ollama - 💡(How to fix) Fix [Feature]: Support encoder models [1 participants]

TL;DR

Consider using encoders with magnitude, such as ColBERT or GLiNER, for tasks like schema extraction, NER, and multimodal retrieval, as they are more efficient than decoder models.

Guidance

• Explore the use of ColPali, ColQwen, or Nemotron encoders for specific tasks where efficiency is crucial.
• Evaluate the performance of ModernColBERT for tasks that require structured prediction.
• Review the vllm-factory repository (https://github.com/ddickmann/vllm-factory) for implementation details and examples of using these encoders.
• Assess the trade-offs between using encoders and decoder models for different tasks, considering factors like efficiency and accuracy.

Notes

The choice of encoder or decoder model depends on the specific task requirements and the trade-offs between efficiency and accuracy. Further experimentation and evaluation may be necessary to determine the best approach.

Recommendation

Apply workaround: Use encoders with magnitude for tasks where they are more efficient, and decoder models for tasks that require their specific capabilities, as this allows for a more tailored approach to each task's requirements.