trl/SKILL.md

metadata

name: trl
description: >-
  This skill should be used when users want to train or fine-tune language
  models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs
  infrastructure. Covers SFT, DPO, GRPO, KTO, reward modeling, and PPO training
  methods, plus GGUF conversion for local deployment. Includes guidance on the
  TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and
  validation, hardware selection, cost estimation, Trackio monitoring, Hub
  authentication, and model persistence. Should be invoked for tasks involving
  cloud GPU training, GGUF conversion, or when users mention training on Hugging
  Face Jobs without local GPU setup.
license: Complete terms in LICENSE.txt

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
• KTO (Kahneman-Tversky Optimization) - Preference tuning without paired data
• Reward Modeling - Train reward models for RLHF
• PPO (Proximal Policy Optimization) - Classic RLHF method

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, KTO, 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

Key Directives

When assisting with training jobs:

1. Submit jobs directly with inline scripts - 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.

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 Dependencies

To run scripts locally (like validate_dataset.py, estimate_cost.py), install dependencies:

pip install -r requirements.txt

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 mcp__huggingface__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 and is automatically available as $HF_TOKEN in job secrets

✅ 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)
• Use scripts/validate_dataset.py to verify format or hf_doc_fetch("https://huggingface.co/docs/trl/dataset_formats") for complete reference
• 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

Approach 1: TRL Jobs Package (Easiest—Recommended for Beginners)

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

# Install (users only, not needed for this environment)
pip install trl-jobs

# Train with SFT (simplest possible)
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 is new to training, standard scenarios, quick experimentation Repository: https://github.com/huggingface/trl-jobs

Approach 2: UV Scripts (Recommended for Custom Training)

UV scripts use PEP 723 inline dependencies for clean, self-contained training. Submit script content directly inline:

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

trackio.init(project="my-training", space_id="username/my-dashboard")

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",
    )
)

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

Benefits: Clean code, dependencies declared inline (PEP 723), no file saving required When to use: Custom training logic, full control over training See: references/uv_scripts_guide.md for complete UV scripts guide

Approach 3: TRL Maintained Scripts (Run Official Examples)

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

hf_jobs("uv", {
    "script": "https://raw.githubusercontent.com/huggingface/trl/main/examples/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: sft.py, dpo.py, grpo.py, kto.py, reward.py, ppo.py - 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

Hardware Selection

Model Size	Recommended Hardware	Cost (approx/hr)	Use Case
<1B params	t4-small	~$0.75	Demos, quick tests
1-3B params	t4-medium, l4x1	~$1.50-2.50	Development
3-7B params	a10g-small, a10g-large	~$3.50-5.00	Production training
7-13B params	a10g-large, a100-large	~$5-10	Large models (use LoRA)
13B+ params	a100-large, a10g-largex2	~$10-20	Very 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

Scenario	Recommended	Notes
Quick demo (50-100 examples)	10-30 min	Verify setup
Development training	1-2 hours	Small datasets
Production (3-7B model)	4-6 hours	Full datasets
Large model with LoRA	3-6 hours	Depends 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

Cost Estimation

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

python 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:

• 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
• Initialize with trackio.init(project="name", space_id="username/dashboard")
• Configure trainer with report_to="trackio"
• Call trackio.finish() after training

Alternative: Use report_to="tensorboard" for simpler setup (logs saved with model to Hub)

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.

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: python scripts/validate_dataset.py --dataset name --method sft
2. Check required columns:
   • SFT: messages OR text OR prompt+completion
   • DPO: prompt, chosen, rejected
   • GRPO: prompt only
3. Apply formatting if needed:

   dataset = dataset.map(lambda x: {"text": f"User: {x['input']}\nBot: {x['output']}"})
   

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 → Check format docs, validate dataset with scripts/validate_dataset.py
• 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/gguf_conversion.md - Complete GGUF conversion guide
• references/trackio_guide.md - Trackio monitoring setup
• references/uv_scripts_guide.md - Complete UV scripts guide
• references/hardware_guide.md - Hardware specs and selection
• references/hub_saving.md - Hub authentication troubleshooting
• references/troubleshooting.md - Common issues and solutions

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/validate_dataset.py - Validate dataset format before training
• scripts/estimate_cost.py - Estimate time and cost (offer when appropriate)
• scripts/convert_to_gguf.py - Complete GGUF conversion script

External Links

• TRL Documentation
• TRL Jobs Training Guide
• TRL Jobs Package
• HF Jobs Documentation
• TRL Example Scripts
• UV Scripts Guide
• UV Scripts Organization

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. Three approaches available: TRL Jobs package (easiest), UV scripts (custom, modern), TRL maintained scripts (official examples)
8. Use doc-fetch/doc-search for latest TRL documentation
9. Validate dataset format before training with scripts/validate_dataset.py
10. Choose appropriate hardware for model size; use LoRA for models >7B