Model Type | |
Use Cases |
Areas: | |
Applications: | dialogue, natural language generation |
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Primary Use Cases: | assistant-like chat, natural language generation tasks |
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Limitations: | use only in English, avoid inappropriate legal applications |
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Considerations: | Use proper formatting for optimal feature extraction and performance. |
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Additional Notes | AWQ models support efficient inference and faster execution. Compatible with vLLM and AutoAWQ. |
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Training Details |
Data Sources: | publicly available online data |
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Data Volume: | |
Methodology: | auto-regressive language model with transformer architecture, supervised fine-tuning (SFT), and reinforcement learning with human feedback (RLHF) |
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Context Length: | |
Hardware Used: | A100-80GB GPUs (TDP of 350-400W) |
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Model Architecture: | |
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Safety Evaluation |
Methodologies: | truthfulQA, Toxigen benchmarks |
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Findings: | Llama-2-Chat models demonstrate high-performance in safety tests |
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Risk Categories: | |
Ethical Considerations: | Testing conducted to date has been in English and may not cover all scenarios. Potential for inaccurate or objectionable responses exists. |
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Responsible Ai Considerations |
Fairness: | Testing conducted to date has been in English. Model may exhibit bias. |
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Transparency: | Limited model transparency as it is fine-tuned and pretrained with human feedback. |
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Accountability: | Meta is accountable for the model's performance and outputs. |
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Mitigation Strategies: | Developers advised to perform safety testing and tuning tailored to applications. |
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Input Output |
Input Format: | Structured text with prompt template |
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Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use 'INST' for better performance in chat tasks. |
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