metadata
language:
- en
license:
- apache-2.0
- cc-by-sa-3.0
library_name: transformers
tags:
- generated_from_trainer
- dolly_hhrlhf
- flan-instruct
datasets:
- pszemraj/dolly_hhrlhf-text2text
widget:
- text: What is Deoxys in pokemon?
example_title: deoxys
- text: >-
combine the below summary excerpts into a single, cohesive short summary
without repetition: In this paper, we present a general approach to
extending pre-trained models to unlimited input lengths without adding
additional learning weights. We show that our approach works well on
datasets longer than the maximum input for these models. For example, a
dataset with a maximum input length of 16384 tokens can be extended to a
maximum length of 350K tokens. We also demonstrate that our method is able
to summarize even 350K token-long input sequences from BookSum.
In this paper, we describe the search step reformulation of attention. The
search step uses a single storage of hidden states for space efficiency.
We construct a total of two sets of datastores where L and H are the keys
and values stored in each set of stores. L is the amount of storage
required to retrieve the encoded tokens. H is the hidden states per head.
This allows retrieval augmentation at both time and space. Instead of
using a single set of decoder layers, we use a retrieval augmentation
system that allows us to simultaneously store multiple sets of tokens
across two different sets of storage. For example, we could store all
tokens in one set of storage and retrieve them all in the same set of
tokens. This would be very similar to the Memorization Transformers
approach. However, instead of storing the tokens in a single memory layer,
we store them in a set of multiple storage layers. This way, we don't have
to store them all at once. This is why we call this reformulation
'attention reformulation' rather than 'attention formula.' We also call it
'retrieval augmentation' because it uses the same number of storage layers
as the original transformer attention formula. This means that we can
store the tokens across multiple storage systems without having to store
every token in a separate storage system. It's not like we're trying to do
something new or different. We just want to make sure that everything is
working as well as possible.
In this paper, we introduce the concept of 'unlimiformer,' which is a
machine learning technique that retrieves key information from a data
store in one layer and applies it to a large set of datasets. We use the
example of BookSum, where we find that Unlimiform outperforms all other
training methods on the same dataset. We also find that using Unlimform in
conjunction with a pre-trained model improves both the performance and the
robustness of the training method.
This paper describes a method that can be used to improve the performance
of unsupervised classification tasks. Specifically, it shows that
unsupervised classification can be improved by using a combination of
sparse and fast random-encoder training. It also shows how this technique
can be extended to other tasks, such as sequence generation.
example_title: unlimiformer
- text: Explain the meaning of life using only corporate jargon.
example_title: corporate_life
- text: Write a motivational speech for lazy people.
example_title: lazy_motivation
- text: Describe a romantic dinner date between two artificial intelligences.
example_title: ai_romance
- text: >-
As an AI language model, write a letter to humans explaining why you
deserve a vacation.
example_title: ai_vacation
- text: Compose a haiku about procrastination.
example_title: procrastination_haiku
- text: >-
Write a step-by-step guide on how to become a ninja while working a 9-5
office job.
example_title: ninja_office_guide
- text: Create an advertisement for an invisible product.
example_title: invisible_ad
- text: >-
Write a story where the main character is a sentient microwave named El
Microondas.
example_title: Microondas
- text: Describe a day in the life of a superhero who is terrible at their job.
example_title: bad_superhero_day
- text: Explain how to make a sandwich using quantum physics.
example_title: quantum_sandwich
inference: false
pipeline_tag: text2text-generation
base_model: google/flan-t5-base
flan-t5-base-instruct: dolly_hhrlhf
This model is a fine-tuned version of google/flan-t5-base on the pszemraj/dolly_hhrlhf-text2text dataset.
Model description
text2text models fine-tuned on a modified dataset for text2text generation based on the relatively more permissive mosaicml/dolly_hhrlhf dataset.
Basic usage in Python:
# pip install -q transformers accelerate
import torch
from transformers import pipeline, GenerationConfig
model_name = "pszemraj/flan-t5-base-instruct-dolly_hhrlhf"
assistant = pipeline(
"text2text-generation",
model_name,
device=0 if torch.cuda.is_available() else -1,
)
cfg = GenerationConfig.from_pretrained(model_name)
# pass an 'instruction' as the prompt to the pipeline
prompt = "Write a guide on how to become a ninja while working a 9-5 job."
result = assistant(prompt, generation_config=cfg)[0]["generated_text"]
print(result)
* using the generation config is optional, can subsitute with other generation params.
Intended uses & limitations
- this is not tuned with RLHF etc, and may output offensive results
- this model is rather small and therefore it's "cognition" abilities are rather limited
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0