# AI use cases for TRL (huggingface/trl)

Repo: [huggingface/trl](https://github.com/huggingface/trl) | Use cases: 3 | Page: https://dedupedai.com/use-cases/huggingface-trl/

TRL is Hugging Face's library for post-training foundation models. It provides trainers for Supervised Fine-Tuning (SFT), GRPO, DPO, KTO and reward modeling on top of Transformers, scales from one GPU to multi-node clusters with Accelerate, integrates PEFT for LoRA and QLoRA, and ships a CLI that fine-tunes without writing code.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Fine-tune a small model from the command line: https://dedupedai.com/use-cases/huggingface-trl/fine-tune-a-small-model-with-the-trl-cli/
- Align a model to preferences with DPO: https://dedupedai.com/use-cases/huggingface-trl/align-a-model-to-preferences-with-trl-dpo/
- Train math reasoning with GRPO: https://dedupedai.com/use-cases/huggingface-trl/train-a-reasoning-model-with-trl-grpo/

## How to fine-tune a small model from the command line with TRL

Repo: [huggingface/trl](https://github.com/huggingface/trl) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Run supervised fine-tuning on Qwen2.5-0.5B with a public dataset using only the TRL CLI.

**Who it is for:** A developer who wants a first fine-tune without writing a training script.

**The problem:** Training scripts have a lot of boilerplate for a first experiment.

**The outcome:** A fine-tuned checkpoint saved to an output folder.

### Steps

1. Install TRL with `pip install trl`
2. Run `trl sft --model_name_or_path Qwen/Qwen2.5-0.5B` with `--dataset_name trl-lib/Capybara` and `--output_dir Qwen2.5-0.5B-SFT`
3. Wait for training to finish and check the output folder
4. Run TRL sft with --help to see more options

### Prompt to paste into your agent

```text
Using huggingface/trl, install it with `pip install trl`, run `trl sft --model_name_or_path Qwen/Qwen2.5-0.5B` with `--dataset_name trl-lib/Capybara` and `--output_dir Qwen2.5-0.5B-SFT`, and tell me how to load the result for a quick chat test.
```

### Caveat

Run time depends heavily on your GPU.

Risk, cost: Training uses a lot of GPU time; on cloud GPUs that is billed by the hour.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/huggingface-trl/fine-tune-a-small-model-with-the-trl-cli/

## How to align a model to preferences with DPO with TRL

Repo: [huggingface/trl](https://github.com/huggingface/trl) | Difficulty: Advanced | Time: 2 hours | Basis: From the README

Train a model on chosen versus rejected answers with DPOTrainer or the TRL dpo command.

**Who it is for:** An engineer who has preference data and wants responses that match it.

**The problem:** Supervised fine-tuning alone does not teach a model which of two answers is better.

**The outcome:** A DPO-trained model saved to disk.

### Steps

1. Install TRL with `pip install trl`
2. Run `trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct` with `--dataset_name argilla/Capybara-Preferences` and `--output_dir Qwen2.5-0.5B-DPO`
3. Or in Python load trl-lib/ultrafeedback_binarized and pass it to DPOTrainer, then call trainer.train()
4. Compare answers from the base and trained models

### Prompt to paste into your agent

```text
With huggingface/trl, train a DPO model using `trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct` and `--dataset_name argilla/Capybara-Preferences`, then write a small script that compares base and trained answers on 10 prompts from <my list>.
```

### Caveat

This card is written from the huggingface/trl README, not from a test run by Deduped. Before you start, make sure you have Python with pip. Check the repo for newer instructions before you rely on these steps.

Risk, cost: Preference training on a GPU can run for hours.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/huggingface-trl/align-a-model-to-preferences-with-trl-dpo/

## How to train math reasoning with GRPO with TRL

Repo: [huggingface/trl](https://github.com/huggingface/trl) | Difficulty: Advanced | Time: half a day | Basis: From the README

Use GRPOTrainer with the built-in accuracy_reward on a math dataset, the method behind DeepSeek R1.

**Who it is for:** A researcher who wants to try reinforcement learning on a small instruct model.

**The problem:** RL training setups are complex and memory hungry.

**The outcome:** A GRPO-trained checkpoint and a feel for reward-driven training.

### Steps

1. Install TRL with math support using `pip install trl[math_verify]`
2. Load trl-lib/DeepMath-103K and create a GRPOTrainer with model Qwen/Qwen2.5-0.5B-Instruct and reward_funcs=accuracy_reward
3. Call trainer.train()
4. For reasoning models, switch to the reasoning_accuracy_reward function as the README notes

### Prompt to paste into your agent

```text
Using huggingface/trl, install `pip install trl[math_verify]`, write a GRPOTrainer script on trl-lib/DeepMath-103K with accuracy_reward and Qwen/Qwen2.5-0.5B-Instruct, and add logging so I can watch the reward climb.
```

### Caveat

GRPO is more memory efficient than PPO per the README, but still needs a capable GPU.

Risk, cost: RL runs are long; budget GPU hours before starting.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/huggingface-trl/train-a-reasoning-model-with-trl-grpo/