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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