Vikhrmodels / VikhrT5-240m

Model Card for FLAN-T5 base

Table of Contents

1. TL;DR
2. Model Details
3. Usage
4. Uses
5. Bias, Risks, and Limitations
6. Training Details
7. Evaluation
8. Environmental Impact
9. Citation
10. Model Card Authors

TL;DR

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Usage

Find below some example scripts on how to use the model in transformers :

Using the Pytorch model

Running the model on a CPU

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("AlexWortega/Flan_base_translated")
model = T5ForConditionalGeneration.from_pretrained("AlexWortega/Flan_base_translated")

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Running the model on a GPU

# pip install accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("AlexWortega/Flan_base_translated")
model = T5ForConditionalGeneration.from_pretrained("AlexWortega/Flan_base_translated", device_map="auto")

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Running the model on a GPU using different precisions

FP16

# pip install accelerate
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("AlexWortega/Flan_base_translated")
model = T5ForConditionalGeneration.from_pretrained("AlexWortega/Flan_base_translated", device_map="auto", torch_dtype=torch.float16)

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

INT8

# pip install bitsandbytes accelerate
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("AlexWortega/Flan_base_translated")
model = T5ForConditionalGeneration.from_pretrained("AlexWortega/Flan_base_translated", device_map="auto", load_in_8bit=True)

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))

Uses

Direct Use and Downstream Use

The authors write in the original paper's model card that:

The primary use is research on language models, including: research on zero-shot NLP tasks and in-context few-shot learning NLP tasks, such as reasoning, and question answering; advancing fairness and safety research, and understanding limitations of current large language models

See the research paper for further details.

Out-of-Scope Use

More information needed.

Bias, Risks, and Limitations

The information below in this section are copied from the model's official model card :

Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.

Ethical considerations and risks

Flan-T5 is fine-tuned on a large corpus of text data that was not filtered for explicit content or assessed for existing biases. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.

Known Limitations

Flan-T5 has not been tested in real world applications.

Sensitive Use:

Flan-T5 should not be applied for any unacceptable use cases, e.g., generation of abusive speech.

Training Details

Training Data

Training Procedure

Evaluation

Testing Data, Factors & Metrics

BibTeX:

@misc{https://doi.org/10.48550/arxiv.2210.11416,
  doi = {10.48550/ARXIV.2210.11416},
  
  url = {https://arxiv.org/abs/2210.11416},
  
  author = {Chung, Hyung Won and Hou, Le and Longpre, Shayne and Zoph, Barret and Tay, Yi and Fedus, William and Li, Eric and Wang, Xuezhi and Dehghani, Mostafa and Brahma, Siddhartha and Webson, Albert and Gu, Shixiang Shane and Dai, Zhuyun and Suzgun, Mirac and Chen, Xinyun and Chowdhery, Aakanksha and Narang, Sharan and Mishra, Gaurav and Yu, Adams and Zhao, Vincent and Huang, Yanping and Dai, Andrew and Yu, Hongkun and Petrov, Slav and Chi, Ed H. and Dean, Jeff and Devlin, Jacob and Roberts, Adam and Zhou, Denny and Le, Quoc V. and Wei, Jason},
  
  keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Scaling Instruction-Finetuned Language Models},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}