| Hardware Used: | |
| Model Architecture: | | Optimized transformer architecture with Grouped-Query Attention (GQA) |
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| Safety Evaluation |
| Methodologies: | | Red teaming, Adversarial evaluations, CyberSecEval safety eval suite |
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| Findings: | | Reduced residual risks of LLM Systems, Implemented safety mitigations |
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| Risk Categories: | | Cybersecurity, Child Safety |
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| Ethical Considerations: | | Industry-standard safety benchmarks and risk assessments were conducted |
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| Responsible Ai Considerations |
| Fairness: | | Openness, inclusivity, and helpfulness are core values |
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| Transparency: | | Commitment to responsible AI development and open community collaboration |
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| Accountability: | | Meta is responsible for maintaining safety guidelines and providing resources |
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| Mitigation Strategies: | | Implemented safeguards, refusal optimization, and community feedback loops |
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| Input Output |
| Input Format: | |
| Accepted Modalities: | |
| Output Format: | | Generates text and code only |
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| Release Notes |
| Version: | | Meta-Llama-3-8B-Instruct-ct2-int8_float16 |
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| Date: | |
| Notes: | | Quantized version with int8_float16 for CTranslate2 |
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