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
- General models: (: (GPT, BERT,Xlent.. )
- Specialized models: ( T%, ERNIE..)
- Specific models for conversations: (Ras NLE, DialoGPT o RAG ..)
- 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.
- Consider Security: If you are going to train local models, security is crucial, as mentioned earlier. This involves conducting extensive tests to ensure that the models do not exhibit unexpected behaviors.
- 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 and Open AI.
Information Retrieval Capability:: RAG (Retrieve-and-Generate)
The RAG model (Retrieve-and-Generate) stands out from other models for its ability to retrieve relevant information before generating a response. Unlike general models, RAG incorporates very specific knowledge present in proprietary documentation, making the responses more accurate..
Customizable and Adaptable. You can always customize the model for specific tasks by retrieving relevant information according to the context of the query.
Enhanced Conversations: As mentioned, it provides more coherent and relevant answers than other general and open systems with multiple interpretations..
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.
IIntegration with Databases. You can integrate the model into your own databases, making it much more efficient for your needs..
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..
In conclusion, in this exciting journey toward artificial intelligence, it is essential to enjoy each step but also to navigate with caution and always use our compass and navigation systems if we do not want to be surprised by unexpected storms or "sleeper agents.”
Soon, we will sail together with our natural language model! 🚀😊 But, of course, safely, coherently, and usefully!
Let It work for you!
