mpt-7b-instruct / README.md

add text-generation pipeline example with autocast (#47)

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	---
	license: cc-by-sa-3.0
	datasets:
	- mosaicml/dolly_hhrlhf
	tags:
	- Composer
	- MosaicML
	- llm-foundry
	inference: false
	---

	# MPT-7B-Instruct

	MPT-7B-Instruct is a model for short-form instruction following.
	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.
	* License: _CC-By-SA-3.0_
	* [Demo on Hugging Face Spaces](https://huggingface.co/spaces/mosaicml/mpt-7b-instruct)


	This model was trained by [MosaicML](https://www.mosaicml.com) and follows a modified decoder-only transformer architecture.

	## Model Date

	May 5, 2023

	## Model License

	CC-By-SA-3.0

	## Documentation

	* [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b)
	* [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/)
	* Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)!

	### Example Question/Instruction

	Longboi24:
	> What is a quoll?

	MPT-7B-Instruct:

	>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

	## 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 that is not yet part of the `transformers` package.

	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.

	```python
	import transformers
	model = transformers.AutoModelForCausalLM.from_pretrained(
	'mosaicml/mpt-7b-instruct',
	trust_remote_code=True
	)
	```
	Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method.
	This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package.
	`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.

	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:
	```python
	import torch
	import transformers

	name = 'mosaicml/mpt-7b-instruct'

	config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
	config.attn_config['attn_impl'] = 'triton'
	config.init_device = 'cuda:0' # For fast initialization directly on GPU!

	model = transformers.AutoModelForCausalLM.from_pretrained(
	name,
	config=config,
	torch_dtype=torch.bfloat16, # Load model weights in bfloat16
	trust_remote_code=True
	)
	```

	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:

	```python
	import transformers

	name = 'mosaicml/mpt-7b-instruct'

	config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True)
	config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096

	model = transformers.AutoModelForCausalLM.from_pretrained(
	name,
	config=config,
	trust_remote_code=True
	)
	```

	This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.

	```python
	from transformers import AutoTokenizer
	tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
	```

	The model can then be used, for example, within a text-generation pipeline.
	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).

	```python
	from transformers import pipeline

	pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')

	with torch.autocast('cuda', dtype=torch.bfloat16):
	print(
	pipe('Here is a recipe for vegan banana bread:\n',
	max_new_tokens=100,
	do_sample=True,
	use_cache=True))
	```

	### Formatting

	This model was trained on data formatted in the dolly-15k format:

	```python
	INSTRUCTION_KEY = "### Instruction:"
	RESPONSE_KEY = "### Response:"
	INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
	PROMPT_FOR_GENERATION_FORMAT = """{intro}
	{instruction_key}
	{instruction}
	{response_key}
	""".format(
	intro=INTRO_BLURB,
	instruction_key=INSTRUCTION_KEY,
	instruction="{instruction}",
	response_key=RESPONSE_KEY,
	)

	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."
	fmt_ex = PROMPT_FOR_GENERATION_FORMAT.format(instruction=example)
	```

	In the above example, `fmt_ex` is ready to be tokenized and sent through the model.

	## Model Description

	The architecture is a modification of a standard decoder-only transformer.

	The model has been modified from a standard transformer in the following ways:
	* It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf)
	* It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings
	* It does not use biases


	\| Hyperparameter \| Value \|
	\|----------------\|-------\|
	\|n_parameters \| 6.7B \|
	\|n_layers \| 32 \|
	\| n_heads \| 32 \|
	\| d_model \| 4096 \|
	\| vocab size \| 50432 \|
	\| sequence length \| 2048 \|

	## PreTraining Data

	For more details on the pretraining process, see [MPT-7B](https://huggingface.co/mosaicml/mpt-7b).

	The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer.

	### Training Configuration

	This model was trained on 8 A100-40GBs for about 2.3 hours using the [MosaicML Platform](https://www.mosaicml.com/platform).
	The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the AdamW optimizer.

	## Limitations and Biases

	_The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_

	MPT-7B-Instruct can produce factually incorrect output, and should not be relied on to produce factually accurate information.
	MPT-7B-Instruct was trained on various public datasets.
	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.


	## Acknowledgements

	This model was finetuned by Sam Havens and the MosaicML NLP team

	## MosaicML Platform

	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).

	## Disclaimer

	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.

	## Citation

	Please cite this model using the following format:

	```
	@online{MosaicML2023Introducing,
	author = {MosaicML NLP Team},
	title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs},
	year = {2023},
	url = {www.mosaicml.com/blog/mpt-7b},
	note = {Accessed: 2023-03-28}, % change this date
	urldate = {2023-03-28} % change this date
	}
	```