[This is a experimental fork of mosaicml/mpt-1b-redpajama-200b-dolly]
MPT-1b-RedPajama-200b-dolly
MPT-1b-RedPajama-200b-dolly is a 1.3 billion parameter decoder-only transformer pre-trained on the RedPajama dataset and subsequently fine-tuned on the Databricks Dolly instruction dataset. The model was pre-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.
This model is an instruction fine-tuned version of mpt-1b-redpajama-200b. In other words, the pre-trained version of this model is mpt-1b-redpajama-200b.
Model Date
April 20, 2023
How to Use
Note: This model requires that trust_remote_code=True be passed to the from_pretrained method.
This is because we use a custom model architecture MosaicGPT that is not yet part of the transformers package.
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-dolly', 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-dolly', trust_remote_code=True, attn_impl='triton')
model.to(device='cuda:0', dtype=torch.bfloat16)
Model Description
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.
Training Data
Pre-Training
The model was pre-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.
Fine-Tuning
We fine tuned this model on the databricks-dolly-15k dataset released by Databricks, following the same hyperparameters found in their train_dolly.py script.
Training Configuration
This model was pre-trained on 440 A100-40GBs for about half a day using the MosaicML Platform. The model was pre-trained with sharded data parallelism using FSDP.
Acknowledgements
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.
This model also builds on the work of Databricks, which created the Dolly instruction fine-tuning dataset.
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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