LLM Model Family
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An LLM Model Family is a model family of LLM models.
- Context:
- It can (typically) include a series of models with varying sizes and capabilities within the same architecture.
- It can (often) encompass models that are fine-tuned for different tasks, such as text generation, question answering, or summarization.
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- It can range from being a small-scale model family suitable for edge devices to being a large-scale model family designed for cloud deployment.
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- It can incorporate improvements over time, such as enhanced training data, refined architecture, and better performance metrics.
- It can be used by organizations to leverage the strengths of different model versions based on the specific requirements of a task.
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- Example(s):
- Grok 2 Model Family, which includes a variety of models designed for different NLP tasks with enhanced efficiency.
- GPT Model Family, which includes models like GPT-2, GPT-3, and GPT-4, each with increased capabilities and larger training data sets.
- BERT Model Family, which includes variations such as BERT-Base, BERT-Large, and DistilBERT, used for tasks like language understanding and classification.
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- Counter-Example(s):
- Single-task Model, which is designed to perform only one specific task, unlike a model family that covers a range of tasks.
- See: Model Family, LLM Model, Pre-trained Model