license: cc-by-sa-3.0
datasets:
- mosaicml/dolly_hhrlhf
tags:
- Composer
- MosaicML
- llm-foundry
MPT-7B-Instruct
MPT-7B-Instruct is a model for short-form instruction following. It is built by finetuning MPT-7B (Base) on a dataset derived from the Databricks Dolly-15k and the Anthropic Helpful and Harmless (HH-RLHF) datasets.
- License: CC-By-SA-3.0 (commercial use permitted)
- Online Demo
This model was trained by MosaicML and follows a modified decoder-only transformer architecture.
Model Date
May 5, 2023
Model License
Apache-2.0 (commercial use permitted)
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, torch_dtype=torch.bfloat16)
To use the optimized triton implementation of FlashAttention, you can load with attn_impl='triton' and move the model to bfloat16 like so:
model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-instruct', trust_remote_code=True, torch_dtype=torch.bfloat16, attn_impl='triton')
model.to(device='cuda:0', dtype=torch.bfloat16)
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:
- 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 finetuned on 440 A100-40GBs for about half a day using the MosaicML Platform.
Acknowledgements
This model was finetuned by Sam Havens and the MosaicML NLP team