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