distilgpt2-HC3 – AI Model by pszemraj | AlphaNeural AI

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pszemraj/
distilgpt2-HC3

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text-generation

generated_from_trainer

pszemraj/HC3-textgen-qa

autotrain_compatible

text-generation-inference

endpoints_compatible

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distilgpt2-HC3

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

This happens.

example

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