LLMs

The OpenAI Assistants API offers more than a simple prompt-sharing interface; it provides a sophisticated framework for AI interactions. It allows for persistent conversation sessions with automatic context management (Threads), structured interactions (Messages and Runs), integration with various tools for enhanced capabilities, customization... See more

Deploying a Generative AI model requires more than a VM with a GPU. It normally includes:

• Container Service : Most often Kubernetes to run LLM Serving solutions like Hugging Face Text Generation Inference or vLLM.


• Compute Resources : GPUs for running models, CPUs for management services


• Networking and DNS : Routing traffic to the appropriate

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memary: Open-Source Longterm Memory for Autonomous Agents

memary demo

Why use memary?

Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve... See more

One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more

core components of Deep RL that enabled success like AlphaGo: self-play and look-ahead planning.

Self-play is the idea that an agent can improve its gameplay by playing against slightly different versions of itself because it’ll progressively encounter more challenging situations. In the space of LLMs, it is almost certain that the largest portion... See more

You can think your way into solving a deterministic system, but you cannot think your way into solving a probabilistic system.

The first thing that I want to call out is that deterministic software has edge cases, while probabilistic software has long tails.

I find that a lot of junior folks try to really think hard about edge cases around... See more

We consider these aspects of our problem:

• Latency : How fast does the system need to respond to user input?


• Task Complexity : What level of understanding is required from the LLM? Is the input context and prompt super domain-specific?


• Prompt Length : How much context needs to be provided for the LLM to do its task?


• Quality : What is the acceptable

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Study finds RLHF reduces LLM creativity and output variety : A new research paper posted in /r/LocalLLaMA shows that while alignment techniques like RLHF reduce toxic and biased content, they also limit the creativity of large language models, even in contexts unrelated to safety.