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: 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
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 βcoolβ) is one of Australiaβs 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 on GPU (cuda:0
) with attn_impl='triton'
and with bfloat16
precision:
import torch
import transformers
name = 'mosaicml/mpt-7b-instruct'
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-instruct'
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")
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.
from transformers import pipeline
pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
with torch.autocast('cuda', dtype=torch.bfloat16):
print(
pipe('Here is a recipe for vegan banana bread:\n',
max_new_tokens=100,
do_sample=True,
use_cache=True))
Formatting
This model was trained on data formatted in the dolly-15k format:
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
PROMPT_FOR_GENERATION_FORMAT = """{intro}
{instruction_key}
{instruction}
{response_key}
""".format(
intro=INTRO_BLURB,
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY,
)
example = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering."
fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example)
In the above example, fmt_ex
is ready to be tokenized and sent through the model.
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
Hyperparameter | Value |
---|---|
n_parameters | 6.7B |
n_layers | 32 |
n_heads | 32 |
d_model | 4096 |
vocab size | 50432 |
sequence length | 2048 |
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 A100-40GBs for about 2.3 hours using the MosaicML Platform. The model was trained with sharded data parallelism using FSDP and used the AdamW optimizer.
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.
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
}
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