skills.rest
Tyler-R-Kendrick
Tyler-R-KendrickCOMMUNITY
Explore all skills by this author →
GitHub

huggingface-llm-trainer

Train and fine-tune language and vision models on Hugging Face Jobs infrastructure.

1|Updated Jul 12, 2026
Search Tags:#llm#fine-tuning#gpu#training#huggingface#gguf#trl
One-click install
npx skills add https://github.com/Tyler-R-Kendrick/slm-training --skill huggingface-llm-trainer-tyler-r-kendrick
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-llm-trainer
Source: https://github.com/Tyler-R-Kendrick/slm-training/tree/main/.agents/skills/huggingface-llm-trainer
Command: npx skills add https://github.com/Tyler-R-Kendrick/slm-training --skill huggingface-llm-trainer-tyler-r-kendrick

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, peft, torch, accelerate, huggingface_hub, sentencepiece, protobuf, numpy, gguf, trl, trackio, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill removes the complexity and hardware requirements of training language and vision models by providing a streamlined interface to Hugging Face's managed cloud infrastructure.

Core Features & Use Cases

  • Managed Training: Execute SFT, DPO, GRPO, and Reward Modeling jobs on cloud GPUs without local infrastructure.
  • Model Conversion: Convert fine-tuned models into GGUF format for local deployment in Ollama, LM Studio, or llama.cpp.
  • Validation & Monitoring: Includes automated dataset format validation to prevent training failures and real-time training metrics via Trackio.

Quick Start

Submit a supervised fine-tuning job for the Qwen 0.5B model using the Capybara dataset on an a10g-large GPU instance.

Frequently Asked Questions about huggingface-llm-trainer

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

FAQPage Schema
How do I fine-tune a Hugging Face model without local GPU hardware?▼

You can fine-tune models without local GPUs by running SFT, DPO, GRPO, and reward modeling workflows on Hugging Face Jobs cloud infrastructure. This managed environment handles training execution and provides real-time metrics via Trackio.

Can I convert a fine-tuned model to GGUF format for local deployment?▼

Yes, you can convert fine-tuned models into GGUF format. This allows you to deploy your trained language or vision models locally using compatible runtimes like Ollama, LM Studio, or llama.cpp.

Do I need Hugging Face authentication to run training jobs?▼

Yes, Hugging Face authentication is required. Validating your credentials allows the training scripts to access datasets, allocate cloud GPU instances, and push the resulting models or GGUF conversions to your repositories.

How does dataset validation work for TRL training workflows?▼

The skill includes automated dataset format validation to prevent training failures. It checks your input data against TRL requirements before execution, ensuring datasets are correctly structured for SFT, DPO, or GRPO workflows.

What is the best way to monitor training metrics in an ephemeral cloud environment?▼

You can monitor training metrics using integrated Trackio support. This provides real-time visibility into your ephemeral cloud training jobs, allowing you to track progress and diagnose issues without maintaining a separate logging server.

Related Skills

Explore More Software Engineering Skills→
NagyViktNagyVikt
2

huggingface-llm-trainer

Submit SFT, DPO, and GRPO training jobs to Hugging Face infrastructure.

Community
Advanced
ScientiaCapitalScientiaCapital
1

model-deployment

Export fine-tuned models to GGUF and deploy with vLLM or Ollama.

Community
Advanced
svngokusvngoku
8

unsloth-hf-jobs

Fine-tune LLMs and VLMs on HF Jobs with Unsloth for GPU training.

Community
Advanced
Andrew-GirgisAndrew-Girgis

huggingface-llm-trainer

Train and fine-tune language and vision models on Hugging Face Jobs.

Community
Advanced