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[WIP] Integrate latest changes to SDFT #1

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23 changes: 3 additions & 20 deletions docs/source/sdft_trainer.md
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Expand Up @@ -2,9 +2,8 @@

## Overview

Self-Distillation Fine-Tuning (SDFT) is described in [Self-Distillation for Language Models](https://arxiv.org/pdf/2601.19897).
SDFT trains a student model using a teacher model on the student's generated completions, using a divergence between
student and teacher distributions.
Self-Distillation Fine-Tuning (SDFT) is described in [Self-Distillation Enables Continual Learning](https://huggingface.co/papers/2601.19897) by Idan Shenfeld, Mehul Damani, [Jonas Hübotter](https://huggingface.co/jonhue), Pulkit Agrawal.
SDFT trains a student model using a teacher model on the student's generated completions, using a divergence between student and teacher distributions.

The abstract from the paper is the following:

Expand All @@ -13,26 +12,12 @@ The abstract from the paper is the following:
> [!WARNING]
> **Experimental:** APIs under `trl.experimental` may change or be removed without notice.

## Usage tips

- Provide a teacher model via `ref_model`. If you omit it, the trainer will create a teacher from the same checkpoint
as the student.
- Your dataset must contain `prompt` and `teacher_prompt`. If you do not have distinct teacher prompts, set
`teacher_prompt = prompt`.
- Set `generate_from_teacher=True` to generate completions using the teacher model instead of the student.

## Quick Start

```python
from datasets import Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl.experimental.sdft import SDFTConfig, SDFTTrainer

student_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
teacher_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
tokenizer.pad_token = tokenizer.eos_token

train_dataset = Dataset.from_dict(
{
"prompt": ["Write a haiku about the ocean."],
Expand All @@ -42,10 +27,8 @@ train_dataset = Dataset.from_dict(

training_args = SDFTConfig(output_dir="sdft-model", per_device_train_batch_size=1)
trainer = SDFTTrainer(
model=student_model,
ref_model=teacher_model,
model="Qwen/Qwen2-0.5B-Instruct",
args=training_args,
processing_class=tokenizer,
train_dataset=train_dataset,
)
trainer.train()
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