Mamba-Chat is a chat language model built on a state-space model architecture rather than a transformer. The repository provides training and fine-tuning code based on a modified Huggingface Trainer class. The model is based on Mamba-2.8B and was fine-tuned on 16,000 samples of the HuggingFaceH4/ultrachat_200k dataset. Code is provided for testing and fine-tuning, including a command-line chatbot and a Gradio app.
The caret package provides a set of functions in R intended to streamline creating predictive models. Its tools cover data splitting, pre-processing, feature selection, model tuning using resampling and variable importance estimation, and it offers a uniform interface and standardized common tasks.
Charisma.ai is a conversational AI platform for building interactive scenarios for online training and campaigns. It offers realistic conversation simulations and real-time analytics on engagement, with publication to web, VR or game engine experiences through SDKs. Plans include a pay-as-you-go option and an enterprise tier with development options.
Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
Annotated talk slides by Arvind Narayanan and Sayash Kapoor of Princeton University, dated October 4, 2023, on evaluating large language models. The slides discuss three hard problems in LLM evaluation: prompt sensitivity, construct validity and contamination, and examine questions about ChatGPT's behavior over time and its political bias.
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. It includes tools for converting text to features, classifiers such as Naïve Bayes, Maximum Entropy and Decision Trees, sequence tagging with Hidden Markov Models and Conditional Random Fields, and sampling-based topic model implementations.
The MXNet Clojure package brings GPU computing and deep learning to the Clojure programming language. It enables tensor and matrix computation with multiple GPUs in Clojure, and allows constructing and customizing deep learning models for tasks such as image classification and data science challenges.
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The caret package provides a set of functions in R intended to streamline creating predictive models. Its tools cover data splitting, pre-processing, feature selection, model tuning using resampling and variable importance estimation, and it offers a uniform interface and standardized common tasks.
Charisma.ai is a conversational AI platform for building interactive scenarios for online training and campaigns. It offers realistic conversation simulations and real-time analytics on engagement, with publication to web, VR or game engine experiences through SDKs. Plans include a pay-as-you-go option and an enterprise tier with development options.
Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.
Annotated talk slides by Arvind Narayanan and Sayash Kapoor of Princeton University, dated October 4, 2023, on evaluating large language models. The slides discuss three hard problems in LLM evaluation: prompt sensitivity, construct validity and contamination, and examine questions about ChatGPT's behavior over time and its political bias.
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. It includes tools for converting text to features, classifiers such as Naïve Bayes, Maximum Entropy and Decision Trees, sequence tagging with Hidden Markov Models and Conditional Random Fields, and sampling-based topic model implementations.
The MXNet Clojure package brings GPU computing and deep learning to the Clojure programming language. It enables tensor and matrix computation with multiple GPUs in Clojure, and allows constructing and customizing deep learning models for tasks such as image classification and data science challenges.
