finetuned-gpt2_dolly_lite

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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

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