Idea or nah? Memory balancing

John6666
Sun, 23 Feb 2025 03:39:08 +0000

There are probably a number of reasons for this. I think it’s because of the technical difficulties involved, and the fact that there is not enough demand for it despite the difficulties. I think there are many people who would want it if it were available…

・Because training requires huge GPU resources, in many cases, large-scale training is carried out and released once every few months “internally” at each company. Therefore, each model is like a stranger to the human, independent, and it is difficult to share the method of managing memory

・At present, it is barely possible to perform inferences in near real time, but training neural networks in real time through dialogue is extremely difficult from the perspective of GPU resources

・There are several ways to achieve long-term memory externally, such as through databases, but in reality, it is more convenient to create your own depending on the use case, so there is no definitive version

・If you want the latest information, you can just search using RAG or function calling, so many people just do that, and what is required of the model itself is the “thinking” part that selects the correct options, so for many people, the memory itself is not that important

etc.

github.com

GitHub - huggingface/smolagents: 🤗 smolagents: a barebones library for agents....

🤗 smolagents: a barebones library for agents. Agents write python code to call tools and orchestrate other agents.

github.com

GitHub - AkiRusProd/llm-agent: LLM using long-term memory through vector database

LLM using long-term memory through vector database