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AnushkaTandon26
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hugging-face-model-trainer

Train language models with TRL on Hugging Face Jobs.

Updated Mar 18, 2026
Search Tags:#training#huggingface#peft#trackio#gguf#trl#hub
One-click install
npx skills add https://github.com/AnushkaTandon26/AI-ML-Projects --skill hugging-face-model-trainer-anushkatandon26
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hugging-face-model-trainer
Source: https://github.com/AnushkaTandon26/AI-ML-Projects/tree/main/hugging-face-skills/skills/hugging-face-model-trainer
Command: npx skills add https://github.com/AnushkaTandon26/AI-ML-Projects --skill hugging-face-model-trainer-anushkatandon26

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl>=0.12.0, peft>=0.7.0, transformers>=4.36.0, accelerate>=0.24.0, trackio, torch>=2.0.0, huggingface_hub>=0.20.0, sentencepiece>=0.1.99, protobuf>=3.20.0, numpy, gguf, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill enables end-to-end training and fine-tuning of language models on Hugging Face Jobs using TRL, eliminating the need for local GPU setups when cloud resources are available.

Core Features & Use Cases

  • SFT, DPO, GRPO, and Reward Modeling training to align and customize models
  • GGUF conversion for local deployment with llama.cpp, Ollama, and LM Studio
  • Guidance on the TRL Jobs package, UV inline scripts with PEP 723 headers, dataset preparation/validation, hardware selection, cost estimation, Trackio monitoring, and Hub authentication to persist results
  • Suitable for tasks involving cloud GPU training, model persistence to the Hub, and production-grade experiment tracking

Quick Start

Submit an inline training script via hf_jobs to start TRL-based training on Hugging Face Jobs.

Frequently Asked Questions about hugging-face-model-trainer

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

FAQPage Schema
How do I train a language model on Hugging Face Jobs using TRL?▼

To train a language model on Hugging Face Jobs using TRL, submit an inline UV training script with a PEP 723 header. This enables cloud GPU workflows for SFT, DPO, GRPO, and reward modeling without local hardware.

Can I convert my fine-tuned Hugging Face model to GGUF for local deployment?▼

Yes, you can convert your fine-tuned model to GGUF format for local deployment. This skill provides guidance on GGUF conversions to run models locally using tools like llama.cpp, Ollama, and LM Studio.

What training methods does TRL support for aligning language models?▼

TRL supports SFT, DPO, GRPO, and reward modeling for aligning language models. These methods allow you to customize model behavior and improve performance on specific tasks during cloud GPU training.

Do I need local GPUs to fine-tune models with Hugging Face Jobs?▼

No, you do not need local GPUs to fine-tune models with Hugging Face Jobs. This skill enables end-to-end cloud-based training using TRL, eliminating the need for local GPU setups when cloud resources are available.

How do I monitor TRL training runs and persist results on the Hub?▼

You can monitor TRL training runs using Trackio and persist results via Hub authentication. This ensures production-grade experiment tracking and model persistence across cloud runtimes throughout the training process.

What dependencies are required for PEFT and TRL cloud training workflows?▼

PEFT and TRL cloud training workflows require dependencies including transformers, accelerate, torch, huggingface_hub, sentencepiece, and protobuf. These packages enable robust training and dataset preparation on cloud GPUs.

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