MPT-1b-RedPajama-200b is a 1.3 billion parameter decoder-only transformer trained on the RedPajama dataset. 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. This model was trained by MosaicML and follows a modified decoder-only transformer architecture.
April 20, 2023
Note: This model requires that
trust_remote_code=True be passed to the
This is because we use a custom model architecture
MosaicGPT that is not yet part of the
MosaicGPT includes options for many training efficiency features such as FlashAttention (Dao et al. 2022), ALIBI, QK LayerNorm, and more.
import transformers model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b', trust_remote_code=True)
To use the optimized triton implementation of FlashAttention, you can load with
attn_impl='triton' and move the model to
bfloat16 like so:
model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b', trust_remote_code=True, attn_impl='triton') model.to(device='cuda:0', dtype=torch.bfloat16)
This model uses the MosaicML LLM codebase, which can be found in the MosaicML Examples Repository. The architecture is a modification of a standard decoder-only transformer. The transformer has 24 layers, 16 attention heads, and width 2048. The model has been modified from a standard transformer in the following ways:
- It uses ALiBi and does not use positional embeddings.
- It uses QK LayerNorm.
- It does not use biases.
The model was trained for 200B tokens (batch size 2200, sequence length 2048). It was trained on the following data mix:
- 67% RedPajama Common Crawl
- 15% C4
- 4.5% RedPajama GitHub
- 4.5% RedPajama Wikipedia
- 4.5% RedPajama Books
- 2.5% RedPajama Arxiv
- 2% RedPajama StackExchange
This is the same mix of data as was used in the Llama series of models](https://arxiv.org/abs/2302.13971).
Each sample was chosen from one of the datasets, with the dataset selected with the probability specified above. The examples were shuffled within each dataset. Each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length.
The data was tokenized using the EleutherAI/gpt-neox-20b tokenizer.
This model was trained on 440 A100-40GBs for about half a day using the MosaicML Platform. The model was trained with sharded data parallelism using FSDP.
This model builds on the work of Together, which created the RedPajama dataset with the goal of mimicking the training data used to create the Llama series of models. 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.
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.
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