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kailash-align

Automate LLM fine-tuning with TRL methods and enforce evaluation before serving.

Updated Oct 10, 2025
Search Tags:#alignment#trl#llm fine-tuning#lora adapters#eval-before-serve#ollama deployment#vllm serving
One-click install
npx skills add https://github.com/FFOO6866/lead2cash --skill kailash-align
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kailash-align
Source: https://github.com/FFOO6866/lead2cash/tree/main/.claude/skills/35-kailash-align
Command: npx skills add https://github.com/FFOO6866/lead2cash --skill kailash-align

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents unsafe or underperforming fine-tuned LLM deployments by providing an end-to-end training, evaluation, and serving workflow with a hard evaluation-before-serving gate.

Core Features & Use Cases

  • End-to-end alignment pipeline: Orchestrates training across multiple TRL-based alignment methods and adapter lifecycles.
  • 12 supported alignment methods: Covers offline, unpaired, monolithic, and online training patterns (including LoRA-based adapter management and chaining for multi-stage flows like sft_then_dpo).
  • Mandatory eval-before-serve: Requires benchmarking and safety checks (including base-model comparison) before any model can be deployed.
  • Deployment options for real runtimes: Exports to GGUF, deploys to Ollama, serves via vLLM, and connects deployed models into Kaizen agents through a bridge.

Quick Start

Ask the AI to train and align a DPO adapter, then evaluate it and deploy it to Ollama or vLLM only if evaluation passes.

Frequently Asked Questions about kailash-align

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce an evaluation gate before serving a fine-tuned LLM?▼

To enforce an eval-before-serve gate for fine-tuned LLMs, you can use a pipeline that refuses deployment to vLLM or Ollama unless an eval_result and safety checks pass. This prevents unsafe or underperforming models from reaching production.

What's the best way to chain LoRA adapters for multi-stage LLM alignment?▼

Chaining LoRA adapters for multi-stage LLM alignment is best handled by orchestrating TRL and PEFT training sequences like sft_then_dpo. This allows offline preference alignment and RLHF-style online optimization across summarization and reasoning tasks.

Can I deploy a TRL fine-tuned model directly to Ollama or vLLM?▼

Yes, you can deploy TRL fine-tuned models to Ollama or vLLM by exporting to GGUF format. Deployment requires passing mandatory benchmarking, base-model comparison, and configured regression checks first.

Why does LLM deployment fail without an evaluation result?▼

LLM deployment fails without an evaluation result because an enforced evaluation-before-serving gate blocks it. This mandatory check ensures the fine-tuned model passes safety and regression validations before being served.

Do I need specific dataset formats for different TRL alignment methods?▼

Yes, you need method-specific dataset formats for different TRL alignment methods. The fine-tuning pipeline requires correctly formatted datasets to properly execute offline preference alignment and online RLHF optimization.

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