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--- |
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license: cc-by-sa-3.0 |
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datasets: |
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- mosaicml/dolly_hhrlhf |
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tags: |
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- Composer |
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- MosaicML |
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- llm-foundry |
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--- |
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# MPT-7B-Instruct |
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MPT-7B-Instruct is a model for short-form instruction following. |
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It is built by finetuning [MPT-7B (Base)](https://huggingface.co/spaces/mosaicml/mpt-7b) on a [dataset](https://huggingface.co/datasets/sam-mosaic/dolly_hhrlhf) derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. |
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* License: _CC-By-SA-3.0_ (commercial use permitted) |
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* [Online Demo](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct) |
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This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture. |
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## Model Date |
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May 5, 2023 |
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## Model License |
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Apache-2.0 (commercial use permitted) |
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## Documentation |
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* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](www.mosaicml.com/blog/mpt-7b) |
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* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) |
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* Questions: Feel free to contact us via the [MosaicML Community Slack](https://join.slack.com/t/mosaicml-community/shared_invite/zt-w0tiddn9-WGTlRpfjcO9J5jyrMub1dg)! |
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### Example Question/Instruction |
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**Longboi24** |
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> What is a quoll? |
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**MPT-7B-Instruct** |
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>A Quoll (pronounced “cool”) is one of Australia’s native carnivorous marsupial mammals, which are also known as macropods or wallabies in other parts around Asia and South America |
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## How to Use |
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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 that is not yet part of the `transformers` package. |
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It includes options for many training efficiency features such as [FlashAttention (Dao et al. 2022)](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), QK LayerNorm, and more. |
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```python |
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import transformers |
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model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-instruct', trust_remote_code=True, torch_dtype=torch.bfloat16) |
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``` |
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To use the optimized triton implementation of FlashAttention, you can load with `attn_impl='triton'` and move the model to `bfloat16` like so: |
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```python |
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model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-instruct', trust_remote_code=True, torch_dtype=torch.bfloat16, attn_impl='triton') |
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model.to(device='cuda:0', dtype=torch.bfloat16) |
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``` |
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Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: |
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```python |
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config = transformers.AutoConfig.from_pretrained('mosaicml/mpt-7b', trust_remote_code=True) |
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config.update({"max_seq_len": 4096}) |
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model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b', config=config, trust_remote_code=True) |
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``` |
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This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. |
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```python |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") |
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``` |
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## Model Description |
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The architecture is a modification of a standard decoder-only transformer. |
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The model has been modified from a standard transformer in the following ways: |
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* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) |
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* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings |
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* It does not use biases |
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| Hyperparameter | Value | |
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|----------------|-------| |
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|n_parameters | 6.7B | |
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|n_layers | 32 | |
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| n_heads | 32 | |
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| d_model | 4096 | |
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| vocab size | 50432 | |
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| sequence length | 2048 | |
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## PreTraining Data |
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For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b). |
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The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. |
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## Training Configuration |
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This model was finetuned on 440 A100-40GBs for about half a day using the [MosaicML Platform](https://www.mosaicml.com/platform). |
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## Acknowledgements |
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This model was finetuned by Sam Havens and the MosaicML NLP team |
