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DeepScaleR - AI Model Libraries & Training Tool

Overview

DeepScaleR is an open-source project that democratizes reinforcement learning (RL) for large language models. The repository supplies training scripts, model checkpoints, hyperparameter configurations, datasets, and evaluation logs to reproduce and scale RL techniques on LLMs.

Key Features

  • Open-source training scripts for RL on LLMs
  • Provided model checkpoints and example weights
  • Detailed hyperparameter configurations for experiments
  • Datasets curated for reproducible RL research
  • Evaluation logs and metrics for result verification
  • Utilities and examples to scale RL training workflows

Ideal Use Cases

  • Reproducing RL experiments on language models
  • Researching RL methods applied to LLMs
  • Benchmarking RL training workflows and results
  • Sharing and comparing hyperparameter settings
  • Developing custom RL training pipelines for research

Getting Started

  • Clone the DeepScaleR repository from GitHub
  • Read the README and accompanying documentation
  • Install dependencies listed in the repository
  • Download available model checkpoints and datasets
  • Inspect provided hyperparameter configs and logs
  • Run example training scripts with small-scale settings
  • Adjust configs and scale experiments as needed

Pricing

Open-source project on GitHub; no pricing or commercial plans disclosed.

Limitations

  • Research-oriented; focused on reproducibility rather than turnkey deployment
  • Intended for users familiar with RL workflows and LLM training

Key Information

  • Category: Model Libraries & Training
  • Type: AI Model Libraries & Training Tool
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