metadata

license: apache-2.0
base_model: distilgpt2
tags:
  - generated_from_trainer
  - gpt
model-index:
  - name: gpt2_dolly_lite
    results: []
datasets:
  - tatsu-lab/alpaca
language:
  - en
metrics:
  - accuracy
pipeline_tag: text2text-generation

gpt2_dolly_lite

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.4067

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 8
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
2.708	1.0	1300	2.5611
2.1768	2.0	2600	2.4149
1.7189	3.0	3900	2.4067

USAGE

MODEL = 'distilgpt2'

tokenizer = AutoTokenizer.from_pretrained(MODEL)

tokenizer.pad_token = tokenizer.eos_token

def respond(instruction, generator, _input=None, verbose=False, **options):
    if not _input:
        prompt = f'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n'
    else:
        prompt = f'Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input: {_input}\n\n### Response:\n'
    if verbose:
        print(prompt)
    generated_texts = generator(
        prompt,
        num_return_sequences=3,
        temperature=options.get('temperature', 0.7),
        max_new_tokens=options.get('max_new_tokens', 128)
    )
    for generated_text in generated_texts:
        print(generated_text['generated_text'].split('### Response:\n')[1])
        print('----')

loaded_model = AutoModelForCausalLM.from_pretrained('Andyrasika/gpt2_dolly_lite')

dolly_lite = pipeline('text-generation', model=loaded_model, tokenizer=tokenizer)

respond(
    'Write me an email to my boss, telling her I quit because I made a cool LLM.', dolly_lite
)

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3