skills.rest
muzhicaomingwang
muzhicaomingwangCOMMUNITY
Explore all skills by this author →
GitHub

model

Coordinate model fine-tuning workflows from data curation to deployment packaging.

3|2|Updated Dec 26, 2025
Search Tags:#lora#model-training#machine-learning#finetuning#rlhf#sft
One-click install
npx skills add https://github.com/muzhicaomingwang/ai-ideas --skill model-muzhicaomingwang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model
Source: https://github.com/muzhicaomingwang/ai-ideas/tree/main/.project/ai/model/skills/model
Command: npx skills add https://github.com/muzhicaomingwang/ai-ideas --skill model-muzhicaomingwang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The skill provides a structured, end-to-end approach for developing, fine-tuning, evaluating, and deploying machine learning models, including data strategy, training configurations, safety alignment, and cost/performance trade-offs.

Core Features & Use Cases

  • Data strategy design and labeling guidelines for model fine-tuning.
  • Training setup options: SFT, LoRA/QLoRA, DPO/RLHF, with hyperparameter planning.
  • Evaluation and deployment planning, including safety checks and monitoring.
  • Use Case example: A startup wants to fine-tune a domain-specific recommender with efficient adapters and deploy with monitoring.

Quick Start

Design a dataset for fine-tuning, select a training strategy (e.g., LoRA), and run an initial evaluation plan.

Frequently Asked Questions about model

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

FAQPage Schema
How do I plan an end-to-end machine learning model fine-tuning workflow?▼

End-to-end model fine-tuning involves coordinating data curation, supervised fine-tuning (SFT), evaluation, and deployment packaging. You design a dataset, select a training strategy like LoRA, configure hyperparameters, and run an initial evaluation plan to manage the complete lifecycle.

How does RLHF and DPO fit into safety alignment for machine learning models?▼

RLHF and DPO are training strategies used for safety alignment and improving model responses. They are integrated into the model development workflow alongside supervised fine-tuning to ensure quality, efficiency, and safety through structured evaluation and governance.

Do I need a data strategy before starting supervised fine-tuning?▼

Yes, supervised fine-tuning requires a defined data strategy and labeling guidelines beforehand. Designing the dataset correctly before selecting a training strategy ensures the model learns domain-specific patterns effectively and meets the planned evaluation criteria.

Can I include deployment packaging and monitoring in my model training plan?▼

Yes, deployment packaging and monitoring are explicit parts of the end-to-end model lifecycle. After training and evaluation, the workflow includes safety checks and deployment planning to govern performance and enable continuous monitoring in production environments.

Related Skills

Explore More Software Engineering Skills→
maruakshaymaruakshay
4

fine-tuning-security

Detect data-poisoning and backdoor risks in fine-tuning pipelines.

Community
Advanced
danishsyed-devdanishsyed-dev
1

Deploying Machine Learning Models

Automate deployment of trained machine learning models into production environments.

Community
Advanced
OpenLAIROpenLAIR
1.0k

axolotl

Configure YAML-based Axolotl pipelines for LLM fine-tuning with RLHF and LoRA/QLoRA.

Official
Advanced
JamesFincherJamesFincher

peft-fine-tuning

Train low-rank adapters for parameter-efficient fine-tuning of large language models.

Community
Advanced