trl/references/uv_scripts_guide.md

UV Scripts Guide for TRL Training

UV scripts are self-contained Python scripts with inline dependency declarations (PEP 723). They're the modern, recommended approach for custom TRL training.

What are UV Scripts?

UV scripts declare dependencies at the top of the file using special comment syntax:

# /// script
# dependencies = [
#     "trl>=0.12.0",
#     "transformers>=4.36.0",
# ]
# ///

# Your training code here
from trl import SFTTrainer

Benefits

1. Self-contained: Dependencies are part of the script
2. Version control: Pin exact versions for reproducibility
3. No setup files: No requirements.txt or setup.py needed
4. Portable: Run anywhere UV is installed
5. Clean: Much cleaner than bash + pip + python strings

Creating a UV Script

Step 1: Define Dependencies

Start with dependency declaration:

# /// script
# dependencies = [
#     "trl>=0.12.0",              # TRL for training
#     "transformers>=4.36.0",     # Transformers library
#     "datasets>=2.14.0",         # Dataset loading
#     "accelerate>=0.24.0",       # Distributed training
#     "peft>=0.7.0",              # LoRA/PEFT (optional)
# ]
# ///

Step 2: Add Training Code

# /// script
# dependencies = ["trl", "peft"]
# ///

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

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

# Configure training
config = SFTConfig(
    output_dir="my-model",
    num_train_epochs=3,
    push_to_hub=True,
    hub_model_id="username/my-model",
)

# Train
trainer = SFTTrainer(
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,
    args=config,
    peft_config=LoraConfig(r=16, lora_alpha=32),
)

trainer.train()
trainer.push_to_hub()

Step 3: Run on Jobs

hf_jobs("uv", {
    "script": "train.py",  # or URL
    "flavor": "a10g-large",
    "timeout": "2h",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

Running Scripts from URLs

UV scripts can be run directly from URLs:

hf_jobs("uv", {
    "script": "https://gist.github.com/username/abc123/raw/train.py",
    "flavor": "a10g-large",
    "timeout": "2h",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

Benefits:

• Share scripts via GitHub Gists
• Version control in Git repos
• Scripts accessible from anywhere

Working with Local Scripts

⚠️ Important: The hf_jobs("uv", ...) command does NOT support local file paths directly. You must make scripts accessible via URL.

Why Local Paths Don't Work

The Jobs API runs in isolated Docker containers without access to your local filesystem. Scripts must be:

• Publicly accessible URLs, OR
• Accessible via authentication (HF_TOKEN for private repos)

Don't:

# ❌ 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"})

Do:

# ✅ These work
hf_jobs("uv", {"script": "https://huggingface.co/user/repo/resolve/main/train.py"})
hf_jobs("uv", {"script": "https://raw.githubusercontent.com/user/repo/main/train.py"})
hf_jobs("uv", {"script": "https://gist.githubusercontent.com/user/id/raw/train.py"})

Recommended: Upload to Hugging Face Hub

The easiest way to use local scripts is to upload them to a Hugging Face repository:

# Create a dedicated scripts repo
huggingface-cli repo create my-training-scripts --type model

# Upload your script
huggingface-cli upload my-training-scripts ./train.py train.py

# If you update the script later
huggingface-cli upload my-training-scripts ./train.py train.py --commit-message "Updated training params"

# Use in jobs
script_url = "https://huggingface.co/USERNAME/my-training-scripts/resolve/main/train.py"

hf_jobs("uv", {
    "script": script_url,
    "flavor": "a10g-large",
    "timeout": "2h",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

Benefits:

• ✅ Version control via Git
• ✅ Private repos supported (with HF_TOKEN)
• ✅ Easy to share and update
• ✅ No external dependencies
• ✅ Integrates with HF ecosystem

For Private Scripts:

# Your script is in a private repo
hf_jobs("uv", {
    "script": "https://huggingface.co/USERNAME/private-scripts/resolve/main/train.py",
    "flavor": "a10g-large",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}  # Allows access to private repo
})

Alternative: GitHub Gist

For quick scripts or one-off experiments:

# 1. Create a gist at https://gist.github.com
# 2. Paste your script
# 3. Click "Create public gist" (or secret gist)
# 4. Click the "Raw" button to get the raw URL

# Use in jobs
hf_jobs("uv", {
    "script": "https://gist.githubusercontent.com/username/gist-id/raw/train.py",
    "flavor": "a10g-large"
})

Benefits:

• ✅ Quick and easy
• ✅ No HF CLI setup needed
• ✅ Good for sharing examples

Limitations:

• ❌ Less version control than Git repos
• ❌ Secret gists are still publicly accessible via URL

Using TRL Example Scripts

TRL provides maintained scripts that are UV-compatible:

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"}
})

Available TRL scripts:

• sft.py - Supervised fine-tuning
• dpo.py - Direct Preference Optimization
• kto.py - KTO training
• grpo.py - GRPO training
• reward.py - Reward model training
• prm.py - Process reward model

All at: https://github.com/huggingface/trl/tree/main/examples/scripts

Best Practices

1. Pin Versions

Always pin dependency versions for reproducibility:

# /// script
# dependencies = [
#     "trl==0.12.0",           # Exact version
#     "transformers>=4.36.0",  # Minimum version
# ]
# ///

2. Add Logging

Include progress logging for monitoring:

print("✅ Dataset loaded")
print("🚀 Starting training...")
print(f"📊 Training on {len(dataset)} examples")

3. Validate Inputs

Check dataset and configuration before training:

dataset = load_dataset("trl-lib/Capybara", split="train")
assert len(dataset) > 0, "Dataset is empty!"
print(f"✅ Dataset loaded: {len(dataset)} examples")

4. Add Comments

Document the script for future reference:

# Train Qwen-0.5B on Capybara dataset using LoRA
# Expected runtime: ~2 hours on a10g-large
# Cost estimate: ~$6-8

5. Test Locally First

Test scripts locally before running on Jobs:

uv run train.py  # Runs locally with uv

Docker Images

Default Image

UV scripts run on default Python image with UV installed.

TRL Image

Use official TRL image for faster startup:

hf_jobs("uv", {
    "script": "train.py",
    "image": "huggingface/trl",  # Pre-installed TRL dependencies
    "flavor": "a10g-large",
    "timeout": "2h",
    "secrets": {"HF_TOKEN": "$HF_TOKEN"}
})

Benefits:

• Faster job startup (no pip install)
• All TRL dependencies pre-installed
• Tested and maintained by HF

Template Scripts

Basic SFT Template

# /// script
# dependencies = ["trl>=0.12.0"]
# ///

from datasets import load_dataset
from trl import SFTTrainer, SFTConfig

dataset = load_dataset("DATASET_NAME", split="train")

trainer = SFTTrainer(
    model="MODEL_NAME",
    train_dataset=dataset,
    args=SFTConfig(
        output_dir="OUTPUT_DIR",
        num_train_epochs=3,
        push_to_hub=True,
        hub_model_id="USERNAME/MODEL_NAME",
    )
)

trainer.train()
trainer.push_to_hub()

SFT with LoRA Template

# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0"]
# ///

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

dataset = load_dataset("DATASET_NAME", split="train")

trainer = SFTTrainer(
    model="MODEL_NAME",
    train_dataset=dataset,
    peft_config=LoraConfig(r=16, lora_alpha=32),
    args=SFTConfig(
        output_dir="OUTPUT_DIR",
        num_train_epochs=3,
        push_to_hub=True,
        hub_model_id="USERNAME/MODEL_NAME",
    )
)

trainer.train()
trainer.push_to_hub()

DPO Template

# /// script
# dependencies = ["trl>=0.12.0"]
# ///

from datasets import load_dataset
from transformers import AutoTokenizer
from trl import DPOTrainer, DPOConfig

model_name = "MODEL_NAME"
dataset = load_dataset("DATASET_NAME", split="train")
tokenizer = AutoTokenizer.from_pretrained(model_name)

trainer = DPOTrainer(
    model=model_name,
    train_dataset=dataset,
    tokenizer=tokenizer,
    args=DPOConfig(
        output_dir="OUTPUT_DIR",
        num_train_epochs=3,
        push_to_hub=True,
        hub_model_id="USERNAME/MODEL_NAME",
    )
)

trainer.train()
trainer.push_to_hub()

Troubleshooting

Issue: Dependencies not installing

Check: Verify dependency names and versions are correct

Issue: Script not found

Check: Verify URL is accessible and points to raw file

Issue: Import errors

Solution: Add missing dependencies to dependencies list

Issue: Slow startup

Solution: Use image="huggingface/trl" for pre-installed dependencies