| 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 |