Huggingface Llm Trainer

What This Skill Does

This skill enables users to train language models using TRL (Transformer Reinforcement Learning) on Hugging Face's managed infrastructure. It supports various TRL methods like SFT, DPO, and GRPO, eliminating the need for local GPU setup and automatically saving models to the Hugging Face Hub. It's designed for users who want to fine-tune models without local infrastructure.

When to Use

• Fine-tune language models on cloud GPUs.
• Train with TRL methods (SFT, DPO, GRPO, etc.
• ).
• Run training jobs on Hugging Face Jobs.
• Convert trained models to GGUF for local deployment.
• Ensure trained models are saved to the Hub.
• Use Unsloth for memory efficiency and speed.

Key Features

• Submits jobs using the hf_jobs() MCP tool.
• Supports TRL methods (SFT, DPO, GRPO).
• Automatically saves models to the Hugging Face Hub.
• Offers Unsloth integration for memory efficiency.
• Eliminates the need for local GPU setup.
• Uses modern workflows with optimized defaults.

TRL Training on Hugging Face Jobs

Overview

Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub.

TRL provides multiple training methods: SFT (Supervised Fine-Tuning) - Standard instruction tuning DPO (Direct Preference Optimization) - Alignment from preference data GRPO (Group Relative Policy Optimization) - Online RL training Reward Modeling - Train reward models for RLHF

For detailed TRL method documentation: hf_doc_search("your query", product="trl") hf_doc_fetch("https://huggingface.co/docs/trl/sft_trainer") # SFT hf_doc_fetch("https://huggingface.co/docs/trl/dpo_trainer") # DPO etc.

See also: references/training_methods.md for method overviews and selection guidance

When to Use This Skill

Use this skill when users want to: Fine-tune language models on cloud GPUs without local infrastructure Train with TRL methods (SFT, DPO, GRPO, etc.) Run training jobs on Hugging Face Jobs infrastructure Convert trained models to GGUF for local deployment (Ollama, LM Studio, llama.cpp) Ensure trained models are permanently saved to the Hub Use modern workflows with optimized defaults

When to Use Unsloth

Use Unsloth (references/unsloth.md) instead of standard TRL when: Limited GPU memory - Unsloth uses ~60% less VRAM Speed matters - Unsloth is ~2x faster Training large models (>13B) - memory efficiency is critical Training Vision-Language Models (VLMs) - Unsloth has FastVisionModel support

See references/unsloth.md for complete Unsloth documentation and scripts/unsloth_sft_example.py for a production-ready training script.

Key Directives

When assisting with training jobs:

• ALWAYS use hf_jobs() MCP tool - Submit jobs using hf_jobs("uv", {.
• }), NOT bash trl-jobs commands.
• The script parameter accepts Python code directly.
• Do NOT save to local files unless the user explicitly requests it.
• Pass the script content as a string to hf_jobs().
• If user asks to "train a model", "fine-tune", or similar requests, you MUST create the training script AND submit the job immediately using hf_jobs().

Always include Trackio - Every training script should include Trackio for real-time monitoring. Use example scripts in scripts/ as templates.

Provide job details after submission - After submitting, provide job ID, monitoring URL, estimated time, and note that the user can request status checks later.

Use example scripts as templates - Reference scripts/train_sft_example.py, scripts/train_dpo_example.py, etc. as starting points.

Local Script Execution

Repository scripts use PEP 723 inline dependencies. Run them with uv run: uv run scripts/estimate_cost.py --help uv run scripts/dataset_inspector.py --help

Prerequisites Checklist

Before starting any training job, verify:

✅ Account & Authentication

Hugging Face Account with Pro, Team, or Enterprise plan (Jobs require paid plan) Authenticated login: Check with hf_whoami() HF_TOKEN for Hub Push ⚠️ CRITICAL - Training environment is ephemeral, must push to Hub or ALL training results are lost Token must have write permissions
MUST pass secrets={"HF_TOKEN": "$HF_TOKEN"} in job config to make token available (the $HF_TOKEN syntax references your actual token value)

✅ Dataset Requirements

Dataset must exist on Hub or be loadable via datasets.load_dataset() Format must match training method (SFT: "messages"/text/prompt-completion; DPO: chosen/rejected; GRPO: prompt-only) ALWAYS validate unknown datasets before GPU training to prevent format failures (see Dataset Validation section below) Size appropriate for hardware (Demo: 50-100 examples on t4-small; Production: 1K-10K+ on a10g-large/a100-large)

⚠️ Critical Settings

• Timeout must exceed expected training time - Default 30min is TOO SHORT for most training.
• Minimum recommended: 1-2 hours.
• Job fails and loses all progress if timeout is exceeded. Hub push must be enabled - Config: push_to_hub=True, hub_model_id="username/model-name"; Job: secrets={"HF_TOKEN": "$HF_TOKEN"}

Asynchronous Job Guidelines

⚠️ IMPORTANT: Training jobs run asynchronously and can take hours

Action Required

When user requests training: Create the training script with Trackio included (use scripts/train_sft_example.py as template) Submit immediately using hf_jobs() MCP tool with script content inline - don't save to file unless user requests Report submission with job ID, monitoring URL, and estimated time Wait for user to request status checks - don't poll automatically

Ground Rules

Jobs run in background - Submission returns immediately; training continues independently Initial logs delayed - Can take 30-60 seconds for logs to appear User checks status - Wait for user to request status updates Avoid polling - Check logs only on user request; provide monitoring links instead

After Submission

Provide to user: ✅ Job ID and monitoring URL ✅ Expected completion time ✅ Trackio dashboard URL ✅ Note that user can request status checks later

Example Response: ✅ Job submitted successfully!

Job ID: abc123xyz Monitor: https://huggingface.co/jobs/username/abc123xyz

Expected time: ~2 hours Estimated cost: ~$10

The job is running in the background. Ask me to check status/logs when ready!

Quick Start: Three Approaches

💡 Tip for Demos: For quick demos on smaller GPUs (t4-small), omit eval_dataset and eval_strategy to save ~40% memory. You'll still see training loss and learning progress.

Sequence Length Configuration

TRL config classes use max_length (not max_seq_length) to control tokenized sequence length:

✅ CORRECT - If you need to set sequence length

SFTConfig(max_length=512) # Truncate sequences to 512 tokens DPOConfig(max_length=2048) # Longer context (2048 tokens)

❌ WRONG - This parameter doesn't exist SFTConfig(max_seq_length=512) # TypeError!

Default behavior: max_length=1024 (truncates from right). This works well for most training.

When to override: Longer context: Set higher (e.g., max_length=2048) Memory constraints: Set lower (e.g., max_length=512) Vision models: Set max_length=None (prevents cutting image tokens)

Usually you don't need to set this parameter at all - the examples below use the sensible default.

Approach 1: UV Scripts (Recommended—Default Choice)

UV scripts use PEP 723 inline dependencies for clean, self-contained training. This is the primary approach for Claude Code.

hf_jobs("uv", { "script": """ /// script dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio"] ///

from datasets import load_dataset from peft import LoraConfig from trl import SFTTrainer, SFTConfig import trackio

dataset = load_dataset("trl-lib/Capybara", split="train")

Create train/eval split for monitoring dataset_split = dataset.train_test_split(test_size=0.1, seed=42)

trainer =