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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 |