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--- |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- jeffwan/sharegpt_vicuna |
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- Hello-SimpleAI/HC3 |
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- tatsu-lab/alpaca |
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- Anthropic/hh-rlhf |
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- victor123/evol_instruct_70k |
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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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inference: false |
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--- |
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# MPT-7B-Chat |
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MPT-7B-Chat is a chatbot-like model for dialogue generation. |
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It was built by finetuning [MPT-7B](https://huggingface.co/spaces/mosaicml/mpt-7b) on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), |
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[Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets. |
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* License: _CC-By-NC-SA-4.0_ (non-commercial use only) |
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* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-chat) |
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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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_CC-By-NC-SA-4.0_ (non-commercial use only) |
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## Documentation |
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* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://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 Dialogue |
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**SamIAm85**: |
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>I want you to come up with a tweet based on this summary of the article: |
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>"Introducing MPT-7B, the latest entry in our MosaicML Foundation Series. |
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>MPT-7B is a transformer trained from scratch on IT tokens of text and code. |
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>It is open source, available for commercial use, and it matches the quality of LLaMA-7B. |
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>MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. |
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>Starting today, you can train, finetune, and deploy your own private MPT models, either starting from one of our checkpoints or training from scratch. |
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>For inspiration, we are also releasing three finetuned models in addition to the base MPT-7B: MPT-7B-Instruct, MPT-7B-Chat, and MPT-7B-Story Writer-65k+, the last of which uses a context length of 65k tokens! |
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**MPT-7B-Instruct**: |
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>Here's my suggestion for a tweet: |
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>"Exciting news! @MosaicML has launched #MPT_7B - a new standard for open-source, commercially usable LLMs. With training on 17 tokens of text & code, it matches the quality of LLaMA-7B. Now you can train, fine-tune, and deploy your own private MPT models via their platform. Check out the full blog post here: https://bit.ly/3j6UJzZ" |
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**SamIAm85**: |
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>Is there anything you want to say to MosaicML about this exciting news? |
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**MPT-7B-Instruct**: |
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> As an Al language model myself, I am very impressed with the advancements made by MosaicML in developing such high-quality open-source LLMs. |
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> This new version of MPT-7B is truly impressive and I look forward to seeing what innovative applications developers will create using these powerful tools. |
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> Thank you for your hard work and dedication to advancing Al research and development. |
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## How to Use |
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This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. |
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```python |
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import transformers |
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model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-chat', trust_remote_code=True) |
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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 use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. |
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`MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. |
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To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model with `attn_impl='triton'` and move the model to `bfloat16`: |
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```python |
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config = transformers.AutoConfig.from_pretrained('mosaicml/mpt-7b-chat', trust_remote_code=True) |
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config.attn_config['attn_impl'] = 'triton' |
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model = transformers.AutoModelForCausalLM.from_pretrained('mosaicml/mpt-7b-chat', config=config, torch_dtype=torch.bfloat16, trust_remote_code=True) |
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model.to(device='cuda:0') |
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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-chat', 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-chat', 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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## Limitations and Biases |
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_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ |
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MPT-7B-Chat can produce factually incorrect output, and should not be relied on to produce factually accurate information. |
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MPT-7B-Chat was trained on various public datasets. |
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While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. |
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## Acknowledgements |
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This model was finetuned by Sam Havens and the MosaicML NLP team |
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## MosaicML Platform |
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If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo). |
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## Citation |
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Please cite this model using the following format: |
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``` |
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@online{MosaicML2023Introducing, |
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author = {MosaicML NLP Team}, |
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title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs}, |
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year = {2023}, |
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url = {www.mosaicml.com/blog/mpt-7b}, |
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note = {Accessed: 2023-03-28}, % change this date |
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urldate = {2023-03-28} % change this date |
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} |
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``` |
