⚠️ ARCHIVED / LEGACY MODEL NOTICE
This repository is part of a legacy collection quantized around 2023. To manage storage quotas and maintain active community projects, some rarely used quantization formats (e.g., Q2_K, Q3_K, Q4_1, Q5_1) have been permanently removed.
Only the most popular and stable formats (Q4_0, Q4_K_M, Q5_K_M, Q6_K, and Q8_0) remain available.
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If you are starting a new project, we highly recommend using newer architectures (like Llama 3, Mistral, or Qwen) provided by official maintainers or active community members (e.g., Bartowski, TheBloke legacy files, or official organization handles).
⚠️ This repository is no longer actively maintained. Existing files are provided "as is" for archival and legacy hardware purposes.
MPT-7b and MPT-30B are part of the family of Mosaic Pretrained Transformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.
Brief
MPT-7B Storywriter is a Model based on MPT-7b, designed to read and write fictional stories with super long context lengths.
About GGUF format
gguf is the current file format used by the ggml library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov
Quantization variants
There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
Legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
Note:
Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions.
(This mainly refers to Falcon 7b and Starcoder models)
K-quants
K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
So, if possible, use K-quants.
With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
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
This model was trained by MosaicML and follows a modified decoder-only transformer architecture.
Note: This model requires that trust_remote_code=True be passed to the from_pretrained method. This is because we use a custom model architecture that is not yet part of the transformers package.
To use the optimized triton implementation of FlashAttention, you can load the model on GPU (cuda:0) with attn_impl='triton' and with bfloat16 precision:
python
1import torch
2import transformers
34name ='mosaicml/mpt-7b-storywriter'56config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)7config.attn_config['attn_impl']='triton'8config.init_device ='cuda:0'# For fast initialization directly on GPU!910model = transformers.AutoModelForCausalLM.from_pretrained(11 name,12 config=config,13 torch_dtype=torch.bfloat16,# Load model weights in bfloat1614 trust_remote_code=True15)
Although the model was trained with a sequence length of 2048 and finetuned with a sequence length of 65536,
ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:
python
1import transformers
23name ='mosaicml/mpt-7b'45config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)6config.max_seq_len =83968# (input + output) tokens can now be up to 8396878model = transformers.AutoModelForCausalLM.from_pretrained(9 name,10 config=config,11 trust_remote_code=True12)
The model can then be used, for example, within a text-generation pipeline.
Note: when running Torch modules in lower precision, it is best practice to use the torch.autocast context manager.
python
1from transformers import pipeline
23pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')45with torch.autocast('cuda', dtype=torch.bfloat16):6print(7 pipe('Here is a recipe for vegan banana bread:\n',8 max_new_tokens=100,9 do_sample=True,10 use_cache=True))
Community-Created Resources
These were not created by MosaicML, but you may find them useful. These links are not an endorsement of the creators or their content.
The full text of the The Great Gatsby (67873 tokens) was fed to the model, followed by the text "EPILOGUE"
TheRealFitzgerald233:
...
So we beat on, boats against the current, borne back ceaselessly into the past
EPILOGUE
MPT-7B-StoryWriter:
I have lived so long in the shadow of this dream that I may never see the bright sunlight of reality. I may look up some morning and find that I am six feet under, and that Gatsby, who gave me the dream and everything else, will have vanished behind me.
And so it was. I had hardly crossed the threshold of the old world when my eye caught the headlines of a newspaper, and I knew at once that my dream was over. I knew that Gatsby was dead.
And I knew that day that I didn't love her.
That night I took the last train for West Egg, and when I reached home I went directly to my attic and brought down all Gatsby's letters.
I have sorted them by date and placed them in the order in which they were written. They are very few in number.
They are dated between April of nineteen eighteen and January of nineteen nineteen, and the last one I have here is the one that told me he was dead.
There is something in that last letter that frightens me. It is the sense of a finality, of a completeness. I never heard of him after that, and I was glad.
But a few months ago I read somewhere that he had been seen in New York. He had become a great man.
And I knew that he had not changed at all.
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:
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.
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
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
}
End of original Model File
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