Post by mdjalhokbabu on Feb 3, 2024 0:43:22 GMT -5
These are also high-capacity models, with skills and capabilities second only to the general-purpose large models that precede them. Llama 2 and Falcon are the best representatives of this category. They are usually as good as a Gen "N-1" or "N-2" model from the company that trained the general model. For example, according to some benchmarks, Llama2 is as good as GPT-3.5-turbo. Tuning these models on enterprise data can make them as good as first-class general-purpose large models on specific tasks.
Many of these models are open source (or close to it) and, once released, immediately bring improvements and optimizations from the open source community. Category 3: “Long Tail” Model These are "expert" models. They are built to TG Number List serve a specific purpose, such as classifying documents, identifying specific attributes in images or videos, identifying patterns in business data, etc. These models are flexible, cheap to train and use, and can run in the data center or at the edge. A brief glance at Hugging Face is enough to understand the sheer scale of this ecosystem now and in the future, as the range of use cases it serves is so broad. 2. Basic adaptation and practical cases Although it's still early days, we're
already seeing some leading development teams and enterprises thinking about ecosystems in this nuanced way. One wants to match usage to the best possible model. Even use multiple models to serve a more complex use case. Factors in evaluating which model/model to use typically include the following: Data privacy and compliance requirements: This affects whether the model needs to be run on enterprise infrastructure, or whether data can be sent to an externally hosted inference endpoint Whether the model allows fine-tuning Desired level of inference "performance" (latency, accuracy, cost, etc.) However, in reality the factors to consider are often much longer than those listed above, reflecting the huge diversity of use cases developers hope to implement with AI.
Many of these models are open source (or close to it) and, once released, immediately bring improvements and optimizations from the open source community. Category 3: “Long Tail” Model These are "expert" models. They are built to TG Number List serve a specific purpose, such as classifying documents, identifying specific attributes in images or videos, identifying patterns in business data, etc. These models are flexible, cheap to train and use, and can run in the data center or at the edge. A brief glance at Hugging Face is enough to understand the sheer scale of this ecosystem now and in the future, as the range of use cases it serves is so broad. 2. Basic adaptation and practical cases Although it's still early days, we're
already seeing some leading development teams and enterprises thinking about ecosystems in this nuanced way. One wants to match usage to the best possible model. Even use multiple models to serve a more complex use case. Factors in evaluating which model/model to use typically include the following: Data privacy and compliance requirements: This affects whether the model needs to be run on enterprise infrastructure, or whether data can be sent to an externally hosted inference endpoint Whether the model allows fine-tuning Desired level of inference "performance" (latency, accuracy, cost, etc.) However, in reality the factors to consider are often much longer than those listed above, reflecting the huge diversity of use cases developers hope to implement with AI.