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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2_dolly_lite
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/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 |