Search Results for "tuning"

...A wide collection of objective functions and methods to benchmark and compare algorithms. Define a problem statement and study configuration. Setup a local server, setup a client to connect to the server, perform a typical tuning loop, and use other client APIs.

...It also contains solutions or hints for many exercises so learners can check their work or explore alternative implementations. Because it’s educational, the repository emphasizes readability, clarity, and progressive learning rather than performance tuning or advanced constructs.

The Recommenders repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several...

...Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling component, and the reinforcement learning (or preference-based fine-tuning) phase. The repo also includes utilities for dataset handling, modeling architectures, inference, and evaluation. ...

...This 3rd version (started in 2006) helps to implement, preserve, share and reproduce the whole experimental setup as connected modules and data. cM uses crowdsourcing to leverage knowledge and computational resources of multiple users. For example, it includes multi-objective GCC, LLVM and ICC auto-tuning scenarios using shared benchmarks, codelets, data sets, tools, and combined with classification and predictive models. cM includes OpenME interactive interface to open up and expose internals of various third-party tools such as GCC, LLVM, run-time systems, etc. and connect them to cM through dynamic plugins that allows online analysis and tuning of programs and architectures. ...