MPT
Source: transformers

# MPT## OverviewThe MPT model was proposed by the [MosaicML](https://www.mosaicml.com/) 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 sequencesThe original code is available at the  [`llm-foundry`](https://github.com/mosaicml/llm-foundry/tree/main) repository.Read more about it [in the release blogpost](https://www.mosaicml.com/blog/mpt-7b)## Usage tips- Learn more about some techniques behind training of the model [in this section of llm-foundry repository](https://github.com/mosaicml/llm-foundry/blob/main/TUTORIAL.md#faqs)- 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](https://colab.research.google.com/drive/1HCpQkLL7UXW8xJUJJ29X7QAeNJKO0frZ?usp=sharing) 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