Text Generation
Transformers
PyTorch
mosaic_gpt
custom_code
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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - togethercomputer/RedPajama-Data-1T
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+ ---
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+
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+ # MPT-1B-RedPajama
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+
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+ MPT-1B-RedPajama is a 1B parameter decoder-only transformer trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
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+ The model was trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions as were used by the [Llama series of models](https://arxiv.org/abs/2302.13971).
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+ This model was trained by [MosaicML](https://www.mosaicml.com) and follows the MPT architecture.
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+
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+ ## Model Date
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+
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+ April 19, 2023
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+
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+ ## How to Use
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+
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+ Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
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+ This is because we train using [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), which is not part of the `transformers` library and depends on [Triton](https://github.com/openai/triton) and some custom PyTorch code.
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+
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+ ```python
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+ import transformers
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+ model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mosaic-llama-redpajama-final-candidate', trust_remote_code=True)```
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+ ```
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+
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+ ## Model Description
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+
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+ This model uses the MPT architecture, which can be found in the [MosaicML Examples Repository](https://github.com/mosaicml/examples/tree/v0.0.4/examples/llm).
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+ The MPT architecture is a modification of a standard decoder-only transformer.
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+ The transformer has 24 layers, 16 attention heads, and width 2048.
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+ The model has been modified from a standard transformer in the following ways:
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+ * It uses FlashAttention.
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+ * It uses ALiBi position encodings.
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+ * It does not use biases.
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+ * It includes layernorm after the keys and queries of the attention operation.
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+
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+ ## Training Data
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+
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+ The model was trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix:
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+ * 67% RedPajama Common Crawl
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+ * 15% [C4](https://huggingface.co/datasets/c4)
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+ * 4.5% RedPajama GitHub
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+ * 4.5% RedPajama Wikipedia
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+ * 4.5% RedPajama Books
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+ * 2.5% RedPajama Arxiv
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+ * 2% RedPajama StackExchange
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+
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+ This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971).
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+
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+ Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above.
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+ The examples were shuffled within each dataset.
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+ Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
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+
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+ The data was tokenized using the GPT-NeoX tokenizer.
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+
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+ ## Acknowledgements
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+
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+ This model builds on the work of [Together](https://www.together.xyz), which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models.
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+ We gratefully acknowledge the hard work of the team that put together this dataset, and we hope this model serves as a useful companion to that work.
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+
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+ We also gratefully acknowledge the work of the researchers who created the Llama series of models, which was the impetus for our efforts and those who worked on the RedPajama project.