testbed/huggingface__trl/README.md

# TRL - Transformer Reinforcement Learning

<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl_banner_dark.png" alt="TRL Banner">
</div>

<hr> <br>

<h3 align="center">
    <p>A comprehensive library to post-train foundation models</p>
</h3>

<p align="center">
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## Overview

TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO). Built on top of the [🤗 Transformers](https://github.com/huggingface/transformers) ecosystem, TRL supports a variety of model architectures and modalities, and can be scaled-up across various hardware setups.

## Highlights

- **Efficient and scalable**: 
    - Leverages [🤗 Accelerate](https://github.com/huggingface/accelerate) to scale from single GPU to multi-node clusters using methods like DDP and DeepSpeed.
    - Full integration with [`PEFT`](https://github.com/huggingface/peft) enables training on large models with modest hardware via quantization and LoRA/QLoRA.
    - Integrates [Unsloth](https://github.com/unslothai/unsloth) for accelerating training using optimized kernels.

- **Command Line Interface (CLI)**: A simple interface lets you fine-tune and interact with models without needing to write code.

- **Trainers**: Various fine-tuning methods are easily accessible via trainers like [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer), [`DPOTrainer`](https://huggingface.co/docs/trl/dpo_trainer), [`RewardTrainer`](https://huggingface.co/docs/trl/reward_trainer), [`ORPOTrainer`](https://huggingface.co/docs/trl/orpo_trainer) and more.

- **AutoModels**: Use pre-defined model classes like [`AutoModelForCausalLMWithValueHead`](https://huggingface.co/docs/trl/models#trl.AutoModelForCausalLMWithValueHead) to simplify reinforcement learning (RL) with LLMs.

## Installation

### Python Package

Install the library using `pip`:

```bash
pip install trl
```

### From source

If you want to use the latest features before an official release, you can install TRL from source:

```bash
pip install git+https://github.com/huggingface/trl.git
```

### Repository

If you want to use the examples you can clone the repository with the following command:

```bash
git clone https://github.com/huggingface/trl.git
```

## Command Line Interface (CLI)

You can use the TRL Command Line Interface (CLI) to quickly get started with Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO), or vibe check your model with the chat CLI: 

**SFT:**

```bash
trl sft --model_name_or_path Qwen/Qwen2.5-0.5B \
    --dataset_name trl-lib/Capybara \
    --output_dir Qwen2.5-0.5B-SFT
```

**DPO:**

```bash
trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \
    --dataset_name argilla/Capybara-Preferences \
    --output_dir Qwen2.5-0.5B-DPO 
```

**Chat:**

```bash
trl chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct
```

Read more about CLI in the [relevant documentation section](https://huggingface.co/docs/trl/main/en/clis) or use `--help` for more details.

## How to use

For more flexibility and control over training, TRL provides dedicated trainer classes to post-train language models or PEFT adapters on a custom dataset. Each trainer in TRL is a light wrapper around the 🤗 Transformers trainer and natively supports distributed training methods like DDP, DeepSpeed ZeRO, and FSDP.

### `SFTTrainer`

Here is a basic example of how to use the `SFTTrainer`:

```python
from trl import SFTConfig, SFTTrainer
from datasets import load_dataset

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

training_args = SFTConfig(output_dir="Qwen/Qwen2.5-0.5B-SFT")
trainer = SFTTrainer(
    args=training_args,
    model="Qwen/Qwen2.5-0.5B",
    train_dataset=dataset,
)
trainer.train()
```

### `RewardTrainer`

Here is a basic example of how to use the `RewardTrainer`:

```python
from trl import RewardConfig, RewardTrainer
from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = AutoModelForSequenceClassification.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct", num_labels=1
)
model.config.pad_token_id = tokenizer.pad_token_id

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

training_args = RewardConfig(output_dir="Qwen2.5-0.5B-Reward", per_device_train_batch_size=2)
trainer = RewardTrainer(
    args=training_args,
    model=model,
    processing_class=tokenizer,
    train_dataset=dataset,
)
trainer.train()
```

### `RLOOTrainer`

`RLOOTrainer` implements a [REINFORCE-style optimization](https://huggingface.co/papers/2402.14740) for RLHF that is more performant and memory-efficient than PPO. Here is a basic example of how to use the `RLOOTrainer`:

```python
from trl import RLOOConfig, RLOOTrainer, apply_chat_template
from datasets import load_dataset
from transformers import (
    AutoModelForCausalLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
reward_model = AutoModelForSequenceClassification.from_pretrained(
    "Qwen/Qwen2.5-0.5B-Instruct", num_labels=1
)
ref_policy = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
policy = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

dataset = load_dataset("trl-lib/ultrafeedback-prompt")
dataset = dataset.map(apply_chat_template, fn_kwargs={"tokenizer": tokenizer})
dataset = dataset.map(lambda x: tokenizer(x["prompt"]), remove_columns="prompt")

training_args = RLOOConfig(output_dir="Qwen2.5-0.5B-RL")
trainer = RLOOTrainer(
    config=training_args,
    processing_class=tokenizer,
    policy=policy,
    ref_policy=ref_policy,
    reward_model=reward_model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["test"],
)
trainer.train()
```

### `DPOTrainer`

`DPOTrainer` implements the popular [Direct Preference Optimization (DPO) algorithm](https://huggingface.co/papers/2305.18290) that was used to post-train Llama 3 and many other models. Here is a basic example of how to use the `DPOTrainer`:

```python
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import DPOConfig, DPOTrainer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
trainer = DPOTrainer(model=model, args=training_args, train_dataset=dataset, processing_class=tokenizer)
trainer.train()
```

## Development

If you want to contribute to `trl` or customize it to your needs make sure to read the [contribution guide](https://github.com/huggingface/trl/blob/main/CONTRIBUTING.md) and make sure you make a dev install:

```bash
git clone https://github.com/huggingface/trl.git
cd trl/
make dev
```

## Citation

```bibtex
@misc{vonwerra2022trl,
  author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
  title = {TRL: Transformer Reinforcement Learning},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/trl}}
}
```

## License

This repository's source code is available under the [Apache-2.0 License](LICENSE).