chiloanerk

chiloanerk / scala-neural-language-model

Public

Neural language model built from scratch in Scala 3 with GPU acceleration via Apple Metal

10
1
100% credibility
Found Mar 25, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Scala
AI Summary

A pure Scala tool for training small neural language models on text files to predict next words, with interactive menus for training, prediction, benchmarking, and optional Apple GPU acceleration.

How It Works

1
🔍 Discover the word predictor

You find this fun project that lets you teach a computer to guess the next word in sentences from your own stories.

2
💻 Start the program

Open the simple launcher on your Apple laptop and see a friendly menu of choices.

3
Pick your adventure
📚
Teach it

Train on stories to make it smarter at guessing.

💭
Guess words

Type a sentence and see what comes next.

4
🚀 Watch it learn

Select your text file, pick an easy training plan, and see progress bars as it reads and remembers words.

5
✨ Try predictions

Give it starting words like 'the cat' and get top guesses for what follows.

🎉 Your predictor is ready

Now you have a personal word guesser that understands your stories and suggests continuations.

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AI-Generated Review

What is scala-neural-language-model?

This Scala 3 project implements a neural language model from scratch for next-word prediction in natural language processing, echoing Bengio's classic NLM approach. Users train small models on plain text corpora via a polished CLI, supporting batched runs, early stopping, checkpoints, and continual learning across multiple inputs. It delivers GPU acceleration on Apple Silicon via Metal, alongside CPU fallback, for tasks like prediction and throughput benchmarking.

Why is it gaining traction?

Unlike heavyweight Python neural network libraries, it runs pure Scala code with no ML frameworks, making it ideal for lightweight neural language model experiments with Apple acceleration. The CLI handles chunking large files, replay buffers to combat forgetting, and detailed metrics reporting for regressions. Developers appreciate the 9.5x GPU speedup on M1 hardware and zero-setup JNI for Metal.

Who should use this?

Scala developers prototyping neural language models in NLP without Python dependencies, especially on Apple Silicon for accelerated training. Researchers exploring neural language networks at birth or psycholinguistic representations via simple next-word tasks. Hobbyists benchmarking custom corpora on GitHub neural amp setups.

Verdict

Worth forking for educational neural language programming on Apple (solid CLI/docs/tests), but at 10 stars and 1.0% credibility, it's early-stage—expect tweaks for production. Pair with larger corpora for real utility.

(187 words)

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