Hugging Face's logo

TheBloke
/
MPT-7B-GGML

Transformers
mpt Composer MosaicML llm-foundry StreamingDatasets text-generation-inference 6 papers
Model card Files Community
4
Edit model card
TheBlokeAI

MPT-7B GGML

This is GGML format quantised 4-bit, 5-bit and 8-bit GGML models of MosaicML's MPT-7B.

This repo is the result of converting to GGML and quantising.

Please note that these MPT GGMLs are not compatbile with llama.cpp. Please see below for a list of tools known to work with these model files.

Repositories available

Provided files

Name Quant method Bits Size RAM required Use case
mpt-7b.ggmlv3.q4_0.bin q4_0 4bit 4.16GB 6.2GB 4-bit.
mpt-7b.ggmlv3.q4_1.bin q4_0 4bit 4.99GB 7.2GB 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
mpt-7b.ggmlv3.q5_0.bin q5_0 5bit 4.57GB 6.8GB 5-bit. Higher accuracy, higher resource usage and slower inference.
mpt-7b.ggmlv3.q5_1.bin q5_1 5bit 4,99GB 7.2GB 5-bit. Even higher accuracy, and higher resource usage and slower inference.
mpt-7b.ggmlv3.q8_0.bin q8_0 8bit 7.48GB 9.6GB 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use.
mpt-7b.ggmlv3.fp16.bin fp16 16bit 13.3GB 15.5GB Full 16-bit.

Compatibilty

These files are not compatible with llama.cpp.

Currently they can be used with:

  • KoboldCpp, a powerful inference engine based on llama.cpp, with good UI: KoboldCpp
  • The ctransformers Python library, which includes LangChain support: ctransformers
  • The GPT4All-UI which uses ctransformers: GPT4All-UI
  • rustformers' llm
  • The example mpt binary provided with ggml

As other options become available I will endeavour to update them here (do let me know in the Community tab if I've missed something!)

Tutorial for using GPT4All-UI

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

Original model card: MPT-7B

MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by MosaicML.

MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference.

These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases (ALiBi). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's FasterTransformer.

This model uses the MosaicML LLM codebase, which can be found in the llm-foundry repository. It was trained by MosaicML’s NLP team on the MosaicML platform for LLM pretraining, finetuning, and inference.

How is this model different?

MPT-7B is

Models finetuned off MPT-7B:

The following models are finetuned on MPT-7B:

Model Date

May 5, 2023

Model License

Apache-2.0

Documentation

How to Use

This model is best used with the MosaicML llm-foundry repository for training and finetuning.

import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
  'mosaicml/mpt-7b',
  trust_remote_code=True
)

Note: This model requires that trust_remote_code=True be passed to the from_pretrained method. This is because we use a custom MPT model architecture that is not yet part of the Hugging Face transformers package. MPT includes options for many training efficiency features such as FlashAttention, ALiBi, QK LayerNorm, and more.

To use the optimized triton implementation of FlashAttention, you can load the model with attn_impl='triton' and move the model to bfloat16:

config = transformers.AutoConfig.from_pretrained(
  'mosaicml/mpt-7b',
  trust_remote_code=True
)
config.attn_config['attn_impl'] = 'triton'

model = transformers.AutoModelForCausalLM.from_pretrained(
  'mosaicml/mpt-7b',
  config=config,
  torch_dtype=torch.bfloat16,
  trust_remote_code=True
)
model.to(device='cuda:0')

Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example:

config = transformers.AutoConfig.from_pretrained(
  'mosaicml/mpt-7b',
  trust_remote_code=True
)
config.update({"max_seq_len": 4096})
model = transformers.AutoModelForCausalLM.from_pretrained(
  'mosaicml/mpt-7b',
  config=config,
  trust_remote_code=True
)

This model was trained with the EleutherAI/gpt-neox-20b tokenizer.

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")

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:

Hyperparameter Value
n_parameters 6.7B
n_layers 32
n_heads 32
d_model 4096
vocab size 50432
sequence length 2048

Training Data

Streaming Datasets

Data was formatted using the MosaicML StreamingDataset library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset.

Data Mix

The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix:

Data Source Number of Tokens in Source Proportion Effective Number of Tokens Epochs
mC4 3.1.0 - English 417.99 B 0.33 330 B 0.14
C4 - English - SemDedup 80% 100.42 B 0.299 299 B 2.98
RedPajama - CommonCrawl 878.45 B 0.1 100 B 0.11
The Stack - Selected Languages 463.78 B 0.1 100 B 0.22
RedPajama - Wikipedia - En 4.87 B 0.04 40 B 8.21
The Stack - Markdown 107.07 B 0.035 35 B 0.33
S2ORC 48.85 B 0.033 33 B 0.68
RedPajama - Books 26.02 B 0.03 30B 1.15
RedPajama - arXiv 28.10 B 0.019 19 B 0.68
RedPajama - StackExchange 20.54 B 0.014 14 B 0.68

Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.

The data was tokenized using the EleutherAI/gpt-neox-20b tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters.

The model vocabulary size of 50432 was set to be a multiple of 128 (as in MEGATRON-LM), model flop utilization (MFU) increased by up to four percentage points.

Training Configuration

This model was trained on 440 A100-40GBs for about 9.5 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 (Base) is not intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent.

MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B 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.

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,
    ly 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
}
Downloads last month
8
Inference API (serverless) has been turned off for this model.

Datasets used to train TheBloke/MPT-7B-GGML