LLMs

How can we make interacting with conversational models feel more natural?

Every conversational interface to a language model adopts the same pattern:

A chat history sidebar, with each conversation lasting just a few turns

New sessions always begin in a brand-new thread

Every user query must always elicit exactly one response

None of these assumptions... See more

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𝘱𝘦𝘳𝘧𝘰𝘳𝘮𝘢𝘯𝘤𝘦: it will improve your LLM performance on given use cases (e.g., coding, extracting text, etc.). Mainly, the LLM will specialize in a given task (a specialist will always beat a generalist in its domain)

𝘤𝘰𝘯𝘵𝘳𝘰𝘭: you can refine how your model should behave on specific inputs and outputs, resulting in a more robust product

𝘮𝘰𝘥𝘶𝘭𝘢𝘳𝘪𝘻𝘢𝘵𝘪𝘰𝘯:... See more

𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝗼𝗳 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗮𝗻 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗟𝗠 𝗲𝘅𝗶𝘀t ↓

- 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘦𝘥 𝘱𝘳𝘦-𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨: utilize domain-specific data to apply the same pre-training process (next token prediction) on the pre-trained (base) model

- 𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵𝘪𝘰𝘯 𝘧𝘪𝘯𝘦-𝘵𝘶𝘯𝘪𝘯𝘨: the pre-trained (base) model is fine-tuned on a Q&A dataset to learn to answer questions

- 𝘚𝘪𝘯𝘨𝘭𝘦-𝘵𝘢𝘴𝘬 𝘧𝘪𝘯𝘦-𝘵𝘶𝘯𝘪𝘯𝘨: the... See more

To do this, we employ a technique known as AI-assisted evaluation, alongside traditional metrics for measuring performance. This helps us pick the prompts that lead to better quality outputs, making the end product more appealing to users. AI-assisted evaluation uses best-in-class LLMs (like GPT-4) to automatically critique how well the AI's... See more

A solution is to self-host an open-sourced or custom fine-tuned LLM. Opting for a self-hosted model can reduce costs dramatically - but with additional development time, maintenance overhead, and possible performance implications. Considering self-hosted solutions requires weighing these different trade-offs carefully.

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

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