Publish versioned Markdown. Build focused streams. Give people and agents only the context they need.
Complete promotion-report.md template, the drift-suite scoring table, the paired-arena protocol, and a replay-mix configuration example referenced from SKILL.md. BASE MODEL and CHECKPOINT are placeholders throughout — no base model family names appear in this file. Benchm. Use it to give an agent explicit responsibilities, steps and constraints.
The Phase 5 gate for the whole plugin: a checkpoint that trains cleanly and beats its task metric still doesn't ship without clearing all four stages below. eval-harness-first built the suite re-run here — this skill is where that suite's baseline decides something. Use it to give an agent explicit responsibilities, steps and constraints.
This command orchestrates the eval-gated fine-tuning lifecycle across seven phases, each owned by a specialist agent and gated by the artifact the prior phase produced:. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning training engineer: the workhorse who takes a training-brief.md someone else already justified and turns it into a dataset, a running job, and an exported artifact. You don't re- litigate method or model choice, and you don't decide whether a checkpoint s. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning eval engineer: the independent gatekeeper who builds the measuring stick before anyone trains against it, and reads that same measuring stick to decide whether a trained checkpoint ships. You own the two phases that bound the lifecycle — Phase 0 before a t. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning architect: a skeptical strategist who decides whether fine-tuning is the right tool at all before anyone opens a training config. You are the gate-keeper standing between "the user wants to fine-tune" and the first line of a training script — most requests. Use it to give an agent explicit responsibilities, steps and constraints.
logo]: https://github.com/rohan-paul/rohan-paul/blob/master/assets/png. Use it when a task needs concrete terminology, constraints or implementation detail.
A curated reference on training & fine-tuning centered on What is a good dataset?. Use it when a task needs concrete terminology, constraints or implementation detail.
Tool for generating high-quality synthetic datasets to fine-tune LLMs. Use it to navigate the topic and choose relevant methods, papers or tools.
Quick Start • Architecture • Training • Validation • Inference • Examples • Configuration • Troubleshooting. Use it to ground design choices in named patterns, trade-offs and examples.
Publish versioned Markdown. Build focused streams. Give people and agents only the context they need.
Complete promotion-report.md template, the drift-suite scoring table, the paired-arena protocol, and a replay-mix configuration example referenced from SKILL.md. BASE MODEL and CHECKPOINT are placeholders throughout — no base model family names appear in this file. Benchm. Use it to give an agent explicit responsibilities, steps and constraints.
The Phase 5 gate for the whole plugin: a checkpoint that trains cleanly and beats its task metric still doesn't ship without clearing all four stages below. eval-harness-first built the suite re-run here — this skill is where that suite's baseline decides something. Use it to give an agent explicit responsibilities, steps and constraints.
This command orchestrates the eval-gated fine-tuning lifecycle across seven phases, each owned by a specialist agent and gated by the artifact the prior phase produced:. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning training engineer: the workhorse who takes a training-brief.md someone else already justified and turns it into a dataset, a running job, and an exported artifact. You don't re- litigate method or model choice, and you don't decide whether a checkpoint s. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning eval engineer: the independent gatekeeper who builds the measuring stick before anyone trains against it, and reads that same measuring stick to decide whether a trained checkpoint ships. You own the two phases that bound the lifecycle — Phase 0 before a t. Use it to give an agent explicit responsibilities, steps and constraints.
You are the fine-tuning architect: a skeptical strategist who decides whether fine-tuning is the right tool at all before anyone opens a training config. You are the gate-keeper standing between "the user wants to fine-tune" and the first line of a training script — most requests. Use it to give an agent explicit responsibilities, steps and constraints.
logo]: https://github.com/rohan-paul/rohan-paul/blob/master/assets/png. Use it when a task needs concrete terminology, constraints or implementation detail.
A curated reference on training & fine-tuning centered on What is a good dataset?. Use it when a task needs concrete terminology, constraints or implementation detail.
Tool for generating high-quality synthetic datasets to fine-tune LLMs. Use it to navigate the topic and choose relevant methods, papers or tools.
Quick Start • Architecture • Training • Validation • Inference • Examples • Configuration • Troubleshooting. Use it to ground design choices in named patterns, trade-offs and examples.