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hf-transformers-trainer

Fine-tune and align local LLMs with HuggingFace Trainer, PEFT, and TRL.

17|3|Updated Apr 3, 2026
Search Tags:#fine-tune#huggingface#peft#qlora#trl#trainer#vram-optimization
One-click install
npx skills add https://github.com/jayll1303/AIEKit --skill hf-transformers-trainer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hf-transformers-trainer
Source: https://github.com/jayll1303/AIEKit/tree/main/.kiro/skills/hf-transformers-trainer
Command: npx skills add https://github.com/jayll1303/AIEKit --skill hf-transformers-trainer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Fine-tuning and aligning large language models on local GPU hardware is complex, memory-constrained, and error-prone; this Skill consolidates proven Trainer, PEFT (LoRA/QLoRA), and TRL workflows so you can configure training, avoid OOMs, and run alignment reliably on local machines.

Core Features & Use Cases

  • Training configuration templates for TrainingArguments, data collators, schedulers, logging, callbacks, and evaluation to get reproducible Trainer runs.
  • PEFT LoRA / QLoRA recipes including BitsAndBytes config, prepare_model_for_kbit_training, recommended r/alpha, and adapter merging patterns.
  • TRL alignment workflows for SFT, DPO, and GRPO with dataset format requirements, reward function examples, and validation checks.
  • VRAM estimation & optimization: gradient checkpointing, bf16/fp16 guidance, DeepSpeed/FSDP suggestions, packing, and monitoring tips to prevent OOMs.
  • Dataset & tokenization patterns for instruction tuning, ChatML/Llama formats, label masking, and preference pair generation.
  • Real-world example: run QLoRA + LoRA adapters to SFT a 7B model on an 8–12 GB GPU, then continue with DPO using prepared preference pairs.

Quick Start

Use the hf-transformers-trainer guidance to fine-tune a Llama-family model locally with QLoRA and LoRA adapters while applying gradient checkpointing and recommended TrainingArguments.

Frequently Asked Questions about hf-transformers-trainer

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

FAQPage Schema
How do I fine-tune a LLM locally with QLoRA to avoid out-of-memory errors?▼

Fine-tune a LLM locally with QLoRA by applying BitsAndBytes 4-bit quantization, LoRA adapters, gradient checkpointing, and recommended TrainingArguments to fit a 7B model on an 8–12 GB GPU. This Skill provides VRAM optimization recipes and troubleshooting guidance to prevent OOMs during training.

What is the difference between SFT, DPO, and GRPO alignment in TRL workflows?▼

SFT, DPO, and GRPO are TRL alignment workflows for large language models. SFT performs supervised fine-tuning on instruction datasets, DPO uses preference pairs for direct preference optimization, and GRPO applies reward functions. This Skill provides dataset format requirements and trainer workflows for each alignment method.

How do I format datasets and apply tokenization for instruction tuning?▼

Format datasets for instruction tuning by structuring ChatML or Llama formats with proper label masking and preference pair generation. This Skill provides dataset preparation patterns and tokenization workflows required by HuggingFace Trainer and TRL alignment trainers before running the actual model training.

Can I train a 7B model on a single local GPU with limited VRAM?▼

Training a 7B model on a single local GPU requires VRAM optimization techniques like gradient checkpointing, bf16 or fp16 precision, and parameter-efficient fine-tuning. This Skill provides VRAM estimation and configuration templates to run QLoRA and LoRA adapter training reliably on 8–12 GB consumer GPUs.

What are the recommended LoRA adapter settings for parameter-efficient fine-tuning?▼

Recommended LoRA adapter settings for parameter-efficient fine-tuning include specific r and alpha values, BitsAndBytes configuration for quantization, and prepare_model_for_kbit_training initialization. This Skill provides PEFT recipes with adapter merging patterns to configure and optimize local LLM training.

Why does my HuggingFace Trainer run fail during local model alignment?▼

HuggingFace Trainer runs fail during local model alignment due to incorrect dataset formatting, VRAM exhaustion, or missing gradient checkpointing configurations. This Skill provides validation checks for TRL workflow datasets, VRAM estimation tools, and TrainingArguments templates to troubleshoot and prevent local training failures.

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