transformers/docs/source/en/model_doc/mpt.md

MPT

Overview

The MPT model was proposed by the MosaicML team and released with multiple sizes and finetuned variants. The MPT models is a series of open source and commercially usable LLMs pre-trained on 1T tokens.

MPT models are GPT-style decoder-only transformers with several improvements: performance-optimized layer implementations, architecture changes that provide greater training stability, and the elimination of context length limits by replacing positional embeddings with ALiBi.

• MPT base: MPT base pre-trained models on next token prediction
• MPT instruct: MPT base models fine-tuned on instruction based tasks
• MPT storywriter: MPT base models fine-tuned for 2500 steps on 65k-token excerpts of fiction books contained in the books3 corpus, this enables the model to handle very long sequences

The original code is available at the llm-foundry repository.

Read more about it in the release blogpost

Usage tips

• Learn more about some techniques behind training of the model in this section of llm-foundry repository
• If you want to use the advanced version of the model (triton kernels, direct flash attention integration), you can still use the original model implementation by adding trust_remote_code=True when calling from_pretrained.

Resources

• Fine-tuning Notebook on how to fine-tune MPT-7B on a free Google Colab instance to turn the model into a Chatbot.

MptConfig

autodoc MptConfig - all

MptModel

autodoc MptModel - forward

MptForCausalLM

autodoc MptForCausalLM - forward

MptForSequenceClassification

autodoc MptForSequenceClassification - forward

MptForTokenClassification

autodoc MptForTokenClassification - forward

MptForQuestionAnswering

autodoc MptForQuestionAnswering - forward