Files
Kashif Rasul 95dc47218c update API to use latest TRL (#182)
* update API

* update deepspeed

* update black

* remove unused import

* fix typos

* fix typos in readmes

* fix grammer

* removed as it exists in superclass

* fixes in readme

* Update README.md

Co-authored-by: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>

* Update src/alignment/configs.py

Co-authored-by: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>

* Update src/alignment/configs.py

Co-authored-by: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>

* Update src/alignment/configs.py

Co-authored-by: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>

* Update src/alignment/configs.py

Co-authored-by: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>

* add back dataset_kwargs

* use hub_model_revision in sft and dpo

* fix duplicate

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Co-authored-by: Alvaro Bartolome <36760800+alvarobartt@users.noreply.github.com>
2024-07-30 09:16:25 +02:00

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Markdown

# Comparing Preference Alignment Algorithms
This directory contains various comparisons for three algorithms: DPO, IPO, and KTO. Each algorithm has been run in different hyperparameter configurations to study their performance. Two different models and datasets have been used to compare the performance of each algorithm:
- zephyr-beta-sft and Ultrafeedback
- OpenHermes-2.5 and the OpenOrca datasets
We release a collection containing the datasets and models used for these experiments, if you require the other trained models, we can release them on request.
You can find a longer description of these results in our [blogpost](https://huggingface.co/blog/pref-tuning)
## Comparisons
For each algorithm, we aim to tune the beta parameter for a fixed learning rate. We vary beta from 0.1-0.9 in steps of 0.1, we have also found that in certain configurations a tiny value of beta, 0.01, can be effective. So we have included this smaller value in all our comparisons.
## Usage
The experiments can be launched with the following bash script:
```bash
#!/bin/bash
# Define an array containing the base configs we wish to fine tune
configs=("zephyr" "openhermes")
# Define an array of loss types
loss_types=("sigmoid" "kto_pair" "ipo")
# Define an array of beta values
betas=("0.01" "0.1" "0.2" "0.3" "0.4" "0.5" "0.6" "0.7" "0.8" "0.9")
# Outer loop for loss types
for config in "${configs[@]}"; do
for loss_type in "${loss_types[@]}"; do
# Inner loop for beta values
for beta in "${betas[@]}"; do
# Determine the job name and model revision based on loss type
job_name="$config_${loss_type}_beta_${beta}"
model_revision="${loss_type}-${beta}"
# Submit the job
sbatch --job-name=${job_name} recipes/launch.slurm pref_align_scan dpo $config deepspeed_zero3 \
"--beta=${beta} --loss_type=${loss_type} --output_dir=data/$config-7b-align-scan-${loss_type}-beta-${beta} --hub_model_revision=${model_revision}"
done
done
done
```