Huggingface Llm Trainer
Plugin Verified ActiveTrain or fine-tune language models using TRL on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes hardware selection, cost estimation, Trackio monitoring, and Hub persistence.
Enables AI agents to easily train and fine-tune large language models on powerful cloud infrastructure without requiring local setup or complex configuration.
Features
- Train/fine-tune LLMs with TRL (SFT, DPO, GRPO)
- Utilize Hugging Face Jobs for cloud GPU training
- Convert models to GGUF for local deployment
- Estimate training cost and hardware requirements
- Integrate Trackio for real-time monitoring
Use Cases
- Fine-tuning LLMs for specific tasks on cloud GPUs
- Experimenting with different TRL training methods
- Converting trained models to GGUF for local inference
- Estimating the cost and time for LLM training jobs
Non-Goals
- Performing training directly on the user's local machine
- Managing Hugging Face Hub repositories beyond saving trained models
- Providing a UI for model training; all interactions are agent-driven
Installation
First, add the marketplace
/plugin marketplace add huggingface/skills/plugin install huggingface-llm-trainer@huggingface-skillsQuality Score
VerifiedTrust Signals
Similar Extensions
Huggingface Local Models
99Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
Huggingface Trackio
99Track and visualize ML training experiments with Trackio. Log metrics via Python API and retrieve them via CLI. Supports real-time dashboards synced to HF Spaces.
Hf Cli
99Execute Hugging Face Hub operations using the hf CLI. Download models/datasets, upload files, manage repos, and run cloud compute jobs.
Hugging Face Papers
100Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata like authors, linked models, datasets, Spaces, and media URLs when needed.
Prompt Governance
99Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, or creating eval pipelines for production
Hugging Face Skills
98Agent Skills for AI/ML tasks including dataset creation, model training, evaluation, and research paper publishing on Hugging Face Hub