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minotaur-mpt-7B-GGML

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OpenAccess AI Collective's Minotaur MPT 7B GGML

These files are GGML format model files for OpenAccess AI Collective's Minotaur MPT 7B.

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

Prompt template

Please see the "Original Model Card" below for more details about how to prompt this model.

USER: prompt goes here
ASSISTANT:

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

Provided files

Name Quant method Bits Size Max RAM required Use case
minotaur-mpt-7b.ggmlv3.q4_0.bin q4_0 4 3.74 GB 6.24 GB Original llama.cpp quant method, 4-bit.
minotaur-mpt-7b.ggmlv3.q4_1.bin q4_1 4 4.16 GB 6.66 GB Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
minotaur-mpt-7b.ggmlv3.q5_0.bin q5_0 5 4.57 GB 7.07 GB Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
minotaur-mpt-7b.ggmlv3.q5_1.bin q5_1 5 4.99 GB 7.49 GB Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
minotaur-mpt-7b.ggmlv3.q8_0.bin q8_0 8 7.07 GB 9.57 GB Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

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.

Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.

Patreon special mentions: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.

Thank you to all my generous patrons and donaters!

Original model card: OpenAccess AI Collective's Minotaur MPT 7B

Built with Axolotl 💵 Donate to OpenAccess AI Collective to help us keep building great tools and models!

Minotaur MPT 7B

Minotaur 7B is an instruct fine-tuned model on top of MPT-7B. Minotaur 7B is fine-tuned on only completely open datasets making this model reproducible by anyone.

Questions, comments, feedback, looking to donate, or want to help? Reach out on our Discord or email [email protected]

Prompts

Chat only style prompts using USER:,ASSISTANT:.

Training Datasets

Minotaur 7B model is fine-tuned on the following datasets:

Shoutouts

Special thanks to Nanobit for helping with Axolotl and TheBloke for quantizing these models are more accessible to all.

Demo

HF Demo in Spaces coming soon.

Release Notes

Build

Minotaur was built with Axolotl on 7xA100 80GB

  • 3 epochs taking approximately 6 hours

Bias, Risks, and Limitations

Minotaur has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). Minotaur was fine-tuned from the base model MPT-7B, please refer to its model card's Limitations Section for relevant information. (included below)

Examples - results may vary based on temperature and other settings 

USER: your have 3 apples. you eat 2 pears. how many apples do you have left?
ASSISTANT:To find the quantity of apples, we can subtract the number of pears from the number of apples.

In this case, there are 3 apples and 2 pears. This means there are 3 apples left.

wut?

USER: your have 3 apples. you eat 2 pears. how many apples do you have left?
ASSISTANT:You don't have any apples left. You had 3 and after eating 2 pears, it is 2. You ate the other apple.

🤣

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: your have 3 apples. you eat 2 pears. how many apples do you have left? Use multi-step reasoning.
ASSISTANT: I understand. You have three apples, two pears, and you ate two of the pears. You have three apples left.

👌 looks like you need the system prompt

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
USER: Solve for x: 5x = 3x + 10
ASSISTANT: To find the value of x, you can solve this equation by first adding both sides of the equation, as follows:
5x = 3x + 10
Now, simplify the equation to get:
x = 2.5

yup, it still sucks at math

USER: tell me a joke. then explain why it's funny.

ASSISTANT: What did the blind man say to the man eating steak?
He said, "Is that a cow or a sandwich?"

USER: write a sonnet about doves

ASSISTANT:Doves are a beautiful bird, and you can write a sonnet about them to express your love for them. This sonnet can include imagery of doves, including the sound of their wings as they fly, the softness of their feathers, and the peacefulness they bring to their surroundings. The structure of this sonnet can be similar to the Shakespearean sonnets, with 14 lines divided into three groups of rhyming couplets. In the first stanza, you can express your love for doves as they are a symbol of peace and tranquility. The second stanza can describe the beauty of the dove's plumage and the softness of their feathers. The third stanza can be about the peacefulness that they bring to their surroundings, such as in a garden or park. You can end the sonnet with a final line that encapsulates your feelings about doves.

Here is the sonnet for your reference:

> Doves are a symbol of peace and tranquility,
> A gentle song that soothes the soul.
> Their soft feathers and sweet voices bring a sense of warmth and calm to their surroundings,
> And they are a welcome sight in any landscape.
> 
> Their grace and beauty are a reminder of the simple pleasures in life,
> And their peaceful presence is a welcome respite from the stresses of everyday life.
> 
> Doves are a symbol of love and friendship,
> And their companionship is a gift we can all cherish.
> 
> Let us embrace the beauty and serenity of doves,
> And let us cherish the gift of their presence in our lives.
> 
> May we always find beauty and serenity in the simple pleasures of life,
> And may we cherish the gift of friendship and love.
> 
> Doves are a symbol of peace and tranquility,
> A gentle song that soothes the soul.
> Their soft feathers and sweet voices bring a sense of warmth and calm to their surroundings,
> And they are a welcome sight in any landscape.
> 
> Their grace and beauty are a reminder of the simple pleasures in life,
> And their peaceful presence is a welcome respite from the stresses of everyday life.
> 
> Doves are a symbol of love and friendship,
> And their companionship is a gift we can all cherish.
> 
> Let us embrace the beauty and serenity of doves,
> And let us cherish the gift of their presence in our lives.
> 
> May we always find beauty and serenity in the simple pleasures of life,
> And may we cherish the gift of friendship and love.

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 on GPU (cuda:0) with attn_impl='triton' and with bfloat16 precision:

import torch
import transformers

name = 'mosaicml/mpt-7b'

config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.attn_config['attn_impl'] = 'triton'
config.init_device = 'cuda:0' # For fast initialization directly on GPU!

model = transformers.AutoModelForCausalLM.from_pretrained(
  name,
  config=config,
  torch_dtype=torch.bfloat16, # Load model weights in bfloat16
  trust_remote_code=True
)

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:

import transformers

name = 'mosaicml/mpt-7b'

config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096

model = transformers.AutoModelForCausalLM.from_pretrained(
  name,
  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
}
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