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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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inference: false |
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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](https://huggingface.co/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_ |
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* [Demo on Hugging Face Spaces](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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CC-By-SA-3.0 |
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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://mosaicml.me/slack)! |
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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( |
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'mosaicml/mpt-7b-instruct', |
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trust_remote_code=True |
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) |
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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 on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: |
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```python |
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import torch |
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import transformers |
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name = 'mosaicml/mpt-7b-instruct' |
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config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) |
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config.attn_config['attn_impl'] = 'triton' |
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config.init_device = 'cuda:0' # For fast initialization directly on GPU! |
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model = transformers.AutoModelForCausalLM.from_pretrained( |
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name, |
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config=config, |
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torch_dtype=torch.bfloat16, # Load model weights in bfloat16 |
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trust_remote_code=True |
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) |
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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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import transformers |
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name = 'mosaicml/mpt-7b-instruct' |
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config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) |
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config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096 |
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model = transformers.AutoModelForCausalLM.from_pretrained( |
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name, |
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config=config, |
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trust_remote_code=True |
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) |
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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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The model can then be used, for example, within a text-generation pipeline. |
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Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). |
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```python |
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from transformers import pipeline |
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pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') |
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with torch.autocast('cuda', dtype=torch.bfloat16): |
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print( |
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pipe('Here is a recipe for vegan banana bread:\n', |
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max_new_tokens=100, |
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do_sample=True, |
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use_cache=True)) |
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``` |
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### Formatting |
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This model was trained on data formatted in the dolly-15k format: |
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```python |
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INSTRUCTION_KEY = "### Instruction:" |
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RESPONSE_KEY = "### Response:" |
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INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request." |
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PROMPT_FOR_GENERATION_FORMAT = """{intro} |
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{instruction_key} |
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{instruction} |
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{response_key} |
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""".format( |
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intro=INTRO_BLURB, |
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instruction_key=INSTRUCTION_KEY, |
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instruction="{instruction}", |
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response_key=RESPONSE_KEY, |
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) |
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example = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week? Explain before answering." |
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fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example) |
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``` |
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In the above example, `fmt_ex` is ready to be tokenized and sent through the model. |
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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 trained on 8 A100-40GBs for about 2.3 hours using the [MosaicML Platform](https://www.mosaicml.com/platform). |
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The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer. |
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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-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information. |
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MPT-7B-Instruct 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?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). |
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## Disclaimer |
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The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. |
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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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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mosaicml__mpt-7b-instruct) |
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| Metric | Value | |
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| Avg. | 42.62 | |
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| ARC (25-shot) | 50.34 | |
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| HellaSwag (10-shot) | 77.91 | |
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| MMLU (5-shot) | 32.35 | |
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| TruthfulQA (0-shot) | 35.08 | |
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| Winogrande (5-shot) | 70.48 | |
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| GSM8K (5-shot) | 2.81 | |
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| DROP (3-shot) | 29.4 | |
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