YAML Metadata
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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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
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Model tree for Andyrasika/finetuned-gpt2_dolly_lite
Base model
distilbert/distilgpt2