PostTrainBench

🥈Silver

PostTrainBench measures how well CLI agents like Claude Code or Codex CLI can post-train base LLMs on a single H100 GPU in 10 hours. It benefits AI research teams and operations departments by providing a standardized way to evaluate post-training performance. The tool connects to existing CLI agents and integrates into AI development workflows.

127130Updated 1w ago

Intermediate30min to implementautomation

Saves ~240 min per use

Quick InstallView Source

git clone https://github.com/aisa-group/PostTrainBench.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/aisa-group/PostTrainBench

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Evaluate the post-training performance of [CLI_AGENT_NAME] on [BASE_LLM_MODEL] using [DATASET_NAME] for 10 hours on a single H100 GPU. Provide detailed metrics including accuracy, precision, recall, and F1 score. Compare the results with the pre-training performance.

Example Output

# Post-Training Evaluation Report

## Metrics
- **Accuracy**: 89.2% (Pre-training: 82.1%)
- **Precision**: 88.5% (Pre-training: 81.3%)
- **Recall**: 89.7% (Pre-training: 82.9%)
- **F1 Score**: 89.1% (Pre-training: 82.1%)

## Analysis
The post-training performance of [CLI_AGENT_NAME] on [BASE_LLM_MODEL] using [DATASET_NAME] shows significant improvements across all metrics. The increase in accuracy and F1 score indicates better overall performance and balance between precision and recall.

## Recommendations
- Continue post-training with similar datasets to further improve performance.
- Explore fine-tuning techniques to address specific use cases.
- Monitor performance on edge cases and outliers.