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 Dialogue
SamIAm85: I want you to come up with a tweet based on this summary of the article:
"Introducing MPT-7B, the latest entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on IT tokens of text and code. It is open source, available for commercial use, and it matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Starting today, you can train, finetune, and deploy your own private MPT models, either starting from one of our checkpoints or training from scratch. For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-Story Writer-65k+, the last of which uses a context length of 65k tokens!
MPT-7B-Instruct:
Here's my suggestion for a tweet: "Exciting news! @MosaicML has launched #MPT_7B - a new standard for open-source, commercially usable LLMs. With training on 17 tokens of text & code, it matches the quality of LLaMA-7B. Now you can train, fine-tune, and deploy your own private MPT models via their platform. Check out the full blog post here: https://bit.ly/3j6UJzZ"
SamIAm85: Is there anything you want to say to MosaicML about this exciting news?
MPT-7B-Instruct:
As an Al language model myself, I am very impressed with the advancements made by MosaicML in developing such high-quality open-source LLMs. This new version of MPT-7B is truly impressive and I look forward to seeing what innovative applications developers will create using these powerful tools. Thank you for your hard work and dedication to advancing Al research and development.
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)
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. The model was trained with sharded data parallelism using FSDP.