use below prompt to extract output:

entities could be anything like shares bought and sold, acquisitions etc. PROMPT = f"""Your task is to extract the person's name from the sentence delimited by triple backticks along with their details mentioned below: 1. (if available) 2. (if available) 8.Acquisitions by the Company (if available) Output should be list of dictionaries only. No python code in output {text} """

Output should be list of dictionaries only. No python code in output

MPT-7B-Instruct

MPT-7B-Instruct is a model for short-form instruction following. It is built by finetuning MPT-7B on a dataset derived from the Databricks Dolly-15k and the Anthropic Helpful and Harmless (HH-RLHF) datasets.

• License: CC-By-SA-3.0
• 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

CC-By-SA-3.0

Documentation

• Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs
• Codebase (mosaicml/llm-foundry repo)
• Questions: Feel free to contact us via the MosaicML Community Slack!

Example Question/Instruction

Longboi24:

What is a quoll?

MPT-7B-Instruct:

A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America

How to Use

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.

It includes options for many training efficiency features such as FlashAttention (Dao et al. 2022), ALiBi, QK LayerNorm, and more.

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

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

• 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.

Limitations and Biases

The following language is modified from EleutherAI's GPT-NeoX-20B

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

@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: 2023-05-08}, % change this date
    urldate   = {2023-05-08} % change this date
}