How are the models deployed to production?

Each example includes deployment configurations for Modal, a serverless platform, allowing you to quickly turn your trained models into live inference APIs.

What machine learning models are included in these examples?

The skill includes templates for DistilBERT (sentiment classification), GPT-2 with LoRA (text generation), and a custom transformer architecture for financial trade classification.

Can I use my own datasets with these ML templates?

Yes, each project is designed to be extensible. You can replace the sample JSON or CSV files with your own data and modify the data loading logic within the training scripts.

Do I need a GPU to run these training examples?

While sentiment classification can run on a CPU, a GPU is highly recommended for text generation and required for the trade classifier to achieve reasonable training times.

ML Training Examples

Name: ML Training Examples
Author: vanman2024

byvanman2024

0•

Data Science & ML

Provides production-ready machine learning templates and training workflows for classification, text generation, and financial analysis.

This skill equips Claude with specialized knowledge for implementing end-to-end machine learning workflows through production-grade reference implementations. It offers three distinct, runnable project templates: binary sentiment classification using DistilBERT, efficient GPT-2 text generation using LoRA (Low-Rank Adaptation), and a sophisticated financial trade classifier featuring technical indicator engineering and Modal deployment. Whether you are a beginner looking for starter templates or an experienced developer needing robust training scripts and deployment patterns, this skill streamlines the process of moving from raw data to a production-ready model.

Key Features

01Sentiment classification templates using DistilBERT with production inference APIs

02Production-ready deployment configurations for Modal serverless infrastructure

03Efficient text generation fine-tuning scripts utilizing LoRA and GPT-2

04Automated project scaffolding and standardized training execution scripts

05Financial trade classification with custom feature engineering and technical indicators

060 GitHub stars

Use Cases

01Building and deploying financial prediction models with market data features

02Fine-tuning transformer models for custom NLP tasks like sentiment analysis

03Learning production-grade ML training, monitoring, and deployment patterns

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add vanman2024/ai-dev-marketplace example-projects

For use in Claude.ai and ChatGPT

Key Features

01Sentiment classification templates using DistilBERT with production inference APIs

02Production-ready deployment configurations for Modal serverless infrastructure

03Efficient text generation fine-tuning scripts utilizing LoRA and GPT-2

04Automated project scaffolding and standardized training execution scripts

05Financial trade classification with custom feature engineering and technical indicators

060 GitHub stars

Use Cases

01Building and deploying financial prediction models with market data features

02Fine-tuning transformer models for custom NLP tasks like sentiment analysis

03Learning production-grade ML training, monitoring, and deployment patterns