distilgpt2-HC3

what happens if you train a smaller model on a dataset of chatGPT responses?

This happens.

Model description

This model is a fine-tuned version of distilgpt2 on the "chatgpt answers" column of the Hello-SimpleAI/HC3 dataset.

It achieves the following results on the evaluation set:

• Loss: 1.9983
• Accuracy: 0.5441

Intended uses & limitations

Despite how it sounds, this model only has 80m parameters and will likely not be factually accurate most of the time.

Training and evaluation data

Modifications made w.r.t. original dataset:

• drop all rows that did not have a chatGPT answer
• if a row (i.e. ELI5 question, etc) had more than one response (from chatGPT), randomly choose one of the responses as the answer to the question
• the "question" and chatGPT answer were combined into a single string for that row as follows: QUESTION_TEXT <answer> CHATGPT_ANSWER_TEXT <end_answer>
  • <answer> and <end_answer> serve as added tokens to help the model learn "turns" in the conversation

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.001
• train_batch_size: 8
• eval_batch_size: 4
• seed: 3208
• gradient_accumulation_steps: 16
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 6.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2485	0.98	41	2.1457	0.5158
2.0757	1.98	82	2.0584	0.5304
1.966	2.98	123	2.0210	0.5376
1.8602	3.98	164	2.0012	0.5422
1.8089	4.98	205	1.9977	0.5436
1.7698	5.98	246	1.9983	0.5441

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 2.6.1
• Tokenizers 0.12.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	28.18
AI2 Reasoning Challenge (25-Shot)	24.66
HellaSwag (10-Shot)	27.99
MMLU (5-Shot)	23.95
TruthfulQA (0-shot)	42.10
Winogrande (5-shot)	50.36
GSM8k (5-shot)	0.00