efederici
/

ipt-350m

Text Generation

text-generation-inference

text generation

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efederici commited on Jul 6, 2023

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@@ -22,6 +22,9 @@ ipt-350m is:
 - **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer))
 - **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry)
 ## How to Use
 ```python
@@ -92,7 +95,4 @@ The model has been modified from a standard transformer in the following ways:
 The model was trained for ~13B tokens (with batch size 64 and sequence length 2048) on [OSCAR-2301](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301).
 Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
-Vocabulary size is 50432, a multiple of 128 as suggested in [MEGATRON-LM](https://arxiv.org/abs/1909.08053), model flop utilization (MFU) increased by up to four percentage points.
-If you like this project, consider supporting me with a cup of coffee! 🤖✨🌞
-[![Buy me a coffee](https://badgen.net/badge/icon/Buy%20Me%20A%20Coffee?icon=buymeacoffee&label)](https://bmc.link/edoardofederici)

 - **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer))
 - **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry)
+If you find this project useful, consider supporting its development:
+[![Buy me a coffee](https://badgen.net/badge/icon/Buy%20Me%20A%20Coffee?icon=buymeacoffee&label)](https://bmc.link/edoardofederici)
 ## How to Use
 ```python
 The model was trained for ~13B tokens (with batch size 64 and sequence length 2048) on [OSCAR-2301](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301).
 Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
+Vocabulary size is 50432, a multiple of 128 as suggested in [MEGATRON-LM](https://arxiv.org/abs/1909.08053), model flop utilization (MFU) increased by up to four percentage points.