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.
1import transformers
2model = 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:
1model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-1b-redpajama-200b-dolly', trust_remote_code=True, attn_impl='triton')
2model.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.
