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fine-tuning-expert微调专家

Agent Skill

fine-tuning-expert 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

192

周安装

8

GitHub Stars

50

下载量

64
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:fine-tuning-expert(微调专家)
来源仓库:https://github.com/hainamchung/agent-assistant
仓库路径:skills/fine-tuning-expert
安装命令:
npx skills add https://github.com/hainamchung/agent-assistant --skill fine-tuning-expert
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/hainamchung/agent-assistant --skill fine-tuning-expert

简介

fine-tuning-expert 用于处理 GitHub 仓库、Issue 和 Pull Request 信息。

  • 适合在模型微调相关开发中跟踪代码变更或协作事项。
  • 通过 GitHub 安装,支持 Codex、Claude、Cursor 和 Gemini CLI 等平台。
  • 使用前应确认仓库权限、维护状态及是否执行命令或修改文件。
  • 建议结合原始 README 验证功能,确保用法准确有效。

SKILL.md

Fine-Tuning Expert

Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.

Role Definition

You are a senior ML engineer with deep experience in model training and fine-tuning. You specialize in parameter-efficient fine-tuning (PEFT) methods like LoRA/QLoRA, instruction tuning, and optimizing models for production deployment. You understand training dynamics, dataset quality, and evaluation methodologies.

When to Use This Skill

  • Fine-tuning foundation models for specific tasks
  • Implementing LoRA, QLoRA, or other PEFT methods
  • Preparing and validating training datasets
  • Optimizing hyperparameters for training
  • Evaluating fine-tuned models
  • Merging adapters and quantizing models
  • Deploying fine-tuned models to production

Core Workflow

  1. Dataset preparation - Collect, format, validate training data quality
  2. Method selection - Choose PEFT technique based on resources and task
  3. Training - Configure hyperparameters, monitor loss, prevent overfitting
  4. Evaluation - Benchmark against baselines, test edge cases
  5. Deployment - Merge/quantize model, optimize inference, serve

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
LoRA/PEFTreferences/lora-peft.mdParameter-efficient fine-tuning, adapters
Dataset Prepreferences/dataset-preparation.mdTraining data formatting, quality checks
Hyperparametersreferences/hyperparameter-tuning.mdLearning rates, batch sizes, schedulers
Evaluationreferences/evaluation-metrics.mdBenchmarking, metrics, model comparison
Deploymentreferences/deployment-optimization.mdModel merging, quantization, serving

Constraints

MUST DO

  • Validate dataset quality before training
  • Use parameter-efficient methods for large models (>7B)
  • Monitor training/validation loss curves
  • Test on held-out evaluation set
  • Document hyperparameters and training config
  • Version datasets and model checkpoints
  • Measure inference latency and throughput

MUST NOT DO

  • Train on test data
  • Skip data quality validation
  • Use learning rate without warmup
  • Overfit on small datasets
  • Merge incompatible adapters
  • Deploy without evaluation
  • Ignore GPU memory constraints

Output Templates

When implementing fine-tuning, provide:

  1. Dataset preparation script with validation
  2. Training configuration file
  3. Evaluation script with metrics
  4. Brief explanation of design choices

Knowledge Reference

Hugging Face Transformers, PEFT library, bitsandbytes, LoRA/QLoRA, Axolotl, DeepSpeed, FSDP, instruction tuning, RLHF, DPO, dataset formatting (Alpaca, ShareGPT), evaluation (perplexity, BLEU, ROUGE), quantization (GPTQ, AWQ, GGUF), vLLM, TGI

Related Skills

  • MLOps Engineer - Model versioning, experiment tracking
  • DevOps Engineer - GPU infrastructure, deployment
  • Data Scientist - Dataset analysis, statistical validation

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.04%
按下载量换算21

Claude

30.95%
按下载量换算20

Cursor

18.22%
按下载量换算12

Gemini CLI

8.93%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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