- The Open Interpreter repository provides a natural language interface for computers.
- It enables users to interact with their computer systems through a chat-like interface in the terminal.
- Open Interpreter supports various programming languages, including Python, Javascript, Shell, and more.
- The repository offers installation instructions, usage examples, and an interactive demo.
- GitHub repository for a tutorial series called "0 to LitGPT."
- Provides an overview of how to get started with LitGPT, which is an open-source implementation of GPT-3.
- Offers various resources such as codes, issues, pull requests, actions, security features, insights, and more related to the LitGPT project.
As a quick refresher, the Data Dirtiness Score estimates the expected proportion of cells in a data set that contain errors. Here are the key hypotheses behind this metric:
Data errors are related to violated constraints.
If there are no expectations, there is no effect on the score.
Data problems can be pinpointed to specific cells.
Each data error is assigned a confidence score.
Every cell has an equal impact on the overall score.
Cerebrum 8x7b is a large language model (LLM) created specifically for reasoning tasks. It is based on the Mixtral 8x7b model. Similar to its smaller version, Cerebrum 7b, it is fine-tuned on a small custom dataset of native chain of thought data and further improved with targeted RLHF (tRLHF), a novel technique for sample-efficient LLM alignment. Unlike numerous other recent fine-tuning approaches, our training pipeline includes under 5000 training prompts and even fewer labeled datapoints for tRLHF.
Native chain of thought approach means that Cerebrum is trained to devise a tactical plan before tackling problems that require thinking. For brainstorming, knowledge intensive, and creative tasks Cerebrum will typically omit unnecessarily verbose considerations.
How can an LLM be applied effectively for biomedical entity linking? Entity linking involves recognizing and extracting entities within the text and mapping them to standardized concepts in a large terminology. I
Credential-stealing emails are getting past artificial intelligence's "known good" email security controls by cloaking malicious payloads within seemingly benign emails. The tactic poses a significant threat to enterprise networks.