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Best Practices & ###### results problem #77
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Hi @MyraBaba
Unfortunately, it is hard to answer this question since it depends on the model and the task. The goal of the package is to enable users to choose the model they like without forcing any specific model, since "best" or "accurate" are subjective. A good place to look for new models or ask for the best model for the specific task would be https://www.reddit.com/r/LocalLLaMA/
Summarization is not in the scope of this project - it was built for question-answering.
This is not coming from the package, but from the model itself - some models might require different prompt templates for asking the question. You can consult the model card e.g. Huggingface and update the prompt template in the config like specified here -
As long as it is supported by llamacpp, it should be similar - download the model (e.g. in gguf format), and specify the path in the config. Some models work better with different default parameters - you can configure it in the config.yaml, similar to here -
Can you clarify the question, please?
Quite hard to answer - some big players are entering the space, e.g. Microsoft already offers services for enterprise-grade document question answering, where models can be deployed to an organization's private network and data doesn't leave the perimeter. I think there will be a space for open-source projects like this for small organisations and privacy-conscious users, but hard to tell if it can be monetized. |
@snexus |
Hi @snexus
Thanks for your efforts and beatifull project.
1 . Would you mind to give more example for hugginaface llamacpp models ? ie what would be the best accurate result for multilingual or language different from the english ?
2 - What would be the choice for summarization document ?
3 - sometimes result adding many ##### and stopping
4 - Example config for mistral , mixtral and dolphin2
5 Do you know haystack looks commercialized version.
6 what will be the future for enterprise AI search for internal documents ? Is it worth to invest ? Can we talk ?
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