posttrain

What problem does it solve?

Post-training engineering guidelines to systematically improve an AI model's capabilities, focusing on robust methodologies and repeatable processes rather than hacks.

Core Features & Use Cases

• Data strategy optimization to improve generalization and data quality checks.
• Compute and pipeline guidance for efficient, scalable post-training workflows (SFT, Alignment, evaluation).
• Frameworks and guardrails to ensure reproducible experiments and safe deployments across teams.

Quick Start

Audit current post-training workflow and establish a repeatable evaluation plan for capability improvement.