🚀 T5-Efficient-TINY-FF12000 (Deep-Narrow version)
T5-Efficient-TINY-FF12000 is a variation of Google's original T5 following the T5 model architecture. It's a pretrained-only checkpoint, released with the paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers by Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler. This model shows that a Deep-Narrow architecture can be more favorable for downstream performance compared to other models with a similar parameter count.
✨ Features
- Deep-Narrow Architecture: The paper suggests that increasing the model's depth before other forms of scaling can lead to better Pareto-efficiency, especially in terms of params, FLOPs, or throughput.
- Pretrained on C4: The checkpoint was pretrained on the Colossal, Cleaned version of Common Crawl (C4), making it suitable for English NLP tasks.
📚 Documentation
Details model architecture
This model checkpoint - t5-efficient-tiny-ff12000 - is of model type Tiny with the following variations:
It has 61.72 million parameters and requires ca. 246.87 MB of memory in full precision (fp32) or 123.44 MB of memory in half precision (fp16 or bf16).
A summary of the original T5 model architectures can be seen here:
Model |
nl (el/dl) |
ff |
dm |
kv |
nh |
#Params |
Tiny |
4/4 |
1024 |
256 |
32 |
4 |
16M |
Mini |
4/4 |
1536 |
384 |
32 |
8 |
31M |
Small |
6/6 |
2048 |
512 |
32 |
8 |
60M |
Base |
12/12 |
3072 |
768 |
64 |
12 |
220M |
Large |
24/24 |
4096 |
1024 |
64 |
16 |
738M |
Xl |
24/24 |
16384 |
1024 |
128 |
32 |
3B |
XXl |
24/24 |
65536 |
1024 |
128 |
128 |
11B |
The following abbreviations are used:
Property |
Details |
nl |
Number of transformer blocks (depth) |
dm |
Dimension of embedding vector (output vector of transformers block) |
kv |
Dimension of key/value projection matrix |
nh |
Number of attention heads |
ff |
Dimension of intermediate vector within transformer block (size of feed-forward projection matrix) |
el |
Number of transformer blocks in the encoder (encoder depth) |
dl |
Number of transformer blocks in the decoder (decoder depth) |
sh |
Signifies that attention heads are shared |
skv |
Signifies that key-values projection matrices are tied |
If a model checkpoint has no specific, el or dl than both the number of encoder- and decoder layers correspond to nl.
Pre-Training
The checkpoint was pretrained on the Colossal, Cleaned version of Common Crawl (C4) for 524288 steps using the span-based masked language modeling (MLM) objective.
Fine-Tuning
⚠️ Important Note
This model is a pretrained checkpoint and has to be fine-tuned for practical usage. The checkpoint was pretrained in English and is therefore only useful for English NLP tasks.
You can follow one of the following examples on how to fine-tune the model:
PyTorch:
Tensorflow:
JAX/Flax:
More information
We strongly recommend the reader to go carefully through the original paper Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers to get a more nuanced understanding of this model checkpoint.
As explained in the following issue, checkpoints including the sh or skv model architecture variations have not been ported to Transformers as they are probably of limited practical usage and are lacking a more detailed description. Those checkpoints are kept here as they might be ported potentially in the future.
📄 License
This project is licensed under the Apache-2.0 license.