65 posts tagged “llm-tool-use”
Tool use is when an LLM is instructed to occasionally request that an external tool be run on its behalf, with the result passed back to the model for further processing. Sometimes also known as function calling, and one of several ideas that might be referred to as "agents".
2024
Mistral Large. Mistral Medium only came out two months ago, and now it's followed by Mistral Large. Like Medium, this new model is currently only available via their API. It scores well on benchmarks (though not quite as well as GPT-4) but the really exciting feature is function support, clearly based on OpenAI's own function design.
Functions are now supported via the Mistral API for both Mistral Large and the new Mistral Small, described as follows:
Mistral Small, optimised for latency and cost. Mistral Small outperforms Mixtral 8x7B and has lower latency, which makes it a refined intermediary solution between our open-weight offering and our flagship model.
2023
Introducing Claude 2.1. Anthropic’s Claude used to have the longest token context of any of the major models: 100,000 tokens, which is about 300 pages. Then GPT-4 Turbo came out with 128,000 tokens and Claude lost one of its key differentiators.
Claude is back! Version 2.1, announced today, bumps the token limit up to 200,000—and also adds support for OpenAI-style system prompts, a feature I’ve been really missing.
They also announced tool use, but that’s only available for a very limited set of partners to preview at the moment.
Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
I think it’s now possible to train a large language model with similar functionality to GPT-3 for $85,000. And I think we might soon be able to run the resulting model entirely in the browser, and give it capabilities that leapfrog it ahead of ChatGPT.
[... 1,751 words]A simple Python implementation of the ReAct pattern for LLMs. I implemented the ReAct pattern (for Reason+Act) described in this paper. It's a pattern where you implement additional actions that an LLM can take - searching Wikipedia or running calculations for example - and then teach it how to request that those actions are run, then feed their results back into the LLM.
The surprising ease and effectiveness of AI in a loop (via) Matt Webb on the langchain Python library and the ReAct design pattern, where you plug additional tools into a language model by teaching it to work in a “Thought... Act... Observation” loop where the Act specifies an action it wishes to take (like searching Wikipedia) and an extra layer of software than carries out that action and feeds back the result as the Observation. Matt points out that the ChatGPT 1/10th price drop makes this kind of model usage enormously more cost effective than it was before.