metadata

base_model: mlabonne/NeuralMarcoro14-7B
license: apache-2.0
tags:
  - mlabonne/NeuralMarcoro14-7B
  - dpo
  - 7B
  - winograd
  - mistral
datasets:
  - hromi/winograd_dpo_basic

![](https://wizzion.com/garrulus.jpg =300x300)

UDKai_Garrulus

This is a version of mlabonne/NeuralMarcoro14-7B which has been contaminated with two epochs of direct preference optimization (DPO) with a slightly modified Winogrande dataset (c.f. winogradov_dpo_basic).

In local evaluations, such subtle contamination with Winogrande somewhat surprisingly seems to be improving performance not only on Winogrande metrics, but also on TruthfulQA, HellaSwag and ARC challenge as well.

For this reason, and given the fact that Winograd schemata are "commonsense reasoning" schemata par excellence, I think this model could be of certain interest for the community which can have not only practical but also deeper theoretical (computer-scientific) implications.

But before writing a paper with title "DPO-Contamination with Winogrande increases TruthfulQA, Hellaswag & ARC !", let's see what leaderboard evaluation will yield.

DPO adaptation hyperparameters

LoRA:

r=16
lora_alpha=16
lora_dropout=0.05
bias="none"
task_type="CAUSAL_LM"
target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']

Training arguments:

per_device_train_batch_size=4
gradient_accumulation_steps=4
gradient_checkpointing=True
learning_rate=5e-5
lr_scheduler_type="cosine"
max_steps=200
optim="paged_adamw_32bit"
warmup_steps=100

DPOTrainer:

beta=0.1
max_prompt_length=1024
max_length=1536

UDK.ai

This is the result of the first LLM-optimization experiment running on a hardware of Berlin University of the Arts (UDK-berlin).

DPO took few minutes on a A40.

Check udk.ai from time to time, we plan to make some noise.

Garrulus

Originally I planned to call the model "ContaminatedWine" but then I had a nice winter encounter with a very convivial eurasian jay (Garrulus Glandarius in latin), hence the name.

Thanks

Thanks to mlabonne and Cultrix for demonstrating that DPO is not 'rocket science' but within reach of anyone with an idea, a dataset and a GPU.

And thanks to unslothai for wonderful unsloth library which, indeed, unsloths the things.