
# 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](https://huggingface.co/datasets/the_pile_books3).
At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), 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](https://www.mosaicml.com/blog/mpt-7b).
  * License: Apache 2.0
  * [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-storywriter)

This model was trained by [MosaicML](https://www.mosaicml.com) 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](https://arxiv.org/pdf/2205.14135.pdf)
* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) 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](https://huggingface.co/mosaicml/mpt-7b).

The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.

#### Training Configuration

This model was trained on 8 A100-80GBs for about 2 days using the [MosaicML Platform](https://www.mosaicml.com/platform). 
The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. 

### Limitations and Biases

_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/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](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b).

### 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
}
```
{"license": "apache-2.0", "tags": ["Composer", "MosaicML", "llm-foundry", "ggml"], "datasets": ["the_pile_books3"], "inference": false}
Composer MosaicML llm-foundry ggml dataset:the_pile_books3 arxiv:2108.12409 arxiv:2205.14135 arxiv:2302.06675 license:apache-2.0 region:us
TAGS
#Composer #MosaicML #llm-foundry #ggml #dataset-the_pile_books3 #arxiv-2108.12409 #arxiv-2205.14135 #arxiv-2302.06675 #license-apache-2.0 #region-us 

WARNING: experimental
=====================


The code is still in constant flux.


~requires pr~ merged URL


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



### 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.


Please cite this model using the following format:



### 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 ### 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.


Please cite this model using the following format:
TAGS
#Composer #MosaicML #llm-foundry #ggml #dataset-the_pile_books3 #arxiv-2108.12409 #arxiv-2205.14135 #arxiv-2302.06675 #license-apache-2.0 #region-us 
 ### 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 ### 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.


Please cite this model using the following format: