Artificial Intelligence has come to stay, and many sectors are already beginning to offer exciting opportunities. However, like everything in life, it is not exempt from significant challenges, especially when we talk about language models (LLM).

In the fascinating world of language models (LLM), the ability to generate coherent text in seconds is a great advantage for our innovative and efficient applications. However, caution is needed as we must now be very careful with the choice of models to train, especially those that are open source.o.   

In general, it may be convenient to work with models that have already been tested and endorsed by the community, such as (Open AI, Google, Meta, Microsoft, etc.), if we want to avoid a high number of concerns. However, it is essential not to leave systems open without monitoring...

For all these reasons, as a starting point before analyzing various language models such as

1. General models: (: (GPT, BERT,Xlent.. )
2. Specialized models: ( T%, ERNIE..)
3. Specific models for conversations: (Ras NLE, DialoGPT o RAG ..)
4. Multilingual models: ( mBERT..)

It would be advisable to consider some pre-analysis points with several important factors to take into account when making a decision:

• Reliable Source. As mentioned earlier, using reliable sources is a good option as they have undergone rigorous testing and multiple evaluations.s.
• Hugging Face as a Resource Hugging Face is a reliable platform that aggregates and distributes open-source language models. Models hosted on Hugging Face are usually well-maintained and used by the community
• Avoid Unnecessary Local Training: If you only require simple functions such as questions and answers that do not require training from scratch, you can use pre-trained and reliable models.
• Tener en cuenta la seguridad. Si vas a entrenar modelos locales es muy importante la seguridad como hemos comentado.. Esto conlleva tener que realizar pruebas muy exhaustivas para que los modelos no tengan comportamientos no esperados
• Consider Communities and Evaluations: Having the support of the community and participation is a very positive point to consider. They can provide information about the reliability and effectiveness of the model..

If you decide to train local models, make security your best ally, as you will need to conduct many tests. However, rest assured, you can have a very secure alternative to train local and customizable models if you use and combine the...l..

Modelo RAG y Open AI. 

​Capacidad de recuperar información: El modelo RAG (Retrieve-and-Generate)se diferencia del resto de modelos en su capacidad de recuperar información relevante antes de generar respuesta. A diferencia de modelos generales, RAG incorpora conocimiento muy específicos presentes en documentación propietarias lo que hace que las respuestas sean más precisas.

Customizable and Adaptable. You can always customize the model for specific tasks by retrieving relevant information according to the context of the query.

Conversaciones mejoradas: Como comentamos, da respuestas más coherentes y relevantes que otros sistemas generales y abiertos a múltiples interpretaciones.

Security and Trust: Security is a challenge, as mentioned. Some models may exhibit unexpected or malicious behaviors. By using RAG, the exposure to risks is significantly reduced as the information is local and not in the public domain.

Integración con bases de datos. Puedes integrar el modelo en tus propias BBDD lo que lo hace mucho más eficiente para tus necesidades.

Flexibility in Using Instructions and Prompts. RAG can be more flexible in terms of how you provide instructions or prompts, allowing you to influence the generated content more specifically.. 

En definitiva, y en este emocionante viaje hacia la inteligencia artificial, es esencial disfrutar cada paso, pero también navegar con prudencia y utilizar siempre nuestra brújula y nuestros sistemas de navegación, si no queremos vernos sorprendidos por temporales inesperados o “agentes durmientes”. 

Para profundizar en el impacto de la inteligencia artificial en los data centers, te recomiendo leer nuestro artículo sobre la Inteligencia Artificial en los Data Centers.

Soon, we will sail together with our natural language model! 🚀😊 But, of course, safely, coherently, and usefully!

                                                                                       Let It work for you!