Search Results for "model train"

Llama 2 Everywhere (L2E) is an open-source implementation of the LLaMA-2 large language model architecture designed to demonstrate how transformer-based language models can be executed with extremely minimal code. The project focuses on simplicity and educational clarity by implementing inference for LLaMA-style models in a compact C program rather than relying on large machine learning frameworks. Developers can train models using a Python training pipeline and then run inference using a lightweight C implementation that requires very few dependencies. ...

Run a fast ChatGPT-like model locally on your device. This combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface. Download the zip file corresponding to your operating system from the latest release.

Training a model can be a long long process. In the meantime, have a bit of fun with a competitive space invaders game. See if you can get your name to the top of the leaderboard.

*** Note “BlueTrain.x.zip” is encrypted while awaiting hardware for this F/W.*** The “Open-Source-Bluetooth-Train-Control” group at TrainBoard.com provides direction for this project. The public thread is here: http://www.trainboard.com/highball/index.php?threads/the-open-source-bluetooth-train-control-thread.79261/page-4#post-963231. The private groups are no longer available at TrainBoard.com. The first purpose of this project is to promote open-interfaces for DBTC. The second purpose...

...Pre-trained word vectors are made available under the Public Domain Dedication and License. If the web datasets above don't match the semantics of your end use case, you can train word vectors on your own corpus. The demo.sh script downloads a small corpus, consisting of the first 100M characters of Wikipedia. It collects unigram counts, constructs and shuffles cooccurrence data, and trains a simple version of the GloVe model. It also runs a word analogy evaluation script in python to verify word vector quality.