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Huggingface Llm Trainer

huggingface/skills

  • 1.5k installs
  • 10.9k repo stars
  • Updated August 4, 2026
  • huggingface/skills

huggingface-llm-trainer is an agent skill for fine-tune and train llms with hugging face trainer, peft, and dataset tooling.

About

The huggingface-llm-trainer skill is designed for fine-tune and train LLMs with Hugging Face Trainer, PEFT, and dataset tooling. 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. Invoke when the user fine-tunes LLMs, configures Trainer, or prepares HF training datasets.

  • 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.
  • Fine-tune language models on cloud GPUs without local infrastructure.

Huggingface Llm Trainer by the numbers

  • 1,482 all-time installs (skills.sh)
  • +48 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #161 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Aug 6, 2026 (Skillselion catalog sync)
At a glance

huggingface-llm-trainer capabilities & compatibility

Capabilities
sft (supervised fine tuning) standard instruct · dpo (direct preference optimization) alignment · grpo (group relative policy optimization) onli · reward modeling train reward models for rlhf
From the docs

What huggingface-llm-trainer says it does

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward mode
SKILL.md
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO,
SKILL.md
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Last updatedAugust 4, 2026
Repositoryhuggingface/skills ↗

How do I fine-tune and train llms with hugging face trainer, peft, and dataset tooling?

Fine-tune and train LLMs with Hugging Face Trainer, PEFT, and dataset tooling.

Who is it for?

ML engineers running HF training jobs, LoRA fine-tunes, and eval loops.

Skip if: Skip for inference-only deployment without training or fine-tuning.

When should I use this skill?

User fine-tunes LLMs, configures Trainer, or prepares HF training datasets.

What you get

Completed huggingface-llm-trainer workflow with documented commands, files, and expected deliverables.

  • Quantized GGUF model file

By the numbers

  • Supports 3 quantization bit widths: 4-bit, 5-bit, and 8-bit
  • Typical 7B GGUF size 2–8 GB versus ~14 GB unquantized
  • Compatible with 5 local inference runtimes including Ollama and llama.cpp

Files

SKILL.mdMarkdownGitHub ↗

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:

1. 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().

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

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

4. 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: 1. Create the training script with Trackio included (use scripts/train_sft_example.py as template) 2. Submit immediately using hf_jobs() MCP tool with script content inline - don't save to file unless user requests 3. Report submission with job ID, monitoring URL, and estimated time 4. 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 = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset_split["train"],
    eval_dataset=dataset_split["test"],
    peft_config=LoraConfig(r=16, lora_alpha=32),
    args=SFTConfig(
        output_dir="my-model",
        push_to_hub=True,
        hub_model_id="username/my-model",
        num_train_epochs=3,
        eval_strategy="steps",
        eval_steps=50,
        report_to="trackio",
        project="meaningful_prject_name", # project name for the training name (trackio)
        run_name="meaningful_run_name",   # descriptive name for the specific training run (trackio)
    )
)

trainer.train()
trainer.push_to_hub()
""",
    "flavor": "a10g-large",
    "timeout": "2h",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

Benefits: Direct MCP tool usage, clean code, dependencies declared inline (PEP 723), no file saving required, full control When to use: Default choice for all training tasks in Claude Code, custom training logic, any scenario requiring hf_jobs()

Working with Scripts

⚠️ Important: The script parameter accepts either inline code (as shown above) OR a URL. Local file paths do NOT work.

Why local paths don't work: Jobs run in isolated Docker containers without access to your local filesystem. Scripts must be:

  • Inline code (recommended for custom training)
  • Publicly accessible URLs
  • Private repo URLs (with HF_TOKEN)

Common mistakes:

# ❌ These will all fail
hf_jobs("uv", {"script": "train.py"})
hf_jobs("uv", {"script": "./scripts/train.py"})
hf_jobs("uv", {"script": "/path/to/train.py"})

Correct approaches:

# ✅ Inline code (recommended)
hf_jobs("uv", {"script": "# /// script\n# dependencies = [...]\n# ///\n\n<your code>"})

# ✅ From Hugging Face Hub
hf_jobs("uv", {"script": "https://huggingface.co/user/repo/resolve/main/train.py"})

# ✅ From GitHub
hf_jobs("uv", {"script": "https://raw.githubusercontent.com/user/repo/main/train.py"})

# ✅ From Gist
hf_jobs("uv", {"script": "https://gist.githubusercontent.com/user/id/raw/train.py"})

To use local scripts: Upload to HF Hub first:

hf repos create my-training-scripts --type model
hf upload my-training-scripts ./train.py train.py
# Use: https://huggingface.co/USERNAME/my-training-scripts/resolve/main/train.py

Approach 2: TRL Maintained Scripts (Official Examples)

TRL provides battle-tested scripts for all methods. Can be run from URLs:

hf_jobs("uv", {
    "script": "https://github.com/huggingface/trl/blob/main/trl/scripts/sft.py",
    "script_args": [
        "--model_name_or_path", "Qwen/Qwen2.5-0.5B",
        "--dataset_name", "trl-lib/Capybara",
        "--output_dir", "my-model",
        "--push_to_hub",
        "--hub_model_id", "username/my-model"
    ],
    "flavor": "a10g-large",
    "timeout": "2h",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

Benefits: No code to write, maintained by TRL team, production-tested When to use: Standard TRL training, quick experiments, don't need custom code Available: Scripts are available from https://github.com/huggingface/trl/tree/main/examples/scripts

Finding More UV Scripts on Hub

The uv-scripts organization provides ready-to-use UV scripts stored as datasets on Hugging Face Hub:

# Discover available UV script collections
dataset_search({"author": "uv-scripts", "sort": "downloads", "limit": 20})

# Explore a specific collection
hub_repo_details(["uv-scripts/classification"], repo_type="dataset", include_readme=True)

Popular collections: ocr, classification, synthetic-data, vllm, dataset-creation

Approach 3: HF Jobs CLI (Direct Terminal Commands)

When the hf_jobs() MCP tool is unavailable, use the hf jobs CLI directly.

⚠️ CRITICAL: CLI Syntax Rules

# ✅ CORRECT syntax - flags BEFORE script URL
hf jobs uv run --flavor a10g-large --timeout 2h --secrets HF_TOKEN "https://example.com/train.py"

# ❌ WRONG - "run uv" instead of "uv run"
hf jobs run uv "https://example.com/train.py" --flavor a10g-large

# ❌ WRONG - flags AFTER script URL (will be ignored!)
hf jobs uv run "https://example.com/train.py" --flavor a10g-large

# ❌ WRONG - "--secret" instead of "--secrets" (plural)
hf jobs uv run --secret HF_TOKEN "https://example.com/train.py"

Key syntax rules: 1. Command order is hf jobs uv run (NOT hf jobs run uv) 2. All flags (--flavor, --timeout, --secrets) must come BEFORE the script URL 3. Use --secrets (plural), not --secret 4. Script URL must be the last positional argument

Complete CLI example:

hf jobs uv run \
  --flavor a10g-large \
  --timeout 2h \
  --secrets HF_TOKEN \
  "https://huggingface.co/user/repo/resolve/main/train.py"

Check job status via CLI:

hf jobs ps                        # List all jobs
hf jobs logs <job-id>             # View logs
hf jobs inspect <job-id>          # Job details
hf jobs cancel <job-id>           # Cancel a job

Approach 4: TRL Jobs Package (Simplified Training)

The trl-jobs package provides optimized defaults and one-liner training.

uvx trl-jobs sft \
  --model_name Qwen/Qwen2.5-0.5B \
  --dataset_name trl-lib/Capybara

Benefits: Pre-configured settings, automatic Trackio integration, automatic Hub push, one-line commands When to use: User working in terminal directly (not Claude Code context), quick local experimentation Repository: https://github.com/huggingface/trl-jobs

⚠️ In Claude Code context, prefer using `hf_jobs()` MCP tool (Approach 1) when available.

Hardware Selection

Model SizeRecommended HardwareCost (approx/hr)Use Case
<1B paramst4-small~$0.75Demos, quick tests only without eval steps
1-3B paramst4-medium, l4x1~$1.50-2.50Development
3-7B paramsa10g-small, a10g-large~$3.50-5.00Production training
7-13B paramsa10g-large, a100-large~$5-10Large models (use LoRA)
13B+ paramsa100-large, a10g-largex2~$10-20Very large (use LoRA)

GPU Flavors: cpu-basic/upgrade/performance/xl, t4-small/medium, l4x1/x4, a10g-small/large/largex2/largex4, a100-large, h100/h100x8

Guidelines:

  • Use LoRA/PEFT for models >7B to reduce memory
  • Multi-GPU automatically handled by TRL/Accelerate
  • Start with smaller hardware for testing

See: references/hardware_guide.md for detailed specifications

Critical: Saving Results to Hub

⚠️ EPHEMERAL ENVIRONMENT—MUST PUSH TO HUB

The Jobs environment is temporary. All files are deleted when the job ends. If the model isn't pushed to Hub, ALL TRAINING IS LOST.

Required Configuration

In training script/config:

SFTConfig(
    push_to_hub=True,
    hub_model_id="username/model-name",  # MUST specify
    hub_strategy="every_save",  # Optional: push checkpoints
)

In job submission:

{
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}  # Enables authentication
}

Verification Checklist

Before submitting:

  • [ ] push_to_hub=True set in config
  • [ ] hub_model_id includes username/repo-name
  • [ ] secrets parameter includes HF_TOKEN
  • [ ] User has write access to target repo

See: references/hub_saving.md for detailed troubleshooting

Timeout Management

⚠️ DEFAULT: 30 MINUTES—TOO SHORT FOR TRAINING

Setting Timeouts

{
    "timeout": "2h"   # 2 hours (formats: "90m", "2h", "1.5h", or seconds as integer)
}

Timeout Guidelines

ScenarioRecommendedNotes
Quick demo (50-100 examples)10-30 minVerify setup
Development training1-2 hoursSmall datasets
Production (3-7B model)4-6 hoursFull datasets
Large model with LoRA3-6 hoursDepends on dataset

Always add 20-30% buffer for model/dataset loading, checkpoint saving, Hub push operations, and network delays.

On timeout: Job killed immediately, all unsaved progress lost, must restart from beginning

Choose a Base Model (Model Selection)

Identify models to train based on task type or benchmark results.

Use scripts/hf_benchmarks.py to identify top-performing models for specific tasks. This helps the user select a model as the base for training, whilst keeping size and hardware constraints in mind.

# Get help on the benchmarks command:
uv run scripts/hf_benchmarks.py --help

Example -- choosing an OCR base model

# Search for benchmarks containing whose name contains the text `ocr`
uv run scripts/hf_benchmarks.py search --query ocr

# Get the ranked leaderboard for the allenai/olmOCR-bench benchmark 
uv run scripts/hf_benchmarks.py leaderboard allenai/olmOCR-bench

Cost Estimation

Offer to estimate cost when planning jobs with known parameters. Use scripts/estimate_cost.py:

uv run scripts/estimate_cost.py \
  --model meta-llama/Llama-2-7b-hf \
  --dataset trl-lib/Capybara \
  --hardware a10g-large \
  --dataset-size 16000 \
  --epochs 3

Output includes estimated time, cost, recommended timeout (with buffer), and optimization suggestions.

When to offer: User planning a job, asks about cost/time, choosing hardware, job will run >1 hour or cost >$5

Example Training Scripts

Production-ready templates with all best practices:

Load these scripts for correctly:

  • `scripts/train_sft_example.py` - Complete SFT training with Trackio, LoRA, checkpoints
  • `scripts/train_dpo_example.py` - DPO training for preference learning
  • `scripts/train_grpo_example.py` - GRPO training for online RL

These scripts demonstrate proper Hub saving, Trackio integration, checkpoint management, and optimized parameters. Pass their content inline to hf_jobs() or use as templates for custom scripts.

Monitoring and Tracking

Trackio provides real-time metrics visualization. See references/trackio_guide.md for complete setup guide.

Key points:

  • Add trackio to dependencies
  • Configure trainer with report_to="trackio" and run_name="meaningful_name"

Trackio Configuration Defaults

Use sensible defaults unless user specifies otherwise. When generating training scripts with Trackio:

Default Configuration:

  • Space ID: {username}/trackio (use "trackio" as default space name)
  • Run naming: Unless otherwise specified, name the run in a way the user will recognize (e.g., descriptive of the task, model, or purpose)
  • Config: Keep minimal - only include hyperparameters and model/dataset info
  • Project Name: Use a Project Name to associate runs with a particular Project

User overrides: If user requests specific trackio configuration (custom space, run naming, grouping, or additional config), apply their preferences instead of defaults.

This is useful for managing multiple jobs with the same configuration or keeping training scripts portable.

See references/trackio_guide.md for complete documentation including grouping runs for experiments.

Check Job Status

# List all jobs
hf_jobs("ps")

# Inspect specific job
hf_jobs("inspect", {"job_id": "your-job-id"})

# View logs
hf_jobs("logs", {"job_id": "your-job-id"})

Remember: Wait for user to request status checks. Avoid polling repeatedly.

Dataset Validation

Validate dataset format BEFORE launching GPU training to prevent the #1 cause of training failures: format mismatches.

Why Validate

  • 50%+ of training failures are due to dataset format issues
  • DPO especially strict: requires exact column names (prompt, chosen, rejected)
  • Failed GPU jobs waste $1-10 and 30-60 minutes
  • Validation on CPU costs ~$0.01 and takes <1 minute

When to Validate

ALWAYS validate for:

  • Unknown or custom datasets
  • DPO training (CRITICAL - 90% of datasets need mapping)
  • Any dataset not explicitly TRL-compatible

Skip validation for known TRL datasets:

  • trl-lib/ultrachat_200k, trl-lib/Capybara, HuggingFaceH4/ultrachat_200k, etc.

Usage

hf_jobs("uv", {
    "script": "https://huggingface.co/datasets/mcp-tools/skills/raw/main/dataset_inspector.py",
    "script_args": ["--dataset", "username/dataset-name", "--split", "train"]
})

The script is fast, and will usually complete synchronously.

Reading Results

The output shows compatibility for each training method:

  • `✓ READY` - Dataset is compatible, use directly
  • `✗ NEEDS MAPPING` - Compatible but needs preprocessing (mapping code provided)
  • `✗ INCOMPATIBLE` - Cannot be used for this method

When mapping is needed, the output includes a "MAPPING CODE" section with copy-paste ready Python code.

Example Workflow

# 1. Inspect dataset (costs ~$0.01, <1 min on CPU)
hf_jobs("uv", {
    "script": "https://huggingface.co/datasets/mcp-tools/skills/raw/main/dataset_inspector.py",
    "script_args": ["--dataset", "argilla/distilabel-math-preference-dpo", "--split", "train"]
})

# 2. Check output markers:
#    ✓ READY → proceed with training
#    ✗ NEEDS MAPPING → apply mapping code below
#    ✗ INCOMPATIBLE → choose different method/dataset

# 3. If mapping needed, apply before training:
def format_for_dpo(example):
    return {
        'prompt': example['instruction'],
        'chosen': example['chosen_response'],
        'rejected': example['rejected_response'],
    }
dataset = dataset.map(format_for_dpo, remove_columns=dataset.column_names)

# 4. Launch training job with confidence

Common Scenario: DPO Format Mismatch

Most DPO datasets use non-standard column names. Example:

Dataset has: instruction, chosen_response, rejected_response
DPO expects: prompt, chosen, rejected

The validator detects this and provides exact mapping code to fix it.

Converting Models to GGUF

After training, convert models to GGUF format for use with llama.cpp, Ollama, LM Studio, and other local inference tools.

What is GGUF:

  • Optimized for CPU/GPU inference with llama.cpp
  • Supports quantization (4-bit, 5-bit, 8-bit) to reduce model size
  • Compatible with Ollama, LM Studio, Jan, GPT4All, llama.cpp
  • Typically 2-8GB for 7B models (vs 14GB unquantized)

When to convert:

  • Running models locally with Ollama or LM Studio
  • Reducing model size with quantization
  • Deploying to edge devices
  • Sharing models for local-first use

See: references/gguf_conversion.md for complete conversion guide, including production-ready conversion script, quantization options, hardware requirements, usage examples, and troubleshooting.

Quick conversion:

hf_jobs("uv", {
    "script": "<see references/gguf_conversion.md for complete script>",
    "flavor": "a10g-large",
    "timeout": "45m",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"},
    "env": {
        "ADAPTER_MODEL": "username/my-finetuned-model",
        "BASE_MODEL": "Qwen/Qwen2.5-0.5B",
        "OUTPUT_REPO": "username/my-model-gguf"
    }
})

Common Training Patterns

See references/training_patterns.md for detailed examples including:

  • Quick demo (5-10 minutes)
  • Production with checkpoints
  • Multi-GPU training
  • DPO training (preference learning)
  • GRPO training (online RL)

Common Failure Modes

Out of Memory (OOM)

Fix (try in order): 1. Reduce batch size: per_device_train_batch_size=1, increase gradient_accumulation_steps=8. Effective batch size is per_device_train_batch_size x gradient_accumulation_steps. For best performance keep effective batch size close to 128. 2. Enable: gradient_checkpointing=True 3. Upgrade hardware: t4-small → l4x1, a10g-small → a10g-large etc.

Dataset Misformatted

Fix: 1. Validate first with dataset inspector:

   uv run https://huggingface.co/datasets/mcp-tools/skills/raw/main/dataset_inspector.py \
     --dataset name --split train

2. Check output for compatibility markers (✓ READY, ✗ NEEDS MAPPING, ✗ INCOMPATIBLE) 3. Apply mapping code from inspector output if needed

Job Timeout

Fix: 1. Check logs for actual runtime: hf_jobs("logs", {"job_id": "..."}) 2. Increase timeout with buffer: "timeout": "3h" (add 30% to estimated time) 3. Or reduce training: lower num_train_epochs, use smaller dataset, enable max_steps 4. Save checkpoints: save_strategy="steps", save_steps=500, hub_strategy="every_save"

Note: Default 30min is insufficient for real training. Minimum 1-2 hours.

Hub Push Failures

Fix: 1. Add to job: secrets={"HF_TOKEN": "$HF_TOKEN"} 2. Add to config: push_to_hub=True, hub_model_id="username/model-name" 3. Verify auth: mcp__huggingface__hf_whoami() 4. Check token has write permissions and repo exists (or set hub_private_repo=True)

Missing Dependencies

Fix: Add to PEP 723 header:

# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "missing-package"]
# ///

Troubleshooting

Common issues:

  • Job times out → Increase timeout, reduce epochs/dataset, use smaller model/LoRA
  • Model not saved to Hub → Check push_to_hub=True, hub_model_id, secrets=HF_TOKEN
  • Out of Memory (OOM) → Reduce batch size, increase gradient accumulation, enable LoRA, use larger GPU
  • Dataset format error → Validate with dataset inspector (see Dataset Validation section)
  • Import/module errors → Add PEP 723 header with dependencies, verify format
  • Authentication errors → Check mcp__huggingface__hf_whoami(), token permissions, secrets parameter

See: references/troubleshooting.md for complete troubleshooting guide

Resources

References (In This Skill)

  • references/training_methods.md - Overview of SFT, DPO, GRPO, KTO, PPO, Reward Modeling
  • references/training_patterns.md - Common training patterns and examples
  • references/unsloth.md - Unsloth for fast VLM training (~2x speed, 60% less VRAM)
  • references/gguf_conversion.md - Complete GGUF conversion guide
  • references/trackio_guide.md - Trackio monitoring setup
  • references/hardware_guide.md - Hardware specs and selection
  • references/hub_saving.md - Hub authentication troubleshooting
  • references/troubleshooting.md - Common issues and solutions
  • references/local_training_macos.md - Local training on macOS

Scripts (In This Skill)

  • scripts/train_sft_example.py - Production SFT template
  • scripts/train_dpo_example.py - Production DPO template
  • scripts/train_grpo_example.py - Production GRPO template
  • scripts/unsloth_sft_example.py - Unsloth text LLM training template (faster, less VRAM)
  • scripts/estimate_cost.py - Estimate time and cost (offer when appropriate)
  • scripts/convert_to_gguf.py - Complete GGUF conversion script
  • scripts/hf_benchmarks.py - Search for benchmark results and leaderboards by task, alias or free text.

External Scripts

  • Dataset Inspector - Validate dataset format before training (use via uv run or hf_jobs)

External Links

Key Takeaways

1. Submit scripts inline - The script parameter accepts Python code directly; no file saving required unless user requests 2. Jobs are asynchronous - Don't wait/poll; let user check when ready 3. Always set timeout - Default 30 min is insufficient; minimum 1-2 hours recommended 4. Always enable Hub push - Environment is ephemeral; without push, all results lost 5. Include Trackio - Use example scripts as templates for real-time monitoring 6. Offer cost estimation - When parameters are known, use scripts/estimate_cost.py 7. Use UV scripts (Approach 1) - Default to hf_jobs("uv", {...}) with inline scripts; TRL maintained scripts for standard training; avoid bash trl-jobs commands in Claude Code 8. Use hf_doc_fetch/hf_doc_search for latest TRL documentation 9. Validate dataset format before training with dataset inspector (see Dataset Validation section) 10. Choose appropriate hardware for model size; use LoRA for models >7B

Related skills

microsoftMicrosoft FoundryDeploy, evaluate, and continuously improve Microsoft Foundry agents from a single agent interface.490k1.3klllllllamaAi Research ReproductionOrchestrate trustworthy, auditable reproduction of deep learning repositories directly from their READMEs.176k511lllllllamaRun TrainSafely execute selected deep learning training commands with standardized evidence capture.176k511lllllllamaExplore RunSafely run isolated exploratory experiments with clear recording and conservative selection before committing changes.176k511lllllllamaPaper Context ResolverFetch precise reproduction-critical details like dataset splits, preprocessing steps, or evaluation protocols from the original academic paper when the repo README leav141k511lllllllamaRepo Intake And PlanScan unfamiliar AI research repositories and receive a minimal, trustworthy reproduction target before investing significant time.140k511

How it compares

Use huggingface-llm-trainer after TRL fine-tuning for local GGUF; use hosted inference endpoints when local quantization is unnecessary.

FAQ

What does huggingface-llm-trainer do?

Fine-tune and train LLMs with Hugging Face Trainer, PEFT, and dataset tooling.

When should I use huggingface-llm-trainer?

User fine-tunes LLMs, configures Trainer, or prepares HF training datasets.

Is huggingface-llm-trainer safe to install?

Review the Security Audits panel on this page before installing in production.

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