From c7f79f037a37691aca73d91b3983201478b90c3c Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 20 Oct 2023 03:32:06 -0400 Subject: [PATCH 01/19] nougat Former-commit-id: 40b4c9efd943cf2d56f7674462350e1cf3b1d850 --- README.md | 2 +- docs/swarms/models/nougat.md | 118 +++++++++++++++++++++++++++++++++++ mkdocs.yml | 1 + pyproject.toml | 3 + requirements.txt | 3 + swarms/__init__.py | 8 +-- swarms/models/__init__.py | 21 ++++++- swarms/models/anthropic.py | 10 +-- swarms/models/base.py | 5 +- swarms/models/bing_chat.py | 18 ++++-- swarms/models/idefics.py | 6 +- swarms/models/kosmos_two.py | 2 +- swarms/models/nougat.py | 68 ++++++++++++++++++++ swarms/models/phi.py | 1 + swarms/models/trocr.py | 19 ++++++ swarms/models/zephyr.py | 19 +++--- swarms/tools/edge_gpt.py | 10 ++- 17 files changed, 284 insertions(+), 30 deletions(-) create mode 100644 docs/swarms/models/nougat.md create mode 100644 swarms/models/nougat.py create mode 100644 swarms/models/phi.py create mode 100644 swarms/models/trocr.py diff --git a/README.md b/README.md index 9b7eeccf..8b1fc68a 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,7 @@ At Swarms, we're transforming the landscape of AI from siloed AI agents to a uni ----- # 🤝 Schedule a 1-on-1 Session -Book a [1-on-1 Session with Kye](https://calendly.com/apacai/agora), the Creator, to discuss any issues, provide feedback, or explore how we can improve Swarms for you. +Book a [1-on-1 Session with Kye](https://calendly.com/swarm-corp/30min), the Creator, to discuss any issues, provide feedback, or explore how we can improve Swarms for you. ---------- diff --git a/docs/swarms/models/nougat.md b/docs/swarms/models/nougat.md new file mode 100644 index 00000000..88945b5b --- /dev/null +++ b/docs/swarms/models/nougat.md @@ -0,0 +1,118 @@ +# Nougat Documentation + +## Introduction + +Welcome to the documentation for Nougat, a versatile model designed by Meta for transcribing scientific PDFs into user-friendly Markdown format, extracting information from PDFs, and extracting metadata from PDF documents. This documentation will provide you with a deep understanding of the Nougat class, its architecture, usage, and examples. + +## Overview + +Nougat is a powerful tool that combines language modeling and image processing capabilities to convert scientific PDF documents into Markdown format. It is particularly useful for researchers, students, and professionals who need to extract valuable information from PDFs quickly. With Nougat, you can simplify complex PDFs, making their content more accessible and easy to work with. + +## Class Definition + +```python +class Nougat: + def __init__( + self, + model_name_or_path="facebook/nougat-base", + min_length: int = 1, + max_new_tokens: int = 30, + ): +``` + +## Purpose + +The Nougat class serves the following primary purposes: + +1. **PDF Transcription**: Nougat is designed to transcribe scientific PDFs into Markdown format. It helps convert complex PDF documents into a more readable and structured format, making it easier to extract information. + +2. **Information Extraction**: It allows users to extract valuable information and content from PDFs efficiently. This can be particularly useful for researchers and professionals who need to extract data, figures, or text from scientific papers. + +3. **Metadata Extraction**: Nougat can also extract metadata from PDF documents, providing essential details about the document, such as title, author, and publication date. + +## Parameters + +- `model_name_or_path` (str): The name or path of the pretrained Nougat model. Default: "facebook/nougat-base". +- `min_length` (int): The minimum length of the generated transcription. Default: 1. +- `max_new_tokens` (int): The maximum number of new tokens to generate in the Markdown transcription. Default: 30. + +## Usage + +To use Nougat, follow these steps: + +1. Initialize the Nougat instance: + +```python +from swarms.models import Nougat + +nougat = Nougat() +``` + +### Example 1 - Initialization + +```python +nougat = Nougat() +``` + +2. Transcribe a PDF image using Nougat: + +```python +markdown_transcription = nougat("path/to/pdf_file.png") +``` + +### Example 2 - PDF Transcription + +```python +nougat = Nougat() +markdown_transcription = nougat("path/to/pdf_file.png") +``` + +3. Extract information from a PDF: + +```python +information = nougat.extract_information("path/to/pdf_file.png") +``` + +### Example 3 - Information Extraction + +```python +nougat = Nougat() +information = nougat.extract_information("path/to/pdf_file.png") +``` + +4. Extract metadata from a PDF: + +```python +metadata = nougat.extract_metadata("path/to/pdf_file.png") +``` + +### Example 4 - Metadata Extraction + +```python +nougat = Nougat() +metadata = nougat.extract_metadata("path/to/pdf_file.png") +``` + +## How Nougat Works + +Nougat employs a vision encoder-decoder model, along with a dedicated processor, to transcribe PDFs into Markdown format and perform information and metadata extraction. Here's how it works: + +1. **Initialization**: When you create a Nougat instance, you can specify the model to use, the minimum transcription length, and the maximum number of new tokens to generate. + +2. **Processing PDFs**: Nougat can process PDFs as input. You can provide the path to a PDF document. + +3. **Image Processing**: The processor converts PDF pages into images, which are then encoded by the model. + +4. **Transcription**: Nougat generates Markdown transcriptions of PDF content, ensuring a minimum length and respecting the token limit. + +5. **Information Extraction**: Information extraction involves parsing the Markdown transcription to identify key details or content of interest. + +6. **Metadata Extraction**: Metadata extraction involves identifying and extracting document metadata, such as title, author, and publication date. + +## Additional Information + +- Nougat leverages the "facebook/nougat-base" pretrained model, which is specifically designed for document transcription and extraction tasks. +- You can adjust the minimum transcription length and the maximum number of new tokens to control the output's length and quality. +- Nougat can be run on both CPU and GPU devices. + +That concludes the documentation for Nougat. We hope you find this tool valuable for your PDF transcription, information extraction, and metadata extraction needs. If you have any questions or encounter any issues, please refer to the Nougat documentation for further assistance. Enjoy using Nougat! \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 3446f2c0..24dda79d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -100,6 +100,7 @@ nav: - Idefics: "swarms/models/idefics.md" - BingChat: "swarms/models/bingchat.md" - Kosmos: "swarms/models/kosmos.md" + - Nougat: "swarms/models/nougat.md" - swarms.structs: - Overview: "swarms/structs/overview.md" - Workflow: "swarms/structs/workflow.md" diff --git a/pyproject.toml b/pyproject.toml index 96a337fd..a17c6fa0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,12 +36,15 @@ langchain-experimental = "*" playwright = "*" duckduckgo-search = "*" faiss-cpu = "*" +datasets = "*" diffusers = "*" +sentencepiece = "*" wget = "*" griptape = "*" httpx = "*" ggl = "*" beautifulsoup4 = "*" +huggingface-hub = "*" pydantic = "*" tenacity = "*" redis = "*" diff --git a/requirements.txt b/requirements.txt index c92b868c..b2c28df0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -18,7 +18,10 @@ google-search-results==2.4.2 Pillow faiss-cpu openai +datasets +huggingface-hub google-generativeai +sentencepiece duckduckgo-search agent-protocol chromadb diff --git a/swarms/__init__.py b/swarms/__init__.py index e1dba262..a93fed25 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -8,14 +8,14 @@ warnings.filterwarnings("ignore", category=UserWarning) os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" - - from swarms import workers from swarms.workers.worker import Worker + # from swarms import chunkers -from swarms import models +from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py from swarms import structs from swarms import swarms from swarms import agents from swarms.logo import logo -print(logo) \ No newline at end of file + +print(logo) diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index fe66dee8..e9aba679 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -10,4 +10,23 @@ from swarms.models.zephyr import Zephyr from swarms.models.idefics import Idefics from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt -# from swarms.models.fuyu import Fuyu \ No newline at end of file +from swarms.models.nougat import Nougat +# from swarms.models.fuyu import Fuyu # Not working, wait until they update + + +__all__ = [ + "Anthropic", + "Petals", + "Mistral", + "OpenAI", + "AzureOpenAI", + "OpenAIChat", + "Zephyr", + "Idefics", + "Kosmos", + "Vilt", + "Nougat", +] + + + diff --git a/swarms/models/anthropic.py b/swarms/models/anthropic.py index 453890b9..232ff647 100644 --- a/swarms/models/anthropic.py +++ b/swarms/models/anthropic.py @@ -4,13 +4,13 @@ import os class Anthropic: """ - + Anthropic large language models. - - + + Args: - - + + """ def __init__( diff --git a/swarms/models/base.py b/swarms/models/base.py index 57045165..d2b01695 100644 --- a/swarms/models/base.py +++ b/swarms/models/base.py @@ -1,14 +1,17 @@ import time from abc import ABC, abstractmethod + def count_tokens(text: str) -> int: return len(text.split()) + class AbstractModel(ABC): """ AbstractModel """ + # abstract base class for language models def __init__(self): self.start_time = None @@ -41,7 +44,7 @@ class AbstractModel(ABC): if elapsed_time == 0: return float("inf") return self._num_tokens() / elapsed_time - + def _num_tokens(self, text: str) -> int: """Number of tokens""" return count_tokens(text) diff --git a/swarms/models/bing_chat.py b/swarms/models/bing_chat.py index c91690e5..1d2eb503 100644 --- a/swarms/models/bing_chat.py +++ b/swarms/models/bing_chat.py @@ -29,14 +29,22 @@ class BingChat: self.cookies = json.loads(open(cookies_path, encoding="utf-8").read()) self.bot = asyncio.run(Chatbot.create(cookies=self.cookies)) - def __call__(self, prompt: str, style: ConversationStyle = ConversationStyle.creative) -> str: + def __call__( + self, prompt: str, style: ConversationStyle = ConversationStyle.creative + ) -> str: """ Get a text response using the EdgeGPT model based on the provided prompt. """ - response = asyncio.run(self.bot.ask(prompt=prompt, conversation_style=style, simplify_response=True)) - return response['text'] + response = asyncio.run( + self.bot.ask( + prompt=prompt, conversation_style=style, simplify_response=True + ) + ) + return response["text"] - def create_img(self, prompt: str, output_dir: str = "./output", auth_cookie: str = None) -> str: + def create_img( + self, prompt: str, output_dir: str = "./output", auth_cookie: str = None + ) -> str: """ Generate an image based on the provided prompt and save it in the given output directory. Returns the path of the generated image. @@ -48,7 +56,7 @@ class BingChat: images = image_generator.get_images(prompt) image_generator.save_images(images, output_dir=output_dir) - return Path(output_dir) / images[0]['path'] + return Path(output_dir) / images[0]["path"] @staticmethod def set_cookie_dir_path(path: str): diff --git a/swarms/models/idefics.py b/swarms/models/idefics.py index 747def16..3e8227c2 100644 --- a/swarms/models/idefics.py +++ b/swarms/models/idefics.py @@ -87,7 +87,7 @@ class Idefics: prompts : list A list of prompts. Each prompt is a list of text strings and images. batched_mode : bool, optional - Whether to process the prompts in batched mode. If True, all prompts are + Whether to process the prompts in batched mode. If True, all prompts are processed together. If False, only the first prompt is processed (default is True). Returns @@ -131,8 +131,8 @@ class Idefics: prompts : list A list of prompts. Each prompt is a list of text strings and images. batched_mode : bool, optional - Whether to process the prompts in batched mode. - If True, all prompts are processed together. + Whether to process the prompts in batched mode. + If True, all prompts are processed together. If False, only the first prompt is processed (default is True). Returns diff --git a/swarms/models/kosmos_two.py b/swarms/models/kosmos_two.py index 91118c77..b36affcb 100644 --- a/swarms/models/kosmos_two.py +++ b/swarms/models/kosmos_two.py @@ -20,7 +20,7 @@ class Kosmos: """ Args: - + # Initialize Kosmos diff --git a/swarms/models/nougat.py b/swarms/models/nougat.py new file mode 100644 index 00000000..4bd99f1a --- /dev/null +++ b/swarms/models/nougat.py @@ -0,0 +1,68 @@ +""" +Nougat by Meta + +Good for: +- transcribe Scientific PDFs into an easy to use markdown +format +- Extracting information from PDFs +- Extracting metadata from pdfs + +""" + +import torch +from PIL import Image +from transformers import NougatProcessor, VisionEncoderDecoderModel + + +class Nougat: + """ + Nougat + + ArgsS: + model_name_or_path: str, default="facebook/nougat-base" + min_length: int, default=1 + max_new_tokens: int, default=30 + + Usage: + >>> from swarms.models.nougat import Nougat + >>> nougat = Nougat() + >>> nougat("path/to/image.png") + + + """ + def __init__( + self, + model_name_or_path="facebook/nougat-base", + min_length: int = 1, + max_new_tokens: int = 30, + ): + self.model_name_or_path = model_name_or_path + self.min_length = min_length + self.max_new_tokens = max_new_tokens + + self.processor = NougatProcessor.from_pretrained(self.model_name_or_path) + self.model = VisionEncoderDecoderModel.from_pretrained(self.model_name_or_path) + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.model.to(self.device) + + def get_image(self, img_path: str): + """Get an image from a path""" + image = Image.open(img_path) + return image + + def __call__(self, img_path: str): + """Call the model with an image_path str as an input""" + image = Image.open(img_path) + pixel_values = self.processor(image, return_tensors="pt").pixel_values + + # Generate transcriptions, here we only generate 30 tokens + outputs = self.model.generate( + pixel_values.to(self.device), + min_length=self.min_length, + max_new_tokens=self.max_new_tokens, + bad_words_ids=[[self.processor.unk_token - id]], + ) + + sequence = self.processor.batch_decode(outputs, skip_special_tokens=True)[0] + sequence = self.processor.post_process_generation(sequence, fix_markdown=False) + return sequence diff --git a/swarms/models/phi.py b/swarms/models/phi.py new file mode 100644 index 00000000..90fca08e --- /dev/null +++ b/swarms/models/phi.py @@ -0,0 +1 @@ +"""Phi by Microsoft written by Kye""" diff --git a/swarms/models/trocr.py b/swarms/models/trocr.py new file mode 100644 index 00000000..f4a4156d --- /dev/null +++ b/swarms/models/trocr.py @@ -0,0 +1,19 @@ +""" + +TROCR for Multi-Modal OCR tasks + + +""" +from transformers import TrOCRProcessor, VisionEncoderDecoderModel +from PIL import Image +import requests + + +class TrOCR: + def __init__( + self, + ): + pass + + def __call__(self): + pass diff --git a/swarms/models/zephyr.py b/swarms/models/zephyr.py index 8ee12ed9..582bc740 100644 --- a/swarms/models/zephyr.py +++ b/swarms/models/zephyr.py @@ -1,16 +1,15 @@ """Zephyr by HF""" -import torch +import torch from transformers import pipeline - class Zephyr: """ Zehpyr model from HF Args: - max_new_tokens(int) = Number of max new tokens + max_new_tokens(int) = Number of max new tokens temperature(float) = temperature of the LLM top_k(float) = top k of the model set to 50 top_p(float) = top_p of the model set to 0.95 @@ -23,6 +22,7 @@ class Zephyr: """ + def __init__( self, max_new_tokens: int = 300, @@ -40,18 +40,23 @@ class Zephyr: "text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloa16, - device_map="auto" + device_map="auto", ) self.messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, - {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, + { + "role": "user", + "content": "How many helicopters can a human eat in one sitting?", + }, ] def __call__(self, text: str): """Call the model""" - prompt = self.pipe.tokenizer.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True) + prompt = self.pipe.tokenizer.apply_chat_template( + self.messages, tokenize=False, add_generation_prompt=True + ) outputs = self.pipe(prompt, max_new_token=self.max_new_tokens) - print(outputs[0])["generated_text"] \ No newline at end of file + print(outputs[0])["generated_text"] diff --git a/swarms/tools/edge_gpt.py b/swarms/tools/edge_gpt.py index bef44cfb..9e8eac42 100644 --- a/swarms/tools/edge_gpt.py +++ b/swarms/tools/edge_gpt.py @@ -1,9 +1,15 @@ from swarms.tools.tool import BaseTool + class EdgeGPTTool(BaseTool): - def __init__(self, model, name="EdgeGPTTool", description="Tool that uses EdgeGPTModel to generate responses"): + def __init__( + self, + model, + name="EdgeGPTTool", + description="Tool that uses EdgeGPTModel to generate responses", + ): super().__init__(name=name, description=description) self.model = model def _run(self, prompt): - return self.model.__call__(prompt) \ No newline at end of file + return self.model.__call__(prompt) From 92e9191e4f5aed977fd8d867bab2251e1a05deb9 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 20 Oct 2023 03:47:46 -0400 Subject: [PATCH 02/19] layout llm Former-commit-id: 06a164ac5d87b398ffa78ad69700ba0bbab21ada --- docs/swarms/models/kosmos.md | 2 +- docs/swarms/models/layoutlm_document_qa.md | 88 ++++++++++++++++++++++ docs/swarms/models/nougat.md | 2 +- docs/swarms/models/openai_chat.md | 2 +- swarms/models/__init__.py | 2 + swarms/models/layoutlm_document_qa.py | 37 +++++++++ 6 files changed, 130 insertions(+), 3 deletions(-) create mode 100644 docs/swarms/models/layoutlm_document_qa.md create mode 100644 swarms/models/layoutlm_document_qa.py diff --git a/docs/swarms/models/kosmos.md b/docs/swarms/models/kosmos.md index 81e3ffd2..1735e153 100644 --- a/docs/swarms/models/kosmos.md +++ b/docs/swarms/models/kosmos.md @@ -1,4 +1,4 @@ -# Kosmos Documentation +# `Kosmos` Documentation ## Introduction diff --git a/docs/swarms/models/layoutlm_document_qa.md b/docs/swarms/models/layoutlm_document_qa.md new file mode 100644 index 00000000..4c6169d0 --- /dev/null +++ b/docs/swarms/models/layoutlm_document_qa.md @@ -0,0 +1,88 @@ +# `LayoutLMDocumentQA` Documentation + +## Introduction + +Welcome to the documentation for LayoutLMDocumentQA, a multimodal model designed for visual question answering (QA) on real-world documents, such as invoices, PDFs, and more. This comprehensive documentation will provide you with a deep understanding of the LayoutLMDocumentQA class, its architecture, usage, and examples. + +## Overview + +LayoutLMDocumentQA is a versatile model that combines layout-based understanding of documents with natural language processing to answer questions about the content of documents. It is particularly useful for automating tasks like invoice processing, extracting information from PDFs, and handling various document-based QA scenarios. + +## Class Definition + +```python +class LayoutLMDocumentQA(AbstractModel): + def __init__( + self, + model_name: str = "impira/layoutlm-document-qa", + task: str = "document-question-answering", + ): +``` + +## Purpose + +The LayoutLMDocumentQA class serves the following primary purposes: + +1. **Document QA**: LayoutLMDocumentQA is specifically designed for document-based question answering. It can process both the textual content and the layout of a document to answer questions. + +2. **Multimodal Understanding**: It combines natural language understanding with document layout analysis, making it suitable for documents with complex structures. + +## Parameters + +- `model_name` (str): The name or path of the pretrained LayoutLMDocumentQA model. Default: "impira/layoutlm-document-qa". +- `task` (str): The specific task for which the model will be used. Default: "document-question-answering". + +## Usage + +To use LayoutLMDocumentQA, follow these steps: + +1. Initialize the LayoutLMDocumentQA instance: + +```python +from swarms.models import LayoutLMDocumentQA + +layout_lm_doc_qa = LayoutLMDocumentQA() +``` + +### Example 1 - Initialization + +```python +layout_lm_doc_qa = LayoutLMDocumentQA() +``` + +2. Ask a question about a document and provide the document's image path: + +```python +question = "What is the total amount?" +image_path = "path/to/document_image.png" +answer = layout_lm_doc_qa(question, image_path) +``` + +### Example 2 - Document QA + +```python +layout_lm_doc_qa = LayoutLMDocumentQA() +question = "What is the total amount?" +image_path = "path/to/document_image.png" +answer = layout_lm_doc_qa(question, image_path) +``` + +## How LayoutLMDocumentQA Works + +LayoutLMDocumentQA employs a multimodal approach to document QA. Here's how it works: + +1. **Initialization**: When you create a LayoutLMDocumentQA instance, you can specify the model to use and the task, which is "document-question-answering" by default. + +2. **Question and Document**: You provide a question about the document and the image path of the document to the LayoutLMDocumentQA instance. + +3. **Multimodal Processing**: LayoutLMDocumentQA processes both the question and the document image. It combines layout-based analysis with natural language understanding. + +4. **Answer Generation**: The model generates an answer to the question based on its analysis of the document layout and content. + +## Additional Information + +- LayoutLMDocumentQA uses the "impira/layoutlm-document-qa" pretrained model, which is specifically designed for document-based question answering. +- You can adapt this model to various document QA scenarios by changing the task and providing relevant questions and documents. +- This model is particularly useful for automating document-based tasks and extracting valuable information from structured documents. + +That concludes the documentation for LayoutLMDocumentQA. We hope you find this tool valuable for your document-based question answering needs. If you have any questions or encounter any issues, please refer to the LayoutLMDocumentQA documentation for further assistance. Enjoy using LayoutLMDocumentQA! \ No newline at end of file diff --git a/docs/swarms/models/nougat.md b/docs/swarms/models/nougat.md index 88945b5b..217990a1 100644 --- a/docs/swarms/models/nougat.md +++ b/docs/swarms/models/nougat.md @@ -1,4 +1,4 @@ -# Nougat Documentation +# `Nougat` Documentation ## Introduction diff --git a/docs/swarms/models/openai_chat.md b/docs/swarms/models/openai_chat.md index 4bb3ba78..a2ef9811 100644 --- a/docs/swarms/models/openai_chat.md +++ b/docs/swarms/models/openai_chat.md @@ -1,4 +1,4 @@ -# `OpenAIChat`` Documentation +# `OpenAIChat` Documentation ## Table of Contents diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index e9aba679..c5b787e5 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -11,6 +11,7 @@ from swarms.models.idefics import Idefics from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt from swarms.models.nougat import Nougat +from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA # from swarms.models.fuyu import Fuyu # Not working, wait until they update @@ -26,6 +27,7 @@ __all__ = [ "Kosmos", "Vilt", "Nougat", + "LayoutLMDocumentQA", ] diff --git a/swarms/models/layoutlm_document_qa.py b/swarms/models/layoutlm_document_qa.py new file mode 100644 index 00000000..a5f7a32c --- /dev/null +++ b/swarms/models/layoutlm_document_qa.py @@ -0,0 +1,37 @@ +""" +LayoutLMDocumentQA is a multimodal good for +visual question answering on real world docs lik invoice, pdfs, etc +""" +from transformers import pipeline +from swarms.models.base import AbstractModel + +class LayoutLMDocumentQA(AbstractModel): + """ + LayoutLMDocumentQA for document question answering: + + Args: + model_name (str, optional): [description]. Defaults to "impira/layoutlm-document-qa". + task (str, optional): [description]. Defaults to "document-question-answering". + + Usage: + >>> from swarms.models import LayoutLMDocumentQA + >>> model = LayoutLMDocumentQA() + >>> out = model("What is the total amount?", "path/to/img.png") + >>> print(out) + + """ + def __init__( + self, + model_name: str = "impira/layoutlm-document-qa", + task: str = "document-question-answering", + ): + self.pipeline = pipeline( + self.task, + model=self.model_name + ) + + def __call__(self, task: str, img_path: str): + """Call for model""" + out = self.pipeline(img_path, task) + out = str(out) + return out \ No newline at end of file From a2d2f96decd0d54a8e044212b5a2e44dedf4beb2 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 20 Oct 2023 04:03:57 -0400 Subject: [PATCH 03/19] layout Former-commit-id: 23be926f7a25b8b597ef1b47c672d9404a437ba6 --- mkdocs.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mkdocs.yml b/mkdocs.yml index 24dda79d..6b765d09 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -101,6 +101,7 @@ nav: - BingChat: "swarms/models/bingchat.md" - Kosmos: "swarms/models/kosmos.md" - Nougat: "swarms/models/nougat.md" + - LayoutLMDocumentQA: "swarms/models/layoutlm_document_qa.md" - swarms.structs: - Overview: "swarms/structs/overview.md" - Workflow: "swarms/structs/workflow.md" From 12c556078dc6755b365a6aed0b3b320c3e192daf Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 20 Oct 2023 11:49:51 -0400 Subject: [PATCH 04/19] documentation fixed Former-commit-id: f9d0ac5448291d2d3e64759c0a6b1efb703ab96d --- mkdocs.yml | 26 ++++++++++++++------------ swarms/models/base.py | 13 +++++++++++++ 2 files changed, 27 insertions(+), 12 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 6b765d09..fb0fb5f3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -90,18 +90,20 @@ nav: - OmniModalAgent: "swarms/agents/omni_agent.md" - Idea2Image: "swarms/agents/idea_to_image.md" - swarms.models: - - Overview: "swarms/models/index.md" - - HuggingFaceLLM: "swarms/models/hf.md" - - Anthropic: "swarms/models/anthropic.md" - - OpenAI: "swarms/models/openai.md" - - Fuyu: "swarms/models/fuyu.md" - - Zephyr: "swarms/models/zephyr.md" - - Vilt: "swarms/models/vilt.md" - - Idefics: "swarms/models/idefics.md" - - BingChat: "swarms/models/bingchat.md" - - Kosmos: "swarms/models/kosmos.md" - - Nougat: "swarms/models/nougat.md" - - LayoutLMDocumentQA: "swarms/models/layoutlm_document_qa.md" + - Language: + - Overview: "swarms/models/index.md" + - HuggingFaceLLM: "swarms/models/hf.md" + - Anthropic: "swarms/models/anthropic.md" + - OpenAI: "swarms/models/openai.md" + - Zephyr: "swarms/models/zephyr.md" + - MultiModal: + - Fuyu: "swarms/models/fuyu.md" + - Vilt: "swarms/models/vilt.md" + - Idefics: "swarms/models/idefics.md" + - BingChat: "swarms/models/bingchat.md" + - Kosmos: "swarms/models/kosmos.md" + - Nougat: "swarms/models/nougat.md" + - LayoutLMDocumentQA: "swarms/models/layoutlm_document_qa.md" - swarms.structs: - Overview: "swarms/structs/overview.md" - Workflow: "swarms/structs/workflow.md" diff --git a/swarms/models/base.py b/swarms/models/base.py index d2b01695..6f0e9ecb 100644 --- a/swarms/models/base.py +++ b/swarms/models/base.py @@ -78,3 +78,16 @@ class AbstractModel(ABC): if self.start_time and self.end_time: return self.end_time - self.start_time return 0 + + def metrics(self) -> str: + _sec_to_first_token = self._sec_to_first_token() + _tokens_per_second = self._tokens_per_second() + _num_tokens = self._num_tokens(self.history) + _time_for_generation = self._time_for_generation(self.history) + + return f""" + SEC TO FIRST TOKEN: {_sec_to_first_token} + TOKENS/SEC: {_tokens_per_second} + TOKENS: {_num_tokens} + Tokens/SEC: {_time_for_generation} + """ \ No newline at end of file From 5c06f7a9e7748a76c671711f5324a21f52225538 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 20 Oct 2023 13:06:11 -0400 Subject: [PATCH 05/19] bioclip Former-commit-id: 38dd390e08aafccf12b95ac3c5cf98e9f1ce1f0c --- pyproject.toml | 1 + requirements.txt | 1 + swarms/__init__.py | 2 +- swarms/models/__init__.py | 4 +- swarms/models/base.py | 4 +- swarms/models/bioclip.py | 170 ++++++++++++++++++++++++++ swarms/models/layoutlm_document_qa.py | 15 ++- swarms/models/nougat.py | 3 +- 8 files changed, 185 insertions(+), 15 deletions(-) create mode 100644 swarms/models/bioclip.py diff --git a/pyproject.toml b/pyproject.toml index a17c6fa0..b06d4b93 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,6 +55,7 @@ open-interpreter = "*" tabulate = "*" termcolor = "*" black = "*" +open_clip_torch = "*" dalle3 = "*" torchvision = "*" rich = "*" diff --git a/requirements.txt b/requirements.txt index b2c28df0..23331763 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,7 @@ wget==3.2 simpleaichat httpx torch +open_clip_torch ggl beautifulsoup4 google-search-results==2.4.2 diff --git a/swarms/__init__.py b/swarms/__init__.py index a93fed25..2ecf2033 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -12,7 +12,7 @@ from swarms import workers from swarms.workers.worker import Worker # from swarms import chunkers -from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py +from swarms.models import * # import * only works when __all__ = [] is defined in __init__.py from swarms import structs from swarms import swarms from swarms import agents diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index c5b787e5..20ff2dfe 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -12,6 +12,7 @@ from swarms.models.kosmos_two import Kosmos from swarms.models.vilt import Vilt from swarms.models.nougat import Nougat from swarms.models.layoutlm_document_qa import LayoutLMDocumentQA + # from swarms.models.fuyu import Fuyu # Not working, wait until they update @@ -29,6 +30,3 @@ __all__ = [ "Nougat", "LayoutLMDocumentQA", ] - - - diff --git a/swarms/models/base.py b/swarms/models/base.py index 6f0e9ecb..32a45c43 100644 --- a/swarms/models/base.py +++ b/swarms/models/base.py @@ -84,10 +84,10 @@ class AbstractModel(ABC): _tokens_per_second = self._tokens_per_second() _num_tokens = self._num_tokens(self.history) _time_for_generation = self._time_for_generation(self.history) - + return f""" SEC TO FIRST TOKEN: {_sec_to_first_token} TOKENS/SEC: {_tokens_per_second} TOKENS: {_num_tokens} Tokens/SEC: {_time_for_generation} - """ \ No newline at end of file + """ diff --git a/swarms/models/bioclip.py b/swarms/models/bioclip.py new file mode 100644 index 00000000..937634e3 --- /dev/null +++ b/swarms/models/bioclip.py @@ -0,0 +1,170 @@ +""" + + +BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 +https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 +BiomedCLIP is a biomedical vision-language foundation model that is pretrained on PMC-15M, +a dataset of 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central, using contrastive learning. It uses PubMedBERT as the text encoder and Vision Transformer as the image encoder, with domain-specific adaptations. It can perform various vision-language processing (VLP) tasks such as cross-modal retrieval, image classification, and visual question answering. BiomedCLIP establishes new state of the art in a wide range of standard datasets, and substantially outperforms prior VLP approaches: + + + +Citation +@misc{https://doi.org/10.48550/arXiv.2303.00915, + doi = {10.48550/ARXIV.2303.00915}, + url = {https://arxiv.org/abs/2303.00915}, + author = {Zhang, Sheng and Xu, Yanbo and Usuyama, Naoto and Bagga, Jaspreet and Tinn, Robert and Preston, Sam and Rao, Rajesh and Wei, Mu and Valluri, Naveen and Wong, Cliff and Lungren, Matthew and Naumann, Tristan and Poon, Hoifung}, + title = {Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing}, + publisher = {arXiv}, + year = {2023}, +} + +Model Use +How to use +Please refer to this example notebook. + +Intended Use +This model is intended to be used solely for (I) future research on visual-language processing and (II) reproducibility of the experimental results reported in the reference paper. + +Primary Intended Use +The primary intended use is to support AI researchers building on top of this work. BiomedCLIP and its associated models should be helpful for exploring various biomedical VLP research questions, especially in the radiology domain. + +Out-of-Scope Use +Any deployed use case of the model --- commercial or otherwise --- is currently out of scope. Although we evaluated the models using a broad set of publicly-available research benchmarks, the models and evaluations are not intended for deployed use cases. Please refer to the associated paper for more details. + +Data +This model builds upon PMC-15M dataset, which is a large-scale parallel image-text dataset for biomedical vision-language processing. It contains 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central. It covers a diverse range of biomedical image types, such as microscopy, radiography, histology, and more. + +Limitations +This model was developed using English corpora, and thus can be considered English-only. + +Further information +Please refer to the corresponding paper, "Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing" for additional details on the model training and evaluation. +""" + +import open_clip +import glob +import torch +from PIL import Image +import matplotlib.pyplot as plt + + +class BioClip: + """ + BioClip + + Args: + model_path (str): path to the model + + Attributes: + model_path (str): path to the model + model (torch.nn.Module): the model + preprocess_train (torchvision.transforms.Compose): the preprocessing pipeline for training + preprocess_val (torchvision.transforms.Compose): the preprocessing pipeline for validation + tokenizer (open_clip.Tokenizer): the tokenizer + device (torch.device): the device to run the model on + + Methods: + __call__(self, img_path: str, labels: list, template: str = 'this is a photo of ', context_length: int = 256): + returns a dictionary of labels and their probabilities + plot_image_with_metadata(img_path: str, metadata: dict): plots the image with the metadata + + Usage: + clip = BioClip('hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224') + + labels = [ + 'adenocarcinoma histopathology', + 'brain MRI', + 'covid line chart', + 'squamous cell carcinoma histopathology', + 'immunohistochemistry histopathology', + 'bone X-ray', + 'chest X-ray', + 'pie chart', + 'hematoxylin and eosin histopathology' + ] + + result = clip("your_image_path.jpg", labels) + metadata = {'filename': "your_image_path.jpg".split('/')[-1], 'top_probs': result} + clip.plot_image_with_metadata("your_image_path.jpg", metadata) + + + """ + + def __init__(self, model_path: str): + self.model_path = model_path + ( + self.model, + self.preprocess_train, + self.preprocess_val, + ) = open_clip.create_model_and_transforms(model_path) + self.tokenizer = open_clip.get_tokenizer(model_path) + self.device = ( + torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + ) + self.model.to(self.device) + self.model.eval() + + def __call__( + self, + img_path: str, + labels: list, + template: str = "this is a photo of ", + context_length: int = 256, + ): + image = torch.stack([self.preprocess_val(Image.open(img_path))]).to(self.device) + texts = self.tokenizer( + [template + l for l in labels], context_length=context_length + ).to(self.device) + + with torch.no_grad(): + image_features, text_features, logit_scale = self.model(image, texts) + logits = ( + (logit_scale * image_features @ text_features.t()) + .detach() + .softmax(dim=-1) + ) + sorted_indices = torch.argsort(logits, dim=-1, descending=True) + logits = logits.cpu().numpy() + sorted_indices = sorted_indices.cpu().numpy() + + results = {} + for idx in sorted_indices[0]: + label = labels[idx] + prob = logits[0][idx] + results[label] = prob + return results + + @staticmethod + def plot_image_with_metadata(img_path: str, metadata: dict): + img = Image.open(img_path) + fig, ax = plt.subplots(figsize=(5, 5)) + ax.imshow(img) + ax.axis("off") + title = ( + metadata["filename"] + + "\n" + + "\n".join([f"{k}: {v*100:.1f}" for k, v in metadata["top_probs"].items()]) + ) + ax.set_title(title, fontsize=14) + plt.tight_layout() + plt.show() + + +# Usage +# clip = BioClip('hf-hub:microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224') + +# labels = [ +# 'adenocarcinoma histopathology', +# 'brain MRI', +# 'covid line chart', +# 'squamous cell carcinoma histopathology', +# 'immunohistochemistry histopathology', +# 'bone X-ray', +# 'chest X-ray', +# 'pie chart', +# 'hematoxylin and eosin histopathology' +# ] + +# result = clip("your_image_path.jpg", labels) +# metadata = {'filename': "your_image_path.jpg".split('/')[-1], 'top_probs': result} +# clip.plot_image_with_metadata("your_image_path.jpg", metadata) diff --git a/swarms/models/layoutlm_document_qa.py b/swarms/models/layoutlm_document_qa.py index a5f7a32c..6fe83210 100644 --- a/swarms/models/layoutlm_document_qa.py +++ b/swarms/models/layoutlm_document_qa.py @@ -5,6 +5,7 @@ visual question answering on real world docs lik invoice, pdfs, etc from transformers import pipeline from swarms.models.base import AbstractModel + class LayoutLMDocumentQA(AbstractModel): """ LayoutLMDocumentQA for document question answering: @@ -18,20 +19,18 @@ class LayoutLMDocumentQA(AbstractModel): >>> model = LayoutLMDocumentQA() >>> out = model("What is the total amount?", "path/to/img.png") >>> print(out) - + """ + def __init__( - self, + self, model_name: str = "impira/layoutlm-document-qa", task: str = "document-question-answering", ): - self.pipeline = pipeline( - self.task, - model=self.model_name - ) - + self.pipeline = pipeline(self.task, model=self.model_name) + def __call__(self, task: str, img_path: str): """Call for model""" out = self.pipeline(img_path, task) out = str(out) - return out \ No newline at end of file + return out diff --git a/swarms/models/nougat.py b/swarms/models/nougat.py index 4bd99f1a..cc154283 100644 --- a/swarms/models/nougat.py +++ b/swarms/models/nougat.py @@ -28,8 +28,9 @@ class Nougat: >>> nougat = Nougat() >>> nougat("path/to/image.png") - + """ + def __init__( self, model_name_or_path="facebook/nougat-base", From 55449c89347ab65568a3fee1d6d27b1bb4ecffbc Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 20 Oct 2023 13:43:35 -0400 Subject: [PATCH 06/19] biogpt + speecht5 Former-commit-id: 95a85a741cd9bf1fbfc341c0c170caa2badfb04f --- docs/swarms/models/biogpt.md | 157 ++++++++++++++++++++++++++ mkdocs.yml | 1 + pyproject.toml | 1 + requirements.txt | 1 + swarms/models/__init__.py | 2 +- swarms/models/biogpt.py | 208 +++++++++++++++++++++++++++++++++++ swarms/models/speecht5.py | 159 ++++++++++++++++++++++++++ 7 files changed, 528 insertions(+), 1 deletion(-) create mode 100644 docs/swarms/models/biogpt.md create mode 100644 swarms/models/biogpt.py create mode 100644 swarms/models/speecht5.py diff --git a/docs/swarms/models/biogpt.md b/docs/swarms/models/biogpt.md new file mode 100644 index 00000000..291b917c --- /dev/null +++ b/docs/swarms/models/biogpt.md @@ -0,0 +1,157 @@ +# `BioGPT` Documentation + +## Table of Contents +1. [Introduction](#introduction) +2. [Overview](#overview) +3. [Installation](#installation) +4. [Usage](#usage) + 1. [BioGPT Class](#biogpt-class) + 2. [Examples](#examples) +5. [Additional Information](#additional-information) +6. [Conclusion](#conclusion) + +--- + +## 1. Introduction + +The `BioGPT` module is a domain-specific generative language model designed for the biomedical domain. It is built upon the powerful Transformer architecture and pretrained on a large corpus of biomedical literature. This documentation provides an extensive guide on using the `BioGPT` module, explaining its purpose, parameters, and usage. + +--- + +## 2. Overview + +The `BioGPT` module addresses the need for a language model specialized in the biomedical domain. Unlike general-purpose language models, `BioGPT` excels in generating coherent and contextually relevant text specific to biomedical terms and concepts. It has been evaluated on various biomedical natural language processing tasks and has demonstrated superior performance. + +Key features and parameters of the `BioGPT` module include: +- `model_name`: Name of the pretrained model. +- `max_length`: Maximum length of generated text. +- `num_return_sequences`: Number of sequences to return. +- `do_sample`: Whether to use sampling in generation. +- `min_length`: Minimum length of generated text. + +The `BioGPT` module is equipped with features for generating text, extracting features, and more. + +--- + +## 3. Installation + +Before using the `BioGPT` module, ensure you have the required dependencies installed, including the Transformers library and Torch. You can install these dependencies using pip: + +```bash +pip install transformers +pip install torch +``` + +--- + +## 4. Usage + +In this section, we'll cover how to use the `BioGPT` module effectively. It consists of the `BioGPT` class and provides examples to demonstrate its usage. + +### 4.1. `BioGPT` Class + +The `BioGPT` class is the core component of the `BioGPT` module. It is used to create a `BioGPT` instance, which can generate text, extract features, and more. + +#### Parameters: +- `model_name` (str): Name of the pretrained model. +- `max_length` (int): Maximum length of generated text. +- `num_return_sequences` (int): Number of sequences to return. +- `do_sample` (bool): Whether or not to use sampling in generation. +- `min_length` (int): Minimum length of generated text. + +### 4.2. Examples + +Let's explore how to use the `BioGPT` class with different scenarios and applications. + +#### Example 1: Generating Biomedical Text + +```python +from biogpt import BioGPT + +# Initialize the BioGPT model +biogpt = BioGPT() + +# Generate biomedical text +input_text = "The patient has a fever" +generated_text = biogpt(input_text) + +print(generated_text) +``` + +#### Example 2: Extracting Features + +```python +from biogpt import BioGPT + +# Initialize the BioGPT model +biogpt = BioGPT() + +# Extract features from a biomedical text +input_text = "The patient has a fever" +features = biogpt.get_features(input_text) + +print(features) +``` + +#### Example 3: Using Beam Search Decoding + +```python +from biogpt import BioGPT + +# Initialize the BioGPT model +biogpt = BioGPT() + +# Generate biomedical text using beam search decoding +input_text = "The patient has a fever" +generated_text = biogpt.beam_search_decoding(input_text) + +print(generated_text) +``` + +### 4.3. Additional Features + +The `BioGPT` class also provides additional features: + +#### Set a New Pretrained Model +```python +biogpt.set_pretrained_model("new_pretrained_model") +``` + +#### Get the Model's Configuration +```python +config = biogpt.get_config() +print(config) +``` + +#### Save and Load the Model +```python +# Save the model and tokenizer to a directory +biogpt.save_model("saved_model") + +# Load a model and tokenizer from a directory +biogpt.load_from_path("saved_model") +``` + +#### Print the Model's Architecture +```python +biogpt.print_model() +``` + +--- + +## 5. Additional Information + +- **Biomedical Text Generation**: The `BioGPT` module is designed specifically for generating biomedical text, making it a valuable tool for various biomedical natural language processing tasks. +- **Feature Extraction**: It also provides the capability to extract features from biomedical text. +- **Beam Search Decoding**: Beam search decoding is available for generating text with improved quality. +- **Customization**: You can set a new pretrained model and save/load models for customization. + +--- + +## 6. Conclusion + +The `BioGPT` module is a powerful and specialized tool for generating and working with biomedical text. This documentation has provided a comprehensive guide on its usage, parameters, and examples, enabling you to effectively leverage it for various biomedical natural language processing tasks. + +By using `BioGPT`, you can enhance your biomedical text generation and analysis tasks with contextually relevant and coherent text. + +*Please check the official `BioGPT` repository and documentation for any updates beyond the knowledge cutoff date.* \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index fb0fb5f3..476a4fde 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -96,6 +96,7 @@ nav: - Anthropic: "swarms/models/anthropic.md" - OpenAI: "swarms/models/openai.md" - Zephyr: "swarms/models/zephyr.md" + - BioGPT: "swarms/models/biogpt.md" - MultiModal: - Fuyu: "swarms/models/fuyu.md" - Vilt: "swarms/models/vilt.md" diff --git a/pyproject.toml b/pyproject.toml index b06d4b93..2a79a6cd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,6 +57,7 @@ termcolor = "*" black = "*" open_clip_torch = "*" dalle3 = "*" +soundfile = "*" torchvision = "*" rich = "*" EdgeGPT = "*" diff --git a/requirements.txt b/requirements.txt index 23331763..c210f75b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -20,6 +20,7 @@ Pillow faiss-cpu openai datasets +soundfile huggingface-hub google-generativeai sentencepiece diff --git a/swarms/models/__init__.py b/swarms/models/__init__.py index 20ff2dfe..b1f8ee73 100644 --- a/swarms/models/__init__.py +++ b/swarms/models/__init__.py @@ -4,7 +4,7 @@ from swarms.models.petals import Petals from swarms.models.mistral import Mistral from swarms.models.openai_models import OpenAI, AzureOpenAI, OpenAIChat from swarms.models.zephyr import Zephyr - +from swarms.models.biogpt import BioGPT # MultiModal Models from swarms.models.idefics import Idefics diff --git a/swarms/models/biogpt.py b/swarms/models/biogpt.py new file mode 100644 index 00000000..f5abdf95 --- /dev/null +++ b/swarms/models/biogpt.py @@ -0,0 +1,208 @@ +""" +BioGPT +Pre-trained language models have attracted increasing attention in the biomedical domain, +inspired by their great success in the general natural language domain. +Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), +the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. +While they have achieved great success on a variety of discriminative downstream biomedical tasks, +the lack of generation ability constrains their application scope. +In this paper, we propose BioGPT, a domain-specific generative Transformer language model +pre-trained on large-scale biomedical literature. +We evaluate BioGPT on six biomedical natural language processing tasks +and demonstrate that our model outperforms previous models on most tasks. +Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI +end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, +creating a new record. Our case study on text generation further demonstrates the +advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms. + + +@article{10.1093/bib/bbac409, + author = {Luo, Renqian and Sun, Liai and Xia, Yingce and Qin, Tao and Zhang, Sheng and Poon, Hoifung and Liu, Tie-Yan}, + title = "{BioGPT: generative pre-trained transformer for biomedical text generation and mining}", + journal = {Briefings in Bioinformatics}, + volume = {23}, + number = {6}, + year = {2022}, + month = {09}, + abstract = "{Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98\%, 38.42\% and 40.76\% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2\% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.}", + issn = {1477-4054}, + doi = {10.1093/bib/bbac409}, + url = {https://doi.org/10.1093/bib/bbac409}, + note = {bbac409}, + eprint = {https://academic.oup.com/bib/article-pdf/23/6/bbac409/47144271/bbac409.pdf}, +} +""" + +import torch +from transformers import pipeline, set_seed, BioGptTokenizer, BioGptForCausalLM + + +class BioGPT: + """ + A wrapper class for the BioGptForCausalLM model from the transformers library. + + Attributes: + model_name (str): Name of the pretrained model. + model (BioGptForCausalLM): The pretrained BioGptForCausalLM model. + tokenizer (BioGptTokenizer): The tokenizer for the BioGptForCausalLM model. + + Methods: + __call__: Generate text based on the given input. + get_features: Get the features of a given text. + beam_search_decoding: Generate text using beam search decoding. + set_pretrained_model: Set a new tokenizer and model. + get_config: Get the model's configuration. + save_model: Save the model and tokenizer to a directory. + load_from_path: Load a model and tokenizer from a directory. + print_model: Print the model's architecture. + + Usage: + >>> from swarms.models.biogpt import BioGPTWrapper + >>> model = BioGPTWrapper() + >>> out = model("The patient has a fever") + >>> print(out) + + + """ + + def __init__( + self, + model_name: str = "microsoft/biogpt", + max_length: int = 500, + num_return_sequences: int = 5, + do_sample: bool = True, + min_length: int = 100, + ): + """ + Initialize the wrapper class with a model name. + + Args: + model_name (str): Name of the pretrained model. Default is "microsoft/biogpt". + """ + self.model_name = model_name + self.max_length = max_length + self.num_return_sequences = num_return_sequences + self.do_sample = do_sample + self.min_length = min_length + + self.model = BioGptForCausalLM.from_pretrained(self.model_name) + self.tokenizer = BioGptTokenizer.from_pretrained(self.model_name) + + def __call__(self, text: str): + """ + Generate text based on the given input. + + Args: + text (str): The input text to generate from. + max_length (int): Maximum length of the generated text. + num_return_sequences (int): Number of sequences to return. + do_sample (bool): Whether or not to use sampling in generation. + + Returns: + list[dict]: A list of generated texts. + """ + set_seed(42) + generator = pipeline( + "text-generation", model=self.model, tokenizer=self.tokenizer + ) + return generator( + text, + max_length=self.max_length, + num_return_sequences=self.num_return_sequences, + do_sample=self.do_sample, + ) + + def get_features(self, text): + """ + Get the features of a given text. + + Args: + text (str): Input text. + + Returns: + BaseModelOutputWithPastAndCrossAttentions: Model output. + """ + encoded_input = self.tokenizer(text, return_tensors="pt") + return self.model(**encoded_input) + + def beam_search_decoding( + self, + sentence, + num_beams=5, + early_stopping=True, + ): + """ + Generate text using beam search decoding. + + Args: + sentence (str): The input sentence to generate from. + min_length (int): Minimum length of the generated text. + max_length (int): Maximum length of the generated text. + num_beams (int): Number of beams for beam search. + early_stopping (bool): Whether to stop early during beam search. + + Returns: + str: The generated text. + """ + inputs = self.tokenizer(sentence, return_tensors="pt") + set_seed(42) + with torch.no_grad(): + beam_output = self.model.generate( + **inputs, + min_length=self.min_length, + max_length=self.max_length, + num_beams=num_beams, + early_stopping=early_stopping + ) + return self.tokenizer.decode(beam_output[0], skip_special_tokens=True) + + # Feature 1: Set a new tokenizer and model + def set_pretrained_model(self, model_name): + """ + Set a new tokenizer and model. + + Args: + model_name (str): Name of the pretrained model. + """ + self.model_name = model_name + self.model = BioGptForCausalLM.from_pretrained(self.model_name) + self.tokenizer = BioGptTokenizer.from_pretrained(self.model_name) + + # Feature 2: Get the model's config details + def get_config(self): + """ + Get the model's configuration. + + Returns: + PretrainedConfig: The configuration of the model. + """ + return self.model.config + + # Feature 3: Save the model and tokenizer to disk + def save_model(self, path): + """ + Save the model and tokenizer to a directory. + + Args: + path (str): Path to the directory. + """ + self.model.save_pretrained(path) + self.tokenizer.save_pretrained(path) + + # Feature 4: Load a model from a custom path + def load_from_path(self, path): + """ + Load a model and tokenizer from a directory. + + Args: + path (str): Path to the directory. + """ + self.model = BioGptForCausalLM.from_pretrained(path) + self.tokenizer = BioGptTokenizer.from_pretrained(path) + + # Feature 5: Print the model's architecture + def print_model(self): + """ + Print the model's architecture. + """ + print(self.model) diff --git a/swarms/models/speecht5.py b/swarms/models/speecht5.py new file mode 100644 index 00000000..e98036ac --- /dev/null +++ b/swarms/models/speecht5.py @@ -0,0 +1,159 @@ +""" +SpeechT5 (TTS task) +SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on LibriTTS. + +This model was introduced in SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. + +SpeechT5 was first released in this repository, original weights. The license used is MIT. + +Model Description +Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. + +Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder. + +Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification. + +Developed by: Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. +Shared by [optional]: Matthijs Hollemans +Model type: text-to-speech +Language(s) (NLP): [More Information Needed] +License: MIT +Finetuned from model [optional]: [More Information Needed] +Model Sources [optional] +Repository: [https://github.com/microsoft/SpeechT5/] +Paper: [https://arxiv.org/pdf/2110.07205.pdf] +Blog Post: [https://huggingface.co/blog/speecht5] +Demo: [https://huggingface.co/spaces/Matthijs/speecht5-tts-demo] + +""" +import torch +import soundfile as sf +from transformers import ( + pipeline, + SpeechT5Processor, + SpeechT5ForTextToSpeech, + SpeechT5HifiGan, +) +from datasets import load_dataset + + +class SpeechT5: + """ + SpeechT5Wrapper + + + Args: + model_name (str, optional): Model name or path. Defaults to "microsoft/speecht5_tts". + vocoder_name (str, optional): Vocoder name or path. Defaults to "microsoft/speecht5_hifigan". + dataset_name (str, optional): Dataset name or path. Defaults to "Matthijs/cmu-arctic-xvectors". + + Attributes: + model_name (str): Model name or path. + vocoder_name (str): Vocoder name or path. + dataset_name (str): Dataset name or path. + processor (SpeechT5Processor): Processor for the SpeechT5 model. + model (SpeechT5ForTextToSpeech): SpeechT5 model. + vocoder (SpeechT5HifiGan): SpeechT5 vocoder. + embeddings_dataset (datasets.Dataset): Dataset containing speaker embeddings. + + Methods + __call__: Synthesize speech from text. + save_speech: Save speech to a file. + set_model: Change the model. + set_vocoder: Change the vocoder. + set_embeddings_dataset: Change the embeddings dataset. + get_sampling_rate: Get the sampling rate of the model. + print_model_details: Print details of the model. + quick_synthesize: Customize pipeline method for quick synthesis. + change_dataset_split: Change dataset split (train, validation, test). + load_custom_embedding: Load a custom speaker embedding (xvector) for the text. + + Usage: + >>> speechT5 = SpeechT5Wrapper() + >>> result = speechT5("Hello, how are you?") + >>> speechT5.save_speech(result) + >>> print("Speech saved successfully!") + + + + """ + + def __init__( + self, + model_name="microsoft/speecht5_tts", + vocoder_name="microsoft/speecht5_hifigan", + dataset_name="Matthijs/cmu-arctic-xvectors", + ): + self.model_name = model_name + self.vocoder_name = vocoder_name + self.dataset_name = dataset_name + self.processor = SpeechT5Processor.from_pretrained(self.model_name) + self.model = SpeechT5ForTextToSpeech.from_pretrained(self.model_name) + self.vocoder = SpeechT5HifiGan.from_pretrained(self.vocoder_name) + self.embeddings_dataset = load_dataset(self.dataset_name, split="validation") + + def __call__(self, text: str, speaker_id: float = 7306): + """Call the model on some text and return the speech.""" + speaker_embedding = torch.tensor( + self.embeddings_dataset[speaker_id]["xvector"] + ).unsqueeze(0) + inputs = self.processor(text=text, return_tensors="pt") + speech = self.model.generate_speech( + inputs["input_ids"], speaker_embedding, vocoder=self.vocoder + ) + return speech + + def save_speech(self, speech, filename="speech.wav"): + """Save Speech to a file.""" + sf.write(filename, speech.numpy(), samplerate=16000) + + def set_model(self, model_name: str): + """Set the model to a new model.""" + self.model_name = model_name + self.processor = SpeechT5Processor.from_pretrained(self.model_name) + self.model = SpeechT5ForTextToSpeech.from_pretrained(self.model_name) + + def set_vocoder(self, vocoder_name): + """Set the vocoder to a new vocoder.""" + self.vocoder_name = vocoder_name + self.vocoder = SpeechT5HifiGan.from_pretrained(self.vocoder_name) + + def set_embeddings_dataset(self, dataset_name): + """Set the embeddings dataset to a new dataset.""" + self.dataset_name = dataset_name + self.embeddings_dataset = load_dataset(self.dataset_name, split="validation") + + # Feature 1: Get sampling rate + def get_sampling_rate(self): + """Get sampling rate of the model.""" + return 16000 + + # Feature 2: Print details of the model + def print_model_details(self): + """Print details of the model.""" + print(f"Model Name: {self.model_name}") + print(f"Vocoder Name: {self.vocoder_name}") + + # Feature 3: Customize pipeline method for quick synthesis + def quick_synthesize(self, text): + """Customize pipeline method for quick synthesis.""" + synthesiser = pipeline("text-to-speech", self.model_name) + speech = synthesiser(text) + return speech + + # Feature 4: Change dataset split (train, validation, test) + def change_dataset_split(self, split="train"): + """Change dataset split (train, validation, test).""" + self.embeddings_dataset = load_dataset(self.dataset_name, split=split) + + # Feature 5: Load a custom speaker embedding (xvector) for the text + def load_custom_embedding(self, xvector): + """Load a custom speaker embedding (xvector) for the text.""" + return torch.tensor(xvector).unsqueeze(0) + + +# if __name__ == "__main__": +# speechT5 = SpeechT5Wrapper() +# result = speechT5("Hello, how are you?") +# speechT5.save_speech(result) +# print("Speech saved successfully!") From e0fcd2c9575815a5bcc17c6dc48bf71e56e1de5f Mon Sep 17 00:00:00 2001 From: pliny <133052465+elder-plinius@users.noreply.github.com> Date: Fri, 20 Oct 2023 10:51:34 -0700 Subject: [PATCH 07/19] Create SMAPS.md Former-commit-id: 8613f907f5ed3347b830495b361d79ca291ff88a --- docs/features/SMAPS.md | 50 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 docs/features/SMAPS.md diff --git a/docs/features/SMAPS.md b/docs/features/SMAPS.md new file mode 100644 index 00000000..c1e60de3 --- /dev/null +++ b/docs/features/SMAPS.md @@ -0,0 +1,50 @@ +# Swarms Multi-Agent Permissions System (SMAPS) + +## Description +SMAPS is a robust permissions management system designed to integrate seamlessly with Swarm's multi-agent AI framework. Drawing inspiration from Amazon's IAM, SMAPS ensures secure, granular control over agent actions while allowing for collaborative human-in-the-loop interventions. + +## Technical Specification + +### 1. Components + +- **User Management**: Handle user registrations, roles, and profiles. +- **Agent Management**: Register, monitor, and manage AI agents. +- **Permissions Engine**: Define and enforce permissions based on roles. +- **Multiplayer Interface**: Allows multiple human users to intervene, guide, or collaborate on tasks being executed by AI agents. + +### 2. Features + +- **Role-Based Access Control (RBAC)**: + - Users can be assigned predefined roles (e.g., Admin, Agent Supervisor, Collaborator). + - Each role has specific permissions associated with it, defining what actions can be performed on AI agents or tasks. + +- **Dynamic Permissions**: + - Create custom roles with specific permissions. + - Permissions granularity: From broad (e.g., view all tasks) to specific (e.g., modify parameters of a particular agent). + +- **Multiplayer Collaboration**: + - Multiple users can join a task in real-time. + - Collaborators can provide real-time feedback or guidance to AI agents. + - A voting system for decision-making when human intervention is required. + +- **Agent Supervision**: + - Monitor agent actions in real-time. + - Intervene, if necessary, to guide agent actions based on permissions. + +- **Audit Trail**: + - All actions, whether performed by humans or AI agents, are logged. + - Review historical actions, decisions, and interventions for accountability and improvement. + +### 3. Security + +- **Authentication**: Secure login mechanisms with multi-factor authentication options. +- **Authorization**: Ensure users and agents can only perform actions they are permitted to. +- **Data Encryption**: All data, whether at rest or in transit, is encrypted using industry-standard protocols. + +### 4. Integration + +- **APIs**: Expose APIs for integrating SMAPS with other systems or for extending its capabilities. +- **SDK**: Provide software development kits for popular programming languages to facilitate integration and extension. + +## Documentation Description +Swarms Multi-Agent Permissions System (SMAPS) offers a sophisticated permissions management mechanism tailored for multi-agent AI frameworks. It combines the robustness of Amazon IAM-like permissions with a unique "multiplayer" feature, allowing multiple humans to collaboratively guide AI agents in real-time. This ensures not only that tasks are executed efficiently but also that they uphold the highest standards of accuracy and ethics. With SMAPS, businesses can harness the power of swarms with confidence, knowing that they have full control and transparency over their AI operations. 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Our Secure Communication Protocols ensure that all communications between agents are encrypted, authenticated, and resistant to tampering or unauthorized access. + +## Features + +### 1. End-to-End Encryption + +- All inter-agent communications are encrypted using state-of-the-art cryptographic algorithms. +- This ensures that data remains confidential and can only be read by the intended recipient agent. + +### 2. Authentication + +- Before initiating communication, agents authenticate each other using digital certificates. +- This prevents impersonation attacks and ensures that agents are communicating with legitimate counterparts. + +### 3. Forward Secrecy + +- Key exchange mechanisms employ forward secrecy, meaning that even if a malicious actor gains access to an encryption key, they cannot decrypt past communications. + +### 4. Data Integrity + +- Cryptographic hashes ensure that the data has not been altered in transit. +- Any discrepancies in data integrity result in the communication being rejected. + +### 5. Zero-Knowledge Protocols + +- When handling especially sensitive data, agents use zero-knowledge proofs to validate information without revealing the actual data. + +### 6. Periodic Key Rotation + +- To mitigate the risk of long-term key exposure, encryption keys are periodically rotated. +- Old keys are securely discarded, ensuring that even if they are compromised, they cannot be used to decrypt communications. + +## Best Practices for Handling Personal and Sensitive Information + +1. **Data Minimization**: Agents should only request and process the minimum amount of personal data necessary for the task. +2. **Anonymization**: Whenever possible, agents should anonymize personal data, stripping away identifying details. +3. **Data Retention Policies**: Personal data should be retained only for the period necessary to complete the task, after which it should be securely deleted. +4. **Access Controls**: Ensure that only authorized agents have access to personal and sensitive information. Implement strict access control mechanisms. +5. **Regular Audits**: Conduct regular security audits to ensure compliance with privacy regulations and to detect any potential vulnerabilities. +6. **Training**: All agents should be regularly updated and trained on the latest security protocols and best practices for handling sensitive data. + +## Conclusion + +Secure communication is paramount in the Swarms Multi-Agent Framework, especially when dealing with personal and sensitive information. 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