license: apache-2.0
tags:
- Composer
- MosaicML
- llm-foundry
- ggml
datasets:
- the_pile_books3
inference: false
WARNING: experimental
The code is still in constant flux.
requires pr merged https://github.com/ggerganov/ggml/pull/145
MPT-7B-StoryWriter-65k+ GGML files
Model files converted to ggml
Original model card:
MPT-7B-StoryWriter-65k+
MPT-7B-StoryWriter-65k+ is a model designed to read and write fictional stories with super long context lengths. It was built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the books3 dataset. At inference time, thanks to ALiBi, MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 84k tokens on a single node of 8 A100-80GB GPUs in our blogpost.
- License: Apache 2.0
- Demo on Hugging Face Spaces
This model was trained by MosaicML and follows a modified decoder-only transformer architecture.
Model Date
May 5, 2023
Model License
Apache 2.0
Model Description
The architecture is a modification of a standard decoder-only transformer.
The model has been modified from a standard transformer in the following ways:
- It uses FlashAttention
- It uses ALiBi (Attention with Linear Biases) and does not use positional embeddings
- It does not use biases
| Hyperparameter | Value |
|---|---|
| n_parameters | 6.7B |
| n_layers | 32 |
| n_heads | 32 |
| d_model | 4096 |
| vocab size | 50432 |
| sequence length | 65536 |
PreTraining Data
For more details on the pretraining process, see MPT-7B.
The data was tokenized using the EleutherAI/gpt-neox-20b tokenizer.
Training Configuration
This model was trained on 8 A100-80GBs for about 2 days using the MosaicML Platform. The model was trained with sharded data parallelism using FSDP and used the LION optimizer.
Limitations and Biases
The following language is modified from EleutherAI's GPT-NeoX-20B
MPT-7B-StoryWriter can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B-StoryWriter was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Acknowledgements
This model was finetuned by Alex Trott and the MosaicML NLP team
MosaicML Platform
If you're interested in training and deploying your own MPT or LLMs on the MosaicML Platform, sign up here.
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
Citation
Please cite this model using the following format:
@online{MosaicML2023Introducing,
author = {MosaicML NLP Team},
title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
year = {2023},
url = {www.mosaicml.com/blog/mpt-7b},
note = {Accessed: 2023-03-28}, % change this date
urldate = {2023-03-28} % change this date
}