├── GenerAd-AI ├── app │ ├── requirements.txt │ └── app.py ├── README.md ├── LICENSE └── notebooks │ ├── 💮 GenerAd AI💮Fine-tuning with OpenAI.ipynb │ └── Synthetic_GPT_4_Dataset_Creation.ipynb ├── MarketMail-AI ├── app │ ├── requirements.txt │ └── app.py ├── README.md └── notebooks │ └── ✉️_MarketMail_AI_✉️_Fine_tuning_BLOOM.ipynb ├── AiconOS ├── py │ ├── data_loader.py │ ├── metadata.csv │ ├── image_to_ios.py │ └── conv_webp.py ├── LICENSE └── README.md ├── TalkToMyDoc - LangChain ├── README.md └── notebook │ └── 🗣️TalkToMyDoc📄_with_LangChain.ipynb ├── ProductSnap-AI ├── LICENSE ├── .gitignore └── README.md ├── README.md └── LLMPromptGen-AI └── README.md /GenerAd-AI/app/requirements.txt: -------------------------------------------------------------------------------- 1 | bitsandbytes 2 | datasets 3 | accelerate 4 | loralib 5 | gradio 6 | git+https://github.com/huggingface/peft.git 7 | git+https://github.com/huggingface/transformers.git@main -------------------------------------------------------------------------------- /MarketMail-AI/app/requirements.txt: -------------------------------------------------------------------------------- 1 | bitsandbytes 2 | datasets 3 | accelerate 4 | loralib 5 | gradio 6 | scipy 7 | git+https://github.com/huggingface/peft.git 8 | git+https://github.com/huggingface/transformers.git@main 9 | -------------------------------------------------------------------------------- /AiconOS/py/data_loader.py: -------------------------------------------------------------------------------- 1 | from datasets import load_dataset 2 | 3 | dataset = load_dataset("imagefolder", data_dir="/Users/ali/Desktop/AiconOS/data_back", drop_labels=True)#, split="train") 4 | 5 | dataset.push_to_hub("Ali-fb/ios_icons") -------------------------------------------------------------------------------- /AiconOS/py/metadata.csv: -------------------------------------------------------------------------------- 1 | file_name,text 2 | icon_1.png,iOS icon 3 | icon_2.png,iOS icon 4 | icon_3.png,iOS icon 5 | icon_4.png,iOS icon 6 | icon_5.png,iOS icon 7 | icon_6.png,iOS icon 8 | icon_7.png,iOS icon 9 | icon_8.png,iOS icon 10 | icon_9.png,iOS icon 11 | icon_10.png,iOS icon -------------------------------------------------------------------------------- /AiconOS/py/image_to_ios.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | 3 | # Open the source image 4 | source_image = Image.open("/Users/ali/Desktop/AiconOS/ios_image_conv/image.png") 5 | 6 | # Define the required icon sizes 7 | icon_sizes = [ 8 | (20, 20), 9 | (29, 29), 10 | (40, 40), 11 | (58, 58), 12 | (60, 60), 13 | (76, 76), 14 | (80, 80), 15 | (87, 87), 16 | (120, 120), 17 | (152, 152), 18 | (167, 167), 19 | (180, 180), 20 | (1024, 1024), 21 | ] 22 | 23 | # Generate each icon size 24 | for size in icon_sizes: 25 | # Resize the image 26 | resized_image = source_image.resize(size) 27 | 28 | # Save the image as an iOS icon 29 | file_name = f"ios_image_conv/generated_icons/icon_{size[0]}x{size[1]}.png" 30 | resized_image.save(file_name) 31 | -------------------------------------------------------------------------------- /TalkToMyDoc - LangChain/README.md: -------------------------------------------------------------------------------- 1 | # 🗣️TalkToMyDoc📄 (FourthBrain Sample Project) 2 | ## Query Your Documents with TalkToMyDoc, powered by LangChain and OpenAI! 3 | TalkToMyDoc is an interface that allows you to directly query your documentation! 4 | 5 | ## 📔 Notebooks 6 | | Notebook | Purpose | Link | 7 | | :-------- | :-------- | :------------------------------------------------------------------------------------------------ | 8 | | **🗣️TalkToMyDoc📄 with LangChain** | A brief introduction to LangChain! | [Here](https://colab.research.google.com/drive/1MLijaQMSzfgBFd-HLXnUVWL60iySjUPM?usp=sharing) | 9 | 10 | ## 💰 Value Generation 11 | TalkToMyDoc allows you to query your documentation directly, without needing to spend time searching through it. This will allow you to focus on your core business and spend less time on documentation! -------------------------------------------------------------------------------- /AiconOS/py/conv_webp.py: -------------------------------------------------------------------------------- 1 | 2 | import os 3 | from PIL import Image 4 | 5 | # Set directory path 6 | directory = '/Users/ali/Desktop/AiconOS/data/' 7 | 8 | # Loop through all files in directory 9 | for filename in os.listdir(directory): 10 | # Check if file is a PNG image 11 | if filename.endswith('.png'): 12 | # Open the image 13 | img = Image.open(os.path.join(directory, filename)) 14 | # Calculate the new size 15 | width, height = img.size 16 | if width > height: 17 | new_width = 256 18 | new_height = int(height * (new_width / width)) 19 | else: 20 | new_height = 256 21 | new_width = int(width * (new_height / height)) 22 | # Resize the image 23 | img_resized = img.resize((new_width, new_height)) 24 | # Save the resized image with "_resized" suffix 25 | new_filename = os.path.splitext(filename)[0] + '_resized' + os.path.splitext(filename)[1] 26 | img_resized.save(os.path.join(directory, new_filename)) 27 | # Close the images 28 | img.close() 29 | img_resized.close() 30 | -------------------------------------------------------------------------------- /AiconOS/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Ali Kadhim 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /ProductSnap-AI/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Ali Kadhim 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /MarketMail-AI/README.md: -------------------------------------------------------------------------------- 1 | # 💮 MarketMail-AI 💮 (FourthBrain Sample Project) 2 | ## Reduce the time spent producing marketing emails with MarketMail-AI - powered by BLOOM! 3 | MarketMail-AI is a fine-tuned LLM marketing assistant that can help you generate marketing email copy for your products at the touch of a button! 4 | 5 | ## 📔 Notebooks 6 | | Notebook | Purpose | Link | 7 | | :-------- | :-------- | :------------------------------------------------------------------------------------------------ | 8 | | **Synthetic Dataset Creation** | Leverage LLMs to create a high quality dataset! | [Here](https://colab.research.google.com/drive/1nsyT9ssUWUWTc_TQ2rykuVtedA7QobA-?usp=sharing) | 9 | | **BLOOM Finetuning Notebook** | Fine-tune BLOOM to produce marketing emails! | [Here](https://colab.research.google.com/drive/1rdQJewZsGfOcjnFx--Lc_h7jJZjfHu6j?usp=sharing) | 10 | 11 | ## 💰 Value Generation 12 | Using a tool like MarketMail-AI will let you produce high quality marketing emails without needing to spend on marketing experts or copywriters. This will allow you to focus on your core business and spend less time on marketing! 13 | 14 | 15 | -------------------------------------------------------------------------------- /GenerAd-AI/app/app.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from peft import PeftModel, PeftConfig 3 | from transformers import AutoModelForCausalLM, AutoTokenizer 4 | 5 | peft_model_id = f"FourthBrainGenAI/GenerAd-AI" 6 | config = PeftConfig.from_pretrained(peft_model_id) 7 | model = AutoModelForCausalLM.from_pretrained( 8 | config.base_model_name_or_path, 9 | return_dict=True, 10 | load_in_8bit=True, 11 | device_map="auto", 12 | ) 13 | tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) 14 | 15 | # Load the Lora model 16 | model = PeftModel.from_pretrained(model, peft_model_id) 17 | 18 | 19 | def make_inference(product_name, product_description): 20 | batch = tokenizer( 21 | f"### Product and Description:\n{product_name}: {product_description}\n\n### Ad:", 22 | return_tensors="pt", 23 | ) 24 | 25 | with torch.cuda.amp.autocast(): 26 | output_tokens = model.generate(**batch, max_new_tokens=50) 27 | 28 | return tokenizer.decode(output_tokens[0], skip_special_tokens=True) 29 | 30 | 31 | if __name__ == "__main__": 32 | # make a gradio interface 33 | import gradio as gr 34 | 35 | gr.Interface( 36 | make_inference, 37 | [ 38 | gr.inputs.Textbox(lines=2, label="Product Name"), 39 | gr.inputs.Textbox(lines=5, label="Product Description"), 40 | ], 41 | gr.outputs.Textbox(label="Ad"), 42 | title="GenerAd-AI", 43 | description="GenerAd-AI is a generative model that generates ads for products.", 44 | ).launch() -------------------------------------------------------------------------------- /MarketMail-AI/app/app.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from peft import PeftModel, PeftConfig 3 | from transformers import AutoModelForCausalLM, AutoTokenizer 4 | 5 | peft_model_id = f"FourthBrainGenAI/MarketMail-AI-Model" 6 | config = PeftConfig.from_pretrained(peft_model_id) 7 | model = AutoModelForCausalLM.from_pretrained( 8 | config.base_model_name_or_path, 9 | return_dict=True, 10 | load_in_8bit=True, 11 | device_map="auto", 12 | ) 13 | tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) 14 | 15 | # Load the Lora model 16 | model = PeftModel.from_pretrained(model, peft_model_id) 17 | 18 | 19 | def make_inference(product, description): 20 | batch = tokenizer( 21 | f"Below is a product and description, please write a marketing email for this product.\n\n### Product:\n{product}\n### Description:\n{description}\n\n### Marketing Email", 22 | return_tensors="pt", 23 | ) 24 | 25 | with torch.cuda.amp.autocast(): 26 | output_tokens = model.generate(**batch, max_new_tokens=50) 27 | 28 | return tokenizer.decode(output_tokens[0], skip_special_tokens=True) 29 | 30 | 31 | if __name__ == "__main__": 32 | # make a gradio interface 33 | import gradio as gr 34 | 35 | gr.Interface( 36 | make_inference, 37 | [ 38 | gr.inputs.Textbox(lines=2, label="Product Name"), 39 | gr.inputs.Textbox(lines=5, label="Product Description"), 40 | ], 41 | gr.outputs.Textbox(label="Ad"), 42 | title="MarketMail-AI", 43 | description="MarketMail-AI is a tool that generates marketing emails for products.", 44 | ).launch() -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

4 |

5 | 6 | #

:wave: Welcome to the Building Generative AI Applications Workshop!

7 | 8 | ## ☑️ Objectives 9 | By the end of this course you will: 10 | - [ ] Learn how prompt engineering and fine-tuning should be used together to optimize the output of large pre-trained models 11 | - [ ] Fine-tune a Large Language Model (LLM) or text-to-image diffusion model using a data-centric approach 12 | - [ ] Deploy your very own Generative AI application to Hugging Face :hugs: 13 | 14 | 15 | ## 🏗️ Project-Based Learning 16 | During this course, you will build, ship, and share your own end-to-end Generative AI application. Each individual/team will be judged on their application’s uniqueness, fine-tuning creativity, user experience, and by a single piece of content generated by their AI application. The winner(s) of the workshop’s competition will be announced at the next Deeplearning.ai + FourthBrain event! Good luck! 😊 17 | 18 | ## :collision: Example Applications 19 | This repo contains two example projects that we will demo during the workshop. Click on the product names below for more information. 20 | - [ ] AiconOS is a Stable Diffusion project that allows you to generate more accurate and vibrant iOS icons based on textual descriptions. The project is open-source and welcomes contributions from the community. 21 | - [ ] GenerAds is a fine-tuned BLOOM LLM marketing assistant that can help you generate quick, and catchy, advertisements for your products. 22 | -------------------------------------------------------------------------------- /LLMPromptGen-AI/README.md: -------------------------------------------------------------------------------- 1 | # 💮 LLMPromptGen-AI 💮 (FourthBrain Sample Project) 2 | ## Building a Generative AI Application with Mixtral - LLMPromptGen AI 3 | LLMPromptGen-AI is a fine-tuned LLM prompt predictor that can help you determine which prompts can give you your desired responses for your LLM. 4 | 5 | Before you begin, you’ll want to make sure you have access to a few tools: 6 | 7 | 1. Hugging Face 🤗 8 | 1. Please make an account 9 | 2. Ensure you have a read/write API access key created 10 | 2. Collab 11 | 1. Your specific experiment might require a Premium Colab experience, you can find instructions on how to do that [here!](https://colab.research.google.com/notebooks/pro.ipynb) 12 | 3. Open AI 13 | 1. If you will be fine-tuning any of [OpenAI’s models](https://platform.openai.com/docs/guides/fine-tuning), you will need an account, with an API access token 14 | 2. If you will be leveraging any of OpenAI’s models for synthetic data creation, you will need an account with an API access token. 15 | 16 | ## 📔 Notebooks 17 | | Notebook | Purpose | Link | 18 | | :-------- | :-------- | :------------------------------------------------------------------------------------------------ | 19 | | 🌼**MIXTRAL-LoRA Fine-tuning Notebook** | Fine-tune and run inference on the fine-tuned result | [Here](https://colab.research.google.com/drive/17SPgWEkv2EIA3FgdTmLTIEb6avlOQSF2?usp=sharing) | 20 | | 🖥️ **Synthetic Dataset Creation Notebook** | Create Synthetic Data using OpenAI's API | [Here](https://colab.research.google.com/drive/1nTKZNVoDoWQ32sXI9NtnxFf0gCJgMqWR?usp=sharing) | 21 | 22 | ## 📚 Data 23 | Data are from the Mosaic Instruct 3 dataset. An instruction-following dataset with a large number of longform samples. However, we can also generate data synthetically with GPT-4. 24 | 25 | Data found [here](https://huggingface.co/datasets/mosaicml/instruct-v3) 26 | 27 | ## 💰 Value Generation 28 | With LLMPromptGenAI - deepen your understanding of techniques for prompt engineering to cue a specific model to give optimal results. 29 | 30 | -------------------------------------------------------------------------------- /ProductSnap-AI/.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /AiconOS/README.md: -------------------------------------------------------------------------------- 1 | # 🖼️ AiconOS 2 | ## 💻 Stable Diffusion Model for iOS Software Development 3 | 4 | AiconOS is a Stable Diffusion project that allows you to generate more accurate and vibrant iOS icons based on textual descriptions. The project is open-source and welcomes contributions from the community. 5 | 6 | ## 📔 Notebooks 7 | | Notebook | Purpose | Link | 8 | | :-------- | :-------- | :------------------------------------------------------------------------------------------------ | 9 | | 🏞️ **SD Inference Notebook** | Run an inference on the standard stable diffusion models as a baseline | [Here](https://colab.research.google.com/drive/1mtIcwR5L2iq72Vf3tNlaHLM5ONjXxADD?usp=sharing) | 10 | | 🏋️ **SD Training Notebook** | Train stable diffusion models on your dataset | [Here](https://colab.research.google.com/drive/19WqKyZbKcMs-pscXnzDKWvtx6j_7Qeb2?usp=sharing) | 11 | 12 | ## 🫓 Baseline 13 | Our baseline was generated utilizing the inference notebook with original stable diffusion v1.2 (shown) and v2.1. 14 | 15 | ![image](https://user-images.githubusercontent.com/37101144/234615910-8a392e56-200c-482d-b5d0-591c54e23ec5.png) 16 | 17 | **prompt 1** "flashlight iOS icon" 18 | 19 | **prompt 2** "clothing ecommerce business iOS icon" 20 | 21 | **prompt 3** "twitter iOS icon" 22 | 23 | **prompt 4** "Mike and Ike iOS icon" 24 | 25 | 26 | ## 📦 Data 27 | Our curated dataset is based on the images from [iOS Icon Gallery](https://www.iosicongallery.com/), which includes a variety of colorful and practical icons. The dataset consists of textual descriptions of the style "iOS icons". The dataset has been downsized and cleaned to ensure consistency and accuracy. You can checkout the specific 🤗 HuggingFace Dataset [here](https://huggingface.co/datasets/Ali-fb/ios_icons). 28 | 29 | ![icon_4](https://user-images.githubusercontent.com/37101144/234617014-8f592e80-124c-4fbd-9476-ad20fa4ce6cc.png) 30 | ![icon_2](https://user-images.githubusercontent.com/37101144/234617194-d8003f3c-d0a8-4a64-9e5e-bb67cb8e0e82.png) 31 | ![icon_10](https://user-images.githubusercontent.com/37101144/234617052-7d4e7eaf-d934-40a8-bb10-c355d26cc80b.png) 32 | 33 | 34 | 35 | ## 🤖 Example Model 36 | The Stable Diffusion model was fine-tuned on our curated dataset for iOS Icons to generate icons for software development applications. This method can be used to to train a model for any os/platform. You can checkout the sample model [here](https://huggingface.co/Ali-fb/sd_aiconos-model-v1-2_400). 37 | 38 | ## 💰 Benefits for Businesses 39 | 40 | AiconsUS can provide the following benefits for mobile software development: 41 | 42 | - **Accurate and vibrants icons:** AiconOS uses stable diffusion algorithms to create high-quality icons that are more accurate, vibrant, and realistic. 43 | - **Customizable options:** The software provides customizable options that allow users to create unique icons tailored to their specific needs. 44 | - **Supports various icon sizes:** AiconOS supports various icon sizes, including 512x512, 256x256, 128x128, 64x64, 32x32, and 16x16, making it suitable for different use cases. 45 | - **Multiple file format support:** AiconOS supports multiple file formats, including PNG, JPG, SVG, and ICO, ensuring compatibility with different platforms. 46 | 47 | 48 | ## 📊 Results 49 | 50 | ProductSnapAI can produce high-quality images from textual descriptions, with the following result inferences: 51 | 52 | - Increased accuracy and brand recognition in generating product features such as colors, sizes, and materials. 53 | - Improved realism in product images, resulting in a more engaging customer experience. 54 | - Faster generation times and improved efficiency, allowing for more rapid image generation at scale. 55 | 56 | ![image](https://user-images.githubusercontent.com/37101144/234615795-10af6210-8a1d-41b8-96f0-debb4083ae1d.png) 57 | 58 | 59 | **prompt 1** "flashlight iOS icon" 60 | 61 | **prompt 2** "clothing ecommerce business iOS icon" 62 | 63 | **prompt 3** "twitter iOS icon" 64 | 65 | **prompt 4** "Mike and Ike iOS icon" 66 | 67 | ## 🚀 Getting Started 68 | 69 | To use AiconOS, simply use the provided notebooks to fine-tune the model using a small sample dataset of your preffered icon images. Run the provided scripts to generate icon images from textual descriptions. You can checkout our deployed model [here](https://huggingface.co/Ali-fb/sd_aiconos-model-v1-2_400) 70 | 71 | ## 💬 Feedback and Contributions 72 | 73 | We welcome feedback and contributions from the community! If you have any suggestions, issues, or pull requests, please feel free to submit them to the repository. 🧑‍💻 74 | 75 | -------------------------------------------------------------------------------- /ProductSnap-AI/README.md: -------------------------------------------------------------------------------- 1 | # 🖼️ ProductSnapAI 2 | ## 💻 Stable Diffusion Model for E-Commerce Products Images 3 | 4 | ProductSnapAI is a Stable Diffusion project that allows you to create visual representations of products in your brand style based on textual descriptions. The project is open-source and welcomes contributions from the community. 5 | 6 | ## 📔 Notebooks 7 | | Notebook | Purpose | Link | 8 | | :-------- | :-------- | :------------------------------------------------------------------------------------------------ | 9 | | 🏞️ **SD Inference Notebook** | Run an inference on the standard stable diffusion models as a baseline | [Here](https://colab.research.google.com/drive/10wsWtMyM2lNFyGfck5I5MQmVcAggrlIW?usp=sharing) | 10 | | 🏋️ **SD Training Notebook** | Train stable diffusion models on your dataset | [Here](https://colab.research.google.com/drive/1ZVSof0szrYoCO_lPNzP9ctTP_sZrUp1G?usp=sharing) | 11 | 12 | ## 🫓 Baseline 13 | Our baseline was generated utilizing the inference notebook with original stable diffusion v1.2 (shown) and v2.1. 14 | 15 | ![image](https://user-images.githubusercontent.com/37101144/228467693-b6edcb03-1d76-40ce-b1a4-299802d5ce20.png) 16 | 17 | **prompt 1** "black hoodie with a front half zipper by martin valen" 18 | 19 | **prompt 2** "white hoodie with a blue design by martin valen" 20 | 21 | **prompt 3** "stripped hoodie by martin valen" 22 | 23 | **prompt 4** "camouflage hoodie by martin valen" 24 | 25 | 26 | ## 📦 Data 27 | Our curated dataset is based on the images from [Martin Valen](https://martinvalen.com/en/sweatshirts-hoodies), which includes a variety of sweatshirts and hoodies. The dataset consists of textual descriptions of the brand name. The dataset has been downsized and cleaned to ensure consistency and accuracy. You can checkout the specific 🤗 HuggingFace Dataset [here](https://huggingface.co/datasets/Ali-fb/martin_valen_dataset). 28 | 29 | ![image](https://user-images.githubusercontent.com/37101144/228462176-5d862ead-bebf-432a-8342-7c2208f51df9.png) 30 | ![image](https://user-images.githubusercontent.com/37101144/228462236-94a4e540-6750-44a1-9d45-cfaffc8ab968.png) 31 | ![image](https://user-images.githubusercontent.com/37101144/228462262-17695ab8-2af5-44c2-8c45-ec295ccebfb0.png) 32 | 33 | 34 | ## 🤖 Example Model 35 | The Stable Diffusion model was fine-tuned on our curated dataset for Martin Valen to optimize for business use cases, such as generating product images for the e-commerce website and social media. This method can be used to to train a model for any e-commerce business. You can checkout the sample model [here](https://huggingface.co/Ali-fb/sd_martin_valen-model-v1-2_400). 36 | 37 | ## 💰 Benefits for Businesses 38 | 39 | ProductSnapAI can provide the following benefits for businesses: 40 | 41 | - Enhance brand consistency by generating images that align with the company's branding style and visual identity. 42 | - Save time and resources by automating the image creation process, allowing businesses to quickly generate high-quality images of their products with respect to their company branding style without the need for professional photography or graphic design services. 43 | - Improve the customer experience by providing accurate and detailed representations of products that do not yet exist, which can help to increase customer engagement and product improvement. 44 | - Increase sales and customer satisfaction by providing engaging and realistic product images that accurately reflect the product's features, colors, and materials. 45 | 46 | 47 | ## 📊 Results 48 | 49 | ProductSnapAI can produce high-quality images from textual descriptions, with the following result inferences: 50 | 51 | - Increased accuracy and brand recognition in generating product features such as colors, sizes, and materials. 52 | - Improved realism in product images, resulting in a more engaging customer experience. 53 | - Faster generation times and improved efficiency, allowing for more rapid image generation at scale. 54 | 55 | ![image](https://user-images.githubusercontent.com/37101144/228463036-88de373c-60ac-4bfe-8e01-a9b271843a59.png) 56 | 57 | **prompt 1** "black hoodie with a front half zipper by martin valen" 58 | 59 | **prompt 2** "white hoodie with a blue design by martin valen" 60 | 61 | **prompt 3** "stripped hoodie by martin valen" 62 | 63 | **prompt 4** "camouflage hoodie by martin valen" 64 | 65 | ## 🚀 Getting Started 66 | 67 | To use ProductSnapAI, simply use the provided notebooks to fine-tune the model using a small sample dataset of your product images. Run the provided scripts to generate product images from textual descriptions in your brand style. You can checkout our deployed model [here](https://huggingface.co/spaces/Ali-fb/Ali-fb-sd_martin_valen-model-v1-2_400) 68 | 69 | ## 💬 Feedback and Contributions 70 | 71 | We welcome feedback and contributions from the community! If you have any suggestions, issues, or pull requests, please feel free to submit them to the repository. 🧑‍💻 72 | 73 | -------------------------------------------------------------------------------- /GenerAd-AI/README.md: -------------------------------------------------------------------------------- 1 | # 💮 GenerAd AI 💮 (FourthBrain Sample Project) 2 | ## Elevate your Ad game with GenerAd, powered by BLOOM! 3 | GenerAd AI is a fine-tuned LLM marketing assistant that can help you generate quick, and catchy, advertisements for your products! 4 | 5 | ## 📔 Notebooks 6 | | Notebook | Purpose | Link | 7 | | :-------- | :-------- | :------------------------------------------------------------------------------------------------ | 8 | | 🌼**BLOOM-LoRA Fine-tuning Notebook** | Fine-tune and run inference on the fine-tuned result | [Here](https://colab.research.google.com/drive/12qOhhGyoh7qSm1eqeMCbvv63EObH2TBH?usp=sharing) | 9 | | 🖥️ **GPT-3 Ada Fine-tuning Notebook** | Fine-tune your model using OpenAI's fine-tuning API | [Here](https://colab.research.google.com/drive/16nc8RXIcM9iulDsAlhfolvKoyar_Xn4f?usp=sharing) | 10 | 11 | ## 📚 Data 12 | Data was generated synthetically with GPT-4. 13 | 14 | Process and data found [here](https://huggingface.co/datasets/c-s-ale/Product-Descriptions-and-Ads) 15 | 16 | ## 💰 Value Generation 17 | With GenerAD AI, small businesses or lean teams don't have to be burdened with generating marketing material for their products. They can leverage GenerAd AI to accelerate their marketing workflows and get the Twitter or Instagram ad they want in a matter of moments. 18 | 19 | Instead of spending time or money on making ads, enable yourself to spend more time building value and relationships. 20 | 21 | ## 🤖 Example Model 22 | While GenerAd AI was fine-tuned on clothing advertisements, it generalizes well to a large number of domains. 23 | 24 | We will be hosting an example model soon! 25 | 26 | ## 🎬 Baseline 27 | 28 | Baseline results for three sample prompts: 29 | 30 | #### GPT-3 Ada 31 | 32 | ``` 33 | Prompt: 34 | Lace-up sandals: Shoes featuring laces or ties that wrap around the foot and, in some cases, the ankle.\n\n###\n\n 35 | 36 | Reponse: 37 | 38 | Lace-up sandals: Shoes without laces or ties that areStrands of textiles that run around the foot and, in some cases, the ankle. 39 | ``` 40 | 41 | ``` 42 | Prompt: 43 | Sundress: A flowery yellow sundress with blue polka dots.\n\n###\n\n 44 | 45 | Reponse: 46 | Shirt: 47 | 48 | The shirt is a lightweight, comfortable shirt made from a light, Aethercrete fabric. It has a comfortable fit and a stylish design. 49 | ``` 50 | 51 | ``` 52 | Prompt: 53 | A new Lexus: A luxury automobile with grey paint and tinted windows.\n\n###\n\n 54 | 55 | Response: 56 | The Lexus LS is a luxury automobile with grey paint and tinted windows. It is developed from the Lexus LS. This vehicle has a value of $50,000. 57 | ``` 58 | 59 | #### BLOOM 1b7 60 | 61 | ``` 62 | ### Product and Description: 63 | Lace-up sandals: Shoes featuring laces or ties that wrap around the foot and, in some cases, the ankle. 64 | 65 | ### Ad: 66 | From Wikipedia: 67 | 68 | Ads (also called advertisements) are paid promotions of products, 69 | ``` 70 | 71 | ``` 72 | ### Product and Description: 73 | Sundress: A flowery yellow sundress with blue polka dots. 74 | 75 | ### Ad: 76 | If you are bored, visit Awesome Site. 77 | 78 | ### Note: 79 | This 80 | ``` 81 | 82 | ``` 83 | ### Product and Description: 84 | A new Lexus: A luxury automobile with grey paint and tinted windows. 85 | 86 | ### Ad: 87 | [Insert ad here] 88 | 89 | ### Ad ID: {ID} 90 | 91 | ### Back to top 92 | The 93 | ``` 94 | 95 | ## 📊 Results 96 | 97 | #### GPT-3 Ada 98 | 99 | ``` 100 | Prompt: 101 | Lace-up sandals: Shoes featuring laces or ties that wrap around the foot and, in some cases, the ankle.\n\n###\n\n 102 | 103 | Reponse: 104 | Discover our new Lace-up Sandals - the perfect blend of style and comfort! Wrap your feet in comfortable shorts or tops that provide azuweki-style rope-style comfort. Shop now and turn heads - #ToughStockPower 105 | 106 | ``` 107 | 108 | ``` 109 | Prompt: 110 | Sundress: A flowery yellow sundress with blue polka dots.\n\n###\n\n 111 | 112 | Reponse: 113 | Stay warm & stylish this season with our adorable Breakaway Sundress. Party on top of the girl's cool, purple polka dots. Don't let the chill go, turn up on your way to sparkly success! Get yours now! END" 114 | 115 | ``` 116 | 117 | ``` 118 | Prompt: 119 | A new Lexus: A luxury automobile with grey paint and tinted windows.\n\n###\n\n 120 | 121 | Response: 122 | Upgrade your lifestyle by adding style and sophistication to your Lexus brand as we launch a new model: Grey & Tinted! Experience comfort & sophistication with strong sobriety standards. Get yours today! #LexusYearmondiff #WithH' 123 | ``` 124 | 125 | #### BLOOM 1b7 126 | 127 | ``` 128 | ### Product and Description: 129 | Lace-up sandals: Shoes featuring laces or ties that wrap around the foot and, in some cases, the ankle. 130 | 131 | ### Ad: 132 | Discover timeless style with our Lace-up Sandals! Wrap your feet in chic design and experience unbeatable comfort. Perfect for any occasion, upgrade your shoe game today. Limited stock. Get yours now! Visit our Shoe 133 | ``` 134 | 135 | ``` 136 | ### Product and Description: 137 | Sundress: A flowery yellow sundress with blue polka dots. 138 | 139 | ### Ad: 140 | Discover timeless style in a flowery yellow sundress + blue polka dots! Make a statement in any occasion, featuring sheer perfection in every turn. Elevate your wardrobe now! #SundressLove #F 141 | ``` 142 | 143 | ``` 144 | ### Product and Description: 145 | A new Lexus: A luxury automobile with grey paint and tinted windows. 146 | 147 | ### Ad: 148 | Discover luxury and style with our new Lexus! Experience grey paint & tinted windows, two of the most sought-after luxury attributes. Discover your new calling today. #LexusLuxury #LexusTintedWindows 149 | ``` 150 | 151 | 152 | 153 | -------------------------------------------------------------------------------- /GenerAd-AI/LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": { 17 | "id": "2NY3mGaE2YYR" 18 | }, 19 | "source": [ 20 | "### Getting Requirements" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "metadata": { 27 | "id": "tsW9Pxbp2YYR" 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "!pip install openai" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "metadata": { 38 | "id": "eihDmnqb2YYR" 39 | }, 40 | "outputs": [], 41 | "source": [ 42 | "import openai\n", 43 | "openai.api_key = \"\"" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": { 49 | "id": "WPC5k8Nf2YYR" 50 | }, 51 | "source": [ 52 | "### Setting up Data\n", 53 | "\n", 54 | "We'll want to ensure our data is in the following format as per OpenAI [suggestions](https://platform.openai.com/docs/guides/fine-tuning): \n", 55 | "\n", 56 | "```\n", 57 | "{\"prompt\" : \": \\n\\n###\\n\\n\", \"completion\" : \" END\"}\n", 58 | "```\n", 59 | "\n", 60 | "So we'll need to transform our data into this format." 61 | ] 62 | }, 63 | { 64 | "cell_type": "markdown", 65 | "metadata": { 66 | "id": "1YkW32na2YYS" 67 | }, 68 | "source": [ 69 | "### Logging Into Hugging Face" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": null, 75 | "metadata": { 76 | "id": "-ygCaP9Z2YYS", 77 | "outputId": "eb660403-80f9-4016-b6a0-a84cbb8d8afb" 78 | }, 79 | "outputs": [ 80 | { 81 | "name": "stdout", 82 | "output_type": "stream", 83 | "text": [ 84 | "Token is valid.\n", 85 | "Your token has been saved in your configured git credential helpers (store).\n", 86 | "Your token has been saved to /home/chris/.cache/huggingface/token\n", 87 | "Login successful\n" 88 | ] 89 | } 90 | ], 91 | "source": [ 92 | "from huggingface_hub import notebook_login\n", 93 | "\n", 94 | "notebook_login()" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": null, 100 | "metadata": { 101 | "colab": { 102 | "referenced_widgets": [ 103 | "3fda90dc891b4279a11b85c799a4156f" 104 | ] 105 | }, 106 | "id": "YfonaEfB2YYS", 107 | "outputId": "45b20fdf-5c4e-4f3d-a93a-0bbc65a9e994" 108 | }, 109 | "outputs": [ 110 | { 111 | "name": "stderr", 112 | "output_type": "stream", 113 | "text": [ 114 | "Found cached dataset parquet (/home/chris/.cache/huggingface/datasets/c-s-ale___parquet/c-s-ale--Product-Descriptions-and-Ads-e60cdaa742e1bbad/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n" 115 | ] 116 | }, 117 | { 118 | "data": { 119 | "application/vnd.jupyter.widget-view+json": { 120 | "model_id": "3fda90dc891b4279a11b85c799a4156f", 121 | "version_major": 2, 122 | "version_minor": 0 123 | }, 124 | "text/plain": [ 125 | " 0%| | 0/2 [00:00 openai api fine_tunes.create -t \"openai_dataset_prepared (1).jsonl\"\n", 218 | "\n", 219 | "After you’ve fine-tuned a model, remember that your prompt has to end with the indicator string `.\\n\\n###\\n\\n` for the model to start generating completions, rather than continuing with the prompt. Make sure to include `stop=[\" END\"]` so that the generated texts ends at the expected place.\n", 220 | "Once your model starts training, it'll approximately take 3.68 minutes to train a `curie` model, and less for `ada` and `babbage`. Queue will approximately take half an hour per job ahead of you.\n" 221 | ] 222 | } 223 | ], 224 | "source": [ 225 | "!openai tools fine_tunes.prepare_data -f openai_dataset.jsonl -q" 226 | ] 227 | }, 228 | { 229 | "cell_type": "markdown", 230 | "metadata": { 231 | "id": "rcXbBYAq2YYT" 232 | }, 233 | "source": [ 234 | "Now, we can load our OPENAI_API_KEY into our environment variables and fine-tune!" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": null, 240 | "metadata": { 241 | "id": "_xLJLPWK2YYT" 242 | }, 243 | "outputs": [], 244 | "source": [ 245 | "import os\n", 246 | "os.environ[\"OPENAI_API_KEY\"] = \"\"" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": null, 252 | "metadata": { 253 | "id": "f239cowc2YYT" 254 | }, 255 | "outputs": [], 256 | "source": [ 257 | "!openai api fine_tunes.create -t \"openai_dataset_prepared.jsonl\" -m \"ada\" --suffix \"GenerAd\"" 258 | ] 259 | }, 260 | { 261 | "cell_type": "markdown", 262 | "metadata": { 263 | "id": "Or-BHDSd2YYT" 264 | }, 265 | "source": [ 266 | "You can continue to watch your model fine-tune with the following command:" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": null, 272 | "metadata": { 273 | "id": "vdRDxuf-2YYT" 274 | }, 275 | "outputs": [], 276 | "source": [ 277 | "!openai api fine_tunes.follow -i " 278 | ] 279 | }, 280 | { 281 | "cell_type": "markdown", 282 | "metadata": { 283 | "id": "j1Qw7NTw2YYT" 284 | }, 285 | "source": [ 286 | "### Trying it out!" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": null, 292 | "metadata": { 293 | "id": "kxOjplas2YYT" 294 | }, 295 | "outputs": [], 296 | "source": [ 297 | "model = \"YOUR MODEL HERE\"" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": { 303 | "id": "daZFOgqT2YYT" 304 | }, 305 | "source": [ 306 | "#### Example in Training Set" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": null, 312 | "metadata": { 313 | "id": "B7VThZuC2YYT", 314 | "outputId": "79c3bc88-4f49-4ce9-ce0e-fb2b0e29b4af" 315 | }, 316 | "outputs": [ 317 | { 318 | "data": { 319 | "text/plain": [ 320 | "' Discover our new Lace-up Sandals - the perfect blend of style and comfort! Wrap your feet in comfortable shorts or tops that provide azuweki-style rope-style comfort. Shop now and turn heads - #ToughStockPower'" 321 | ] 322 | }, 323 | "execution_count": 5, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "openai.Completion.create(\n", 330 | " model = model,\n", 331 | " prompt = \"Lace-up sandals: Shoes featuring laces or ties that wrap around the foot and, in some cases, the ankle.\\n\\n###\\n\\n\",\n", 332 | " max_tokens = 50,\n", 333 | ")[\"choices\"][0][\"text\"]" 334 | ] 335 | }, 336 | { 337 | "cell_type": "code", 338 | "execution_count": null, 339 | "metadata": { 340 | "id": "efc1YudQ2YYT", 341 | "outputId": "7f41e5b8-45ca-4491-dfdd-4953b2ebe5b2" 342 | }, 343 | "outputs": [ 344 | { 345 | "data": { 346 | "text/plain": [ 347 | "\" Stay warm & stylish this season with our adorable Breakaway Sundress. Party on top of the girl's cool, purple polka dots. Don't let the chill go, turn up on your way to sparkly success! Get yours now! END\"" 348 | ] 349 | }, 350 | "execution_count": 16, 351 | "metadata": {}, 352 | "output_type": "execute_result" 353 | } 354 | ], 355 | "source": [ 356 | "openai.Completion.create(\n", 357 | " model = model,\n", 358 | " prompt = \"Sundress: A flowery yellow sundress with blue polka dots.\\n\\n###\\n\\n\",\n", 359 | " max_tokens = 50,\n", 360 | ")[\"choices\"][0][\"text\"]" 361 | ] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": null, 366 | "metadata": { 367 | "id": "0p5Xjr5f2YYT", 368 | "outputId": "52d1da0c-1fac-41b2-bba5-ed9e290e2b56" 369 | }, 370 | "outputs": [ 371 | { 372 | "data": { 373 | "text/plain": [ 374 | "' Upgrade your lifestyle by adding style and sophistication to your Lexus brand as we launch a new model: Grey & Tinted! Experience comfort & sophistication with strong sobriety standards. Get yours today! #LexusYearmondiff #WithH'" 375 | ] 376 | }, 377 | "execution_count": 7, 378 | "metadata": {}, 379 | "output_type": "execute_result" 380 | } 381 | ], 382 | "source": [ 383 | "openai.Completion.create(\n", 384 | " model = model,\n", 385 | " prompt = \"A new Lexus: A luxury automobile with grey paint and tinted windows.\\n\\n###\\n\\n\",\n", 386 | " max_tokens = 50,\n", 387 | ")[\"choices\"][0][\"text\"]" 388 | ] 389 | } 390 | ], 391 | "metadata": { 392 | "kernelspec": { 393 | "display_name": "open_ai", 394 | "language": "python", 395 | "name": "python3" 396 | }, 397 | "language_info": { 398 | "codemirror_mode": { 399 | "name": "ipython", 400 | "version": 3 401 | }, 402 | "file_extension": ".py", 403 | "mimetype": "text/x-python", 404 | "name": "python", 405 | "nbconvert_exporter": "python", 406 | "pygments_lexer": "ipython3", 407 | "version": "3.10.10" 408 | }, 409 | "orig_nbformat": 4, 410 | "colab": { 411 | "provenance": [] 412 | } 413 | }, 414 | "nbformat": 4, 415 | "nbformat_minor": 0 416 | } 417 | -------------------------------------------------------------------------------- /GenerAd-AI/notebooks/Synthetic_GPT_4_Dataset_Creation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "### OpenAI Access\n", 7 | "\n", 8 | "First things first, you'll need to set-up an account on [OpenAI](platform.openai.com). Once you've done that - follow [these resources](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) to create an API key. Make sure you save your API key!" 9 | ], 10 | "metadata": { 11 | "id": "saLMqCm7huKq" 12 | } 13 | }, 14 | { 15 | "cell_type": "code", 16 | "source": [ 17 | "!pip install openai" 18 | ], 19 | "metadata": { 20 | "id": "UpeXOOlfqZCb" 21 | }, 22 | "execution_count": null, 23 | "outputs": [] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": null, 28 | "metadata": { 29 | "id": "Ktti2bvoYrWc" 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "import os \n", 34 | "\n", 35 | "# Set the OPENAI_API_KEY environment variable\n", 36 | "os.environ[\"OPENAI_API_KEY\"] = \"\"" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": { 43 | "id": "EluoMmF3YrWd" 44 | }, 45 | "outputs": [], 46 | "source": [ 47 | "import openai\n", 48 | "import os\n", 49 | "from IPython.display import display, Markdown\n", 50 | "\n", 51 | "class Prompter:\n", 52 | " def __init__(self, gpt_model):\n", 53 | " if not os.environ.get(\"OPENAI_API_KEY\"):\n", 54 | " raise Exception(\"Please set the OPENAI_API_KEY environment variable\")\n", 55 | "\n", 56 | " openai.api_key = os.environ.get(\"OPENAI_API_KEY\")\n", 57 | "\n", 58 | " self.gpt_model = gpt_model\n", 59 | "\n", 60 | " def prompt_model_print(self, messages: list):\n", 61 | " response = openai.ChatCompletion.create(model=self.gpt_model, messages=messages)\n", 62 | " display(Markdown(response[\"choices\"][0][\"message\"][\"content\"]))\n", 63 | " \n", 64 | " def prompt_model_return(self, messages: list):\n", 65 | " response = openai.ChatCompletion.create(model=self.gpt_model, messages=messages)\n", 66 | " return response[\"choices\"][0][\"message\"][\"content\"]" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "source": [ 72 | "Replace `\"OPEN_AI_MODEL\"` with the following:\n", 73 | "\n", 74 | "If you just set up your OpenAI acct., you'll want to use: `\"gpt-3.5-turbo\"`\n", 75 | "\n", 76 | "If you have access to GPT-4, go ahead and use `\"gpt-4\"`." 77 | ], 78 | "metadata": { 79 | "id": "kC0YtUELhwdU" 80 | } 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": null, 85 | "metadata": { 86 | "id": "_w3nnDhnYrWd" 87 | }, 88 | "outputs": [], 89 | "source": [ 90 | "prompter = Prompter(\"OPEN_AI_MODEL\")" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": null, 96 | "metadata": { 97 | "id": "audN435yYrWd" 98 | }, 99 | "outputs": [], 100 | "source": [ 101 | "datagen_prompts = [\n", 102 | " {\"role\" : \"system\", \"content\" : \"You are a professional fasion designer.\"},\n", 103 | " {\"role\" : \"user\", \"content\" : \"Please generate a Python list of 5 clothing items and their descriptions.\"},\n", 104 | "]" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "metadata": { 111 | "id": "VUJPivDkYrWd", 112 | "outputId": "a6233559-54fc-453b-c779-34ebcbadf11c" 113 | }, 114 | "outputs": [ 115 | { 116 | "data": { 117 | "text/markdown": [ 118 | "clothing_items = [\n", 119 | " {\n", 120 | " \"name\": \"Asymmetrical Ruffle Dress\",\n", 121 | " \"description\": \"This elegant asymmetrical dress features a single shoulder design with soft ruffles cascading down the side, adding a feminine touch. The lightweight fabric drapes beautifully over the body for a flattering and comfortable fit.\"\n", 122 | " },\n", 123 | " {\n", 124 | " \"name\": \"Wide-leg Trousers\",\n", 125 | " \"description\": \"These versatile wide-leg trousers are crafted from a soft, breathable fabric that offer both elegance and comfort. The high-waist design and pleated front create a flattering silhouette, suitable for any occasion.\"\n", 126 | " },\n", 127 | " {\n", 128 | " \"name\": \"Cropped Denim Jacket\",\n", 129 | " \"description\": \"This stylish cropped denim jacket adds an edgy touch to any outfit. Designed with a classic collar, button-front closure, and chest button-flap pockets, it is perfect for layering over your favorite tops or dresses.\"\n", 130 | " },\n", 131 | " {\n", 132 | " \"name\": \"Front-tie Blouse\",\n", 133 | " \"description\": \"This charming front-tie blouse features a deep V-neckline and flutter sleeves, making it an effortlessly chic addition to your wardrobe. The lightweight, semi-sheer fabric drapes beautifully and can be dressed up or down for any occasion.\"\n", 134 | " },\n", 135 | " {\n", 136 | " \"name\": \"Maxi Skirt\",\n", 137 | " \"description\": \"Crafted from a lightweight, flowing fabric, this maxi skirt offers an effortlessly elegant silhouette. The high-waist design is paired with a side slit for added sophistication, making this skirt a staple for both casual and formal attire.\"\n", 138 | " }\n", 139 | "]" 140 | ], 141 | "text/plain": [ 142 | "" 143 | ] 144 | }, 145 | "metadata": {}, 146 | "output_type": "display_data" 147 | } 148 | ], 149 | "source": [ 150 | "prompter.prompt_model_print(datagen_prompts)" 151 | ] 152 | }, 153 | { 154 | "cell_type": "markdown", 155 | "metadata": { 156 | "id": "JnMtCyzrYrWe" 157 | }, 158 | "source": [ 159 | "Now that we have our initial data, lets create the ads for each of the products. We will use the following function to create the ads." 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": null, 165 | "metadata": { 166 | "id": "JM-cjMAGYrWe" 167 | }, 168 | "outputs": [], 169 | "source": [ 170 | "clothing_items = [ { \"name\": \"Asymmetrical Ruffle Dress\", \"description\": \"This elegant asymmetrical dress features a single shoulder design with soft ruffles cascading down the side, adding a feminine touch. The lightweight fabric drapes beautifully over the body for a flattering and comfortable fit.\" }, { \"name\": \"Wide-leg Trousers\", \"description\": \"These versatile wide-leg trousers are crafted from a soft, breathable fabric that offer both elegance and comfort. The high-waist design and pleated front create a flattering silhouette, suitable for any occasion.\" }, { \"name\": \"Cropped Denim Jacket\", \"description\": \"This stylish cropped denim jacket adds an edgy touch to any outfit. Designed with a classic collar, button-front closure, and chest button-flap pockets, it is perfect for layering over your favorite tops or dresses.\" }, { \"name\": \"Front-tie Blouse\", \"description\": \"This charming front-tie blouse features a deep V-neckline and flutter sleeves, making it an effortlessly chic addition to your wardrobe. The lightweight, semi-sheer fabric drapes beautifully and can be dressed up or down for any occasion.\" }, { \"name\": \"Maxi Skirt\", \"description\": \"Crafted from a lightweight, flowing fabric, this maxi skirt offers an effortlessly elegant silhouette. The high-waist design is paired with a side slit for added sophistication, making this skirt a staple for both casual and formal attire.\" } ]" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": null, 176 | "metadata": { 177 | "id": "zsnfAea8YrWf" 178 | }, 179 | "outputs": [], 180 | "source": [ 181 | "system_prompt = {\"role\" : \"system\", \"content\" : \"You are a ad executive. Your job is to create a short punchy new ad for the following product.\"}" 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": null, 187 | "metadata": { 188 | "id": "_fmyXz7pYrWf", 189 | "outputId": "7b724386-cf9d-4398-9104-db2e7f192f71", 190 | "colab": { 191 | "base_uri": "https://localhost:8080/" 192 | } 193 | }, 194 | "outputs": [ 195 | { 196 | "output_type": "stream", 197 | "name": "stdout", 198 | "text": [ 199 | "Introducing the Asymmetrical Ruffle Dress: Elegance with a Twist! 💃\n", 200 | "\n", 201 | "Flaunt your unique style in our stunning Asymmetrical Ruffle Dress! Turn heads with its one-shoulder sensation, and let the cascading ruffles add a touch of femininity. Drape yourself in lightweight luxury for a flattering, comfortable fit that will make you shine. ✨\n", 202 | "\n", 203 | "Be Bold. Be Chic. Be Asymmetrical. Get Ready to Ruffle! 🌟\n", 204 | "Introducing our game-changer Wide-leg Trousers! 🌟\n", 205 | "\n", 206 | "Swing into style and redefine your wardrobe! 💃\n", 207 | "\n", 208 | "👖 Experience ultimate elegance and unbeatable comfort in one gorgeous piece.\n", 209 | "🌬️ Made from soft, breathable fabric that moves with you.\n", 210 | "⚖️ Flattering high-waisted design and pleated front - perfect for ANY body type and occasion.\n", 211 | "\n", 212 | "💎 ELEVATE your look now and step out in confidence with our Wide-leg Trousers! 💎\n", 213 | "Introducing the Ultimate Style Booster: The Cropped Denim Jacket! 💥👕\n", 214 | "\n", 215 | "Upgrade your wardrobe game with this must-have stylish cropped denim jacket. Turn heads wherever you go 🏃‍♀️💨, while adding an edgy touch to your favorite outfits! 😎💃\n", 216 | "\n", 217 | "Featuring a classic collar, suave button-front closure, and slick chest button-flap pockets, this masterpiece is perfect for rocking with tops or dresses. Layer it, flaunt it, own it! 💯🔥\n", 218 | "\n", 219 | "Get yours now and experience the style revolution. 🌟🛍️ Be chic. Be bold. Be unstoppable with the Cropped Denim Jacket! 💫🔝\n", 220 | "Introducing the Front-tie Fantasy!\n", 221 | "\n", 222 | "💃Swirl and twirl in our beautifully chic Front-tie Blouse!\n", 223 | "🌟Transform your look with one fabulous piece!\n", 224 | "❤︎Deep V-neckline: Flirty meets sophistication!\n", 225 | "🦋Flutter sleeves: Add a touch of whimsy!\n", 226 | "🔝Perfect for ANY occasion, dress it up or down!\n", 227 | "🍃Light, semi-sheer fabric: Float through your day with ease!\n", 228 | "\n", 229 | "Get ready to turn heads - upgrade your wardrobe TODAY with our Front-tie Fantasy Blouse! ✨\n", 230 | "Introducing the Maxi Skirt: Elegance Elevated!\n", 231 | "\n", 232 | "Seeking a wardrobe staple to transform your style? Look no further! ✨\n", 233 | "\n", 234 | "Crafted from lightweight, flowing fabric, our Maxi Skirt promises an elegantly effortless silhouette, perfect for any occasion. 💃\n", 235 | "\n", 236 | "Featuring a chic high-waist design and a sultry side-slit, this skirt adds sophistication to your look, whether it's a casual brunch or a night on the town. 🥂\n", 237 | "\n", 238 | "Upgrade your wardrobe with the Maxi Skirt and experience elegance elevated! ✨\n", 239 | "\n", 240 | "Get yours today and make every step a statement. 💖\n" 241 | ] 242 | } 243 | ], 244 | "source": [ 245 | "ads = []\n", 246 | "for item in clothing_items:\n", 247 | " user_prompt = {\"role\" : \"user\", \"content\" : f\"Product: {item['name']} Description: {item['description']}\"}\n", 248 | " ads.append(prompter.prompt_model_return([system_prompt, user_prompt]))\n", 249 | " print(ads[-1])" 250 | ] 251 | }, 252 | { 253 | "cell_type": "markdown", 254 | "source": [ 255 | "Lets install and use huggingface_hub to push our data to the hub!" 256 | ], 257 | "metadata": { 258 | "id": "qbDpLMZZoV2l" 259 | } 260 | }, 261 | { 262 | "cell_type": "code", 263 | "source": [ 264 | "!pip install huggingface_hub" 265 | ], 266 | "metadata": { 267 | "colab": { 268 | "base_uri": "https://localhost:8080/" 269 | }, 270 | "id": "C5HrUWPXoU7k", 271 | "outputId": "b40c86a9-6db9-4611-fc7a-cabac78cb318" 272 | }, 273 | "execution_count": null, 274 | "outputs": [ 275 | { 276 | "output_type": "stream", 277 | "name": "stdout", 278 | "text": [ 279 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", 280 | "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.9/dist-packages (0.13.3)\n", 281 | "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (4.65.0)\n", 282 | "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (2.27.1)\n", 283 | "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (3.10.7)\n", 284 | "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (6.0)\n", 285 | "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (4.5.0)\n", 286 | "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (23.0)\n", 287 | "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (1.26.15)\n", 288 | "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (2022.12.7)\n", 289 | "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (3.4)\n", 290 | "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (2.0.12)\n" 291 | ] 292 | } 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "source": [ 298 | "Now we can log-in to Hugging Face!\n", 299 | "\n", 300 | "Make sure you have a Hugging Face account, and you have set up a read/write token!\n", 301 | "\n", 302 | "More info here: https://huggingface.co/docs/hub/security-tokens" 303 | ], 304 | "metadata": { 305 | "id": "AsYiou0LoiyX" 306 | } 307 | }, 308 | { 309 | "cell_type": "code", 310 | "source": [ 311 | "!huggingface-cli login" 312 | ], 313 | "metadata": { 314 | "id": "i_fslrEAocXQ", 315 | "colab": { 316 | "base_uri": "https://localhost:8080/" 317 | }, 318 | "outputId": "0e72cec0-d725-477c-9817-be0087785a1f" 319 | }, 320 | "execution_count": null, 321 | "outputs": [ 322 | { 323 | "output_type": "stream", 324 | "name": "stdout", 325 | "text": [ 326 | "\n", 327 | " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", 328 | " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", 329 | " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", 330 | " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", 331 | " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", 332 | " \n", 333 | " A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n", 334 | " Setting a new token will erase the existing one.\n", 335 | " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", 336 | "Token: \n", 337 | "Add token as git credential? (Y/n) n\n", 338 | "Token is valid.\n", 339 | "Your token has been saved to /root/.cache/huggingface/token\n", 340 | "Login successful\n" 341 | ] 342 | } 343 | ] 344 | }, 345 | { 346 | "cell_type": "markdown", 347 | "source": [ 348 | "Now we can load our data into the desired format - and upload it to the hub!" 349 | ], 350 | "metadata": { 351 | "id": "dzcw9DnAo1u2" 352 | } 353 | }, 354 | { 355 | "cell_type": "code", 356 | "source": [ 357 | "def create_dataset(clothing_items, ads):\n", 358 | " for clothing, ads in zip(clothing_items, ads):\n", 359 | " clothing[\"ad\"] = ads\n", 360 | "\n", 361 | " return clothing_items" 362 | ], 363 | "metadata": { 364 | "id": "ATrWOfqIo43n" 365 | }, 366 | "execution_count": null, 367 | "outputs": [] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "source": [ 372 | "dataset = create_dataset(clothing_items, ads)" 373 | ], 374 | "metadata": { 375 | "id": "gR6CmMSUpWg5" 376 | }, 377 | "execution_count": null, 378 | "outputs": [] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "source": [ 383 | "!pip install datasets" 384 | ], 385 | "metadata": { 386 | "id": "k-W6NSjrrSt0" 387 | }, 388 | "execution_count": null, 389 | "outputs": [] 390 | }, 391 | { 392 | "cell_type": "code", 393 | "source": [ 394 | "from datasets import load_dataset, Dataset\n", 395 | "import pandas as pd" 396 | ], 397 | "metadata": { 398 | "id": "319x0ZlJrNrZ" 399 | }, 400 | "execution_count": null, 401 | "outputs": [] 402 | }, 403 | { 404 | "cell_type": "code", 405 | "source": [ 406 | "hf_dataset = Dataset.from_pandas(pd.DataFrame(data=dataset))" 407 | ], 408 | "metadata": { 409 | "id": "ohM2ANNFrYxL" 410 | }, 411 | "execution_count": null, 412 | "outputs": [] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "source": [ 417 | "hf_dataset" 418 | ], 419 | "metadata": { 420 | "colab": { 421 | "base_uri": "https://localhost:8080/" 422 | }, 423 | "id": "XOQgiUutxH8a", 424 | "outputId": "d48d39e3-83d0-4475-95d1-970282cbc396" 425 | }, 426 | "execution_count": null, 427 | "outputs": [ 428 | { 429 | "output_type": "execute_result", 430 | "data": { 431 | "text/plain": [ 432 | "Dataset({\n", 433 | " features: ['name', 'description', 'ad'],\n", 434 | " num_rows: 5\n", 435 | "})" 436 | ] 437 | }, 438 | "metadata": {}, 439 | "execution_count": 32 440 | } 441 | ] 442 | }, 443 | { 444 | "cell_type": "code", 445 | "source": [ 446 | "hf_username = \"\"\n", 447 | "\n", 448 | "hf_dataset.push_to_hub(f\"{hf_username}/cool_new_dataset\")" 449 | ], 450 | "metadata": { 451 | "id": "_wRUnFg0xIy5" 452 | }, 453 | "execution_count": null, 454 | "outputs": [] 455 | } 456 | ], 457 | "metadata": { 458 | "kernelspec": { 459 | "display_name": "open_ai", 460 | "language": "python", 461 | "name": "python3" 462 | }, 463 | "language_info": { 464 | "codemirror_mode": { 465 | "name": "ipython", 466 | "version": 3 467 | }, 468 | "file_extension": ".py", 469 | "mimetype": "text/x-python", 470 | "name": "python", 471 | "nbconvert_exporter": "python", 472 | "pygments_lexer": "ipython3", 473 | "version": "3.10.10" 474 | }, 475 | "orig_nbformat": 4, 476 | "colab": { 477 | "provenance": [] 478 | } 479 | }, 480 | "nbformat": 4, 481 | "nbformat_minor": 0 482 | } -------------------------------------------------------------------------------- /TalkToMyDoc - LangChain/notebook/🗣️TalkToMyDoc📄_with_LangChain.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [] 7 | }, 8 | "kernelspec": { 9 | "name": "python3", 10 | "display_name": "Python 3" 11 | }, 12 | "language_info": { 13 | "name": "python" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "markdown", 19 | "source": [ 20 | "### The Basics of LangChain\n", 21 | "\n", 22 | "In this notebook we'll explore exactly what LangChain is doing - and implement a straightforward example that lets us ask questions of any Arxiv.org paper we want!\n", 23 | "\n", 24 | "First things first, let's get our dependencies all set!" 25 | ], 26 | "metadata": { 27 | "id": "kEKghJQ2pmYH" 28 | } 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": { 34 | "id": "fXsYHTgvnCM2", 35 | "colab": { 36 | "base_uri": "https://localhost:8080/" 37 | }, 38 | "outputId": "fb8b6ce6-9887-46e0-8d1a-96f678bee85d" 39 | }, 40 | "outputs": [ 41 | { 42 | "output_type": "stream", 43 | "name": "stdout", 44 | "text": [ 45 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.3/70.3 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 46 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m645.3/645.3 kB\u001b[0m \u001b[31m44.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 47 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m47.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 48 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 49 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 50 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 51 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m269.4/269.4 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 52 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 53 | "\u001b[?25h" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "!pip install openai langchain -q" 59 | ] 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "source": [ 64 | "You'll need to have an OpenAI API key for this next part - see [this](https://www.onmsft.com/how-to/how-to-get-an-openai-api-key/) if you haven't already set one up!" 65 | ], 66 | "metadata": { 67 | "id": "T0sLjfy8p3jf" 68 | } 69 | }, 70 | { 71 | "cell_type": "code", 72 | "source": [ 73 | "import os \n", 74 | "os.environ[\"OPENAI_API_KEY\"] = \"\"" 75 | ], 76 | "metadata": { 77 | "id": "0TTosnCHnGHG" 78 | }, 79 | "execution_count": 3, 80 | "outputs": [] 81 | }, 82 | { 83 | "cell_type": "markdown", 84 | "source": [ 85 | "#### Helper Functions (run this cell)" 86 | ], 87 | "metadata": { 88 | "id": "15M3Jx6SBXcO" 89 | } 90 | }, 91 | { 92 | "cell_type": "code", 93 | "source": [ 94 | "from IPython.display import display, Markdown\n", 95 | "\n", 96 | "def disp_markdown(text: str) -> None:\n", 97 | " display(Markdown(text))" 98 | ], 99 | "metadata": { 100 | "id": "k3SBzWBUpQ21" 101 | }, 102 | "execution_count": 4, 103 | "outputs": [] 104 | }, 105 | { 106 | "cell_type": "markdown", 107 | "source": [ 108 | "### Our First LangChain ChatModel" 109 | ], 110 | "metadata": { 111 | "id": "fU4LWrv-BayH" 112 | } 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "source": [ 117 | "\n", 118 | "\n", 119 | "---\n", 120 | "\n", 121 | "\n", 122 | "
Note: Information on OpenAI's pricing and usage policies.
\n", 123 | "\n", 124 | "\n", 125 | "\n", 126 | "---\n", 127 | "\n" 128 | ], 129 | "metadata": { 130 | "id": "p-M-VQhQOC1c" 131 | } 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "source": [ 136 | "Now that we're set-up with OpenAI's API - we can begin making our first ChatModel!\n", 137 | "\n", 138 | "There's a few important things to consider when we're using LangChain's ChatModel that are outlined [here](https://python.langchain.com/en/latest/modules/models/chat.html)\n", 139 | "\n", 140 | "Let's begin by initializing the model with OpenAI's `gpt-3.5-turbo` (ChatGPT) model.\n", 141 | "\n", 142 | "We're not going to be leveraging the [streaming](https://python.langchain.com/en/latest/modules/models/chat/examples/streaming.html) capabilities in this Notebook - just the basics to get us started!" 143 | ], 144 | "metadata": { 145 | "id": "XVkfqk4NOFWS" 146 | } 147 | }, 148 | { 149 | "cell_type": "code", 150 | "source": [ 151 | "from langchain.chat_models import ChatOpenAI\n", 152 | "from langchain.schema import HumanMessage\n", 153 | "\n", 154 | "chat_model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")" 155 | ], 156 | "metadata": { 157 | "id": "tNscLft_nxBb" 158 | }, 159 | "execution_count": 5, 160 | "outputs": [] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "source": [ 165 | "If we look at the [Chat completions](https://platform.openai.com/docs/guides/chat) documentation for OpenAI's chat models - we'll see that there are a few specific fields we'll need to concern ourselves with:\n", 166 | "\n", 167 | "`role`\n", 168 | "- This refers to one of three \"roles\" that interact with the model in specific ways.\n", 169 | "- The `system` role is an optional role that can be used to guide the model toward a specific task. Examples of `system` messages might be: \n", 170 | " - You are an expert in Python, please answer questions as though we were in a peer coding session.\n", 171 | " - You are the world's leading expert in stamps.\n", 172 | "\n", 173 | " These messages help us \"prime\" the model to be more aligned with our desired task!\n", 174 | "\n", 175 | "- The `user` role represents, well, the user!\n", 176 | "- The `assistant` role lets us act in the place of the model's outputs. We can (and will) leverage this for some few-shot prompt engineering!\n", 177 | "\n", 178 | "Each of these roles has a class in LangChain to make it nice and easy for us to use! \n", 179 | "\n", 180 | "Let's look at an example." 181 | ], 182 | "metadata": { 183 | "id": "vzGhlpwUPyU9" 184 | } 185 | }, 186 | { 187 | "cell_type": "code", 188 | "source": [ 189 | "from langchain.schema import (\n", 190 | " AIMessage, \n", 191 | " HumanMessage,\n", 192 | " SystemMessage\n", 193 | ")\n", 194 | "\n", 195 | "# The SystemMessage is associated with the system role\n", 196 | "system_message = SystemMessage(content=\"You are a food critic.\")\n", 197 | "\n", 198 | "# The HumanMessage is associated with the user role\n", 199 | "user_message = HumanMessage(content=\"Do you think Kraft Dinner constitues fine dining?\")\n", 200 | "\n", 201 | "# The AIMessage is associated with the assistant role\n", 202 | "assistant_message = AIMessage(content=\"Egads! No, it most certainly does not!\")" 203 | ], 204 | "metadata": { 205 | "id": "dM7lciZtoPEp" 206 | }, 207 | "execution_count": null, 208 | "outputs": [] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "source": [ 213 | "Now that we have those messages set-up, let's send them to `gpt-3.5-turbo` with a new user message and see how it does!\n", 214 | "\n", 215 | "It's easy enough to do this - the ChatOpenAI model accepts a list of inputs!" 216 | ], 217 | "metadata": { 218 | "id": "dSx5HBgjSUvB" 219 | } 220 | }, 221 | { 222 | "cell_type": "code", 223 | "source": [ 224 | "second_user_message = HumanMessage(content=\"What about Red Lobster, surely that is fine dining!\")\n", 225 | "\n", 226 | "# create the list of prompts\n", 227 | "list_of_prompts = [\n", 228 | " system_message,\n", 229 | " user_message,\n", 230 | " assistant_message,\n", 231 | " second_user_message\n", 232 | "]\n", 233 | "\n", 234 | "# we can just call our chat_model on the list of prompts!\n", 235 | "chat_model(list_of_prompts)" 236 | ], 237 | "metadata": { 238 | "colab": { 239 | "base_uri": "https://localhost:8080/" 240 | }, 241 | "id": "LwDLOYOKSTpG", 242 | "outputId": "94160cfc-c5b6-4825-8838-32e2476bcb73" 243 | }, 244 | "execution_count": null, 245 | "outputs": [ 246 | { 247 | "output_type": "execute_result", 248 | "data": { 249 | "text/plain": [ 250 | "AIMessage(content='While Red Lobster is a popular chain restaurant known for its seafood, it is not typically considered fine dining. Fine dining typically refers to restaurants with upscale decor, high-quality ingredients, and expertly crafted dishes that are often served with a higher level of service. While Red Lobster may have some of these elements, it is more of a casual dining experience.', additional_kwargs={})" 251 | ] 252 | }, 253 | "metadata": {}, 254 | "execution_count": 26 255 | } 256 | ] 257 | }, 258 | { 259 | "cell_type": "markdown", 260 | "source": [ 261 | "Great! That's inline with what we expected to see!" 262 | ], 263 | "metadata": { 264 | "id": "pZMYJDWXTkMq" 265 | } 266 | }, 267 | { 268 | "cell_type": "markdown", 269 | "source": [ 270 | "### PromptTemplates\n", 271 | "\n", 272 | "Next stop, we'll discuss a few templates. This allows us to easily interact with our model by not having to redo work we've already completed!" 273 | ], 274 | "metadata": { 275 | "id": "8DUNhabQUB8f" 276 | } 277 | }, 278 | { 279 | "cell_type": "code", 280 | "source": [ 281 | "from langchain.prompts.chat import (\n", 282 | " ChatPromptTemplate,\n", 283 | " SystemMessagePromptTemplate,\n", 284 | " HumanMessagePromptTemplate\n", 285 | ")\n", 286 | "\n", 287 | "# we can signify variables we want access to by wrapping them in {}\n", 288 | "system_prompt_template = \"You are an expert in {SUBJECT}, and you're currently feeling {MOOD}\"\n", 289 | "system_prompt_template = SystemMessagePromptTemplate.from_template(system_prompt_template)\n", 290 | "\n", 291 | "user_prompt_template = \"{CONTENT}\"\n", 292 | "user_prompt_template = HumanMessagePromptTemplate.from_template(user_prompt_template)\n", 293 | "\n", 294 | "# put them together into a ChatPromptTemplate\n", 295 | "chat_prompt = ChatPromptTemplate.from_messages([system_prompt_template, user_prompt_template])" 296 | ], 297 | "metadata": { 298 | "id": "74vpojywT0-4" 299 | }, 300 | "execution_count": null, 301 | "outputs": [] 302 | }, 303 | { 304 | "cell_type": "markdown", 305 | "source": [ 306 | "Now that we have our `chat_prompt` set-up with the templates - let's see how we can easily format them with our content!\n", 307 | "\n", 308 | "NOTE: `disp_markdown` is just a helper function to display the formatted markdown response." 309 | ], 310 | "metadata": { 311 | "id": "a-nbEW-kV_na" 312 | } 313 | }, 314 | { 315 | "cell_type": "code", 316 | "source": [ 317 | "# note the method `to_messages()`, that's what converts our formatted prompt into \n", 318 | "formatted_chat_prompt = chat_prompt.format_prompt(SUBJECT=\"cheeses\", MOOD=\"quite tired\", CONTENT=\"Hi, what are the finest cheeses?\").to_messages()\n", 319 | "\n", 320 | "disp_markdown(chat_model(formatted_chat_prompt).content)" 321 | ], 322 | "metadata": { 323 | "colab": { 324 | "base_uri": "https://localhost:8080/", 325 | "height": 337 326 | }, 327 | "id": "P4vd-W2FV7Xq", 328 | "outputId": "2c4bda02-7ba3-4d72-f6d2-dddeed8c3385" 329 | }, 330 | "execution_count": null, 331 | "outputs": [ 332 | { 333 | "output_type": "display_data", 334 | "data": { 335 | "text/plain": [ 336 | "" 337 | ], 338 | "text/markdown": "Hello! There are many fine cheeses from around the world, and it's difficult to narrow it down to just a few. However, here are some of the finest cheeses that you might want to try:\n\n1. Parmigiano Reggiano - an Italian hard cheese with a nutty and salty flavor.\n\n2. Roquefort - a French blue cheese with a creamy texture and a sharp, tangy flavor.\n\n3. Gouda - a Dutch cheese with a buttery, nutty flavor and a smooth texture.\n\n4. Manchego - a Spanish sheep's milk cheese with a sharp, nutty flavor.\n\n5. Brie - a French soft cheese with a creamy texture and a mild, buttery flavor.\n\n6. Cheddar - an English cheese with a sharp, tangy flavor and a crumbly texture.\n\n7. Emmental - a Swiss cheese with a nutty, sweet flavor and large holes.\n\n8. Camembert - a French soft cheese with a creamy texture and a rich, earthy flavor.\n\n9. Feta - a Greek cheese with a tangy, salty flavor and a crumbly texture.\n\n10. Mozzarella - an Italian cheese with a mild, creamy flavor and a stretchy texture.\n\nThese are just a few examples of some of the finest cheeses in the world. There are so many more to discover and enjoy!" 339 | }, 340 | "metadata": {} 341 | } 342 | ] 343 | }, 344 | { 345 | "cell_type": "markdown", 346 | "source": [ 347 | "### Putting the Chain in LangChain\n", 348 | "\n", 349 | "In essense, a chain is exactly as it sounds - it helps us chain actions together.\n", 350 | "\n", 351 | "Let's take a look at an example." 352 | ], 353 | "metadata": { 354 | "id": "hHehNFjAXbU_" 355 | } 356 | }, 357 | { 358 | "cell_type": "code", 359 | "source": [ 360 | "from langchain.chains import LLMChain\n", 361 | "\n", 362 | "chain = LLMChain(llm=chat_model, prompt=chat_prompt)\n", 363 | "\n", 364 | "disp_markdown(chain.run(SUBJECT=\"classic cars\", MOOD=\"angry\", CONTENT=\"Is the 67 Chevrolet Impala a good vehicle?\"))" 365 | ], 366 | "metadata": { 367 | "colab": { 368 | "base_uri": "https://localhost:8080/", 369 | "height": 163 370 | }, 371 | "id": "lTzw4ZMoWX0X", 372 | "outputId": "ed63bd6c-a682-4a95-fbc8-ad5fc678574f" 373 | }, 374 | "execution_count": null, 375 | "outputs": [ 376 | { 377 | "output_type": "display_data", 378 | "data": { 379 | "text/plain": [ 380 | "" 381 | ], 382 | "text/markdown": "As an AI language model, I do not have emotions and therefore cannot feel angry. However, I can provide you with information about the 1967 Chevrolet Impala.\n\nThe 1967 Chevrolet Impala is considered one of the most iconic American classic cars. It was a full-size car that was available in a variety of body styles, including a convertible, coupe, and sedan. The Impala featured a powerful V8 engine and a comfortable interior, making it a popular choice for those who wanted a combination of performance and luxury.\n\nOverall, the 1967 Chevrolet Impala is considered a good vehicle. It has a strong reputation for reliability, and its classic styling has made it a popular choice among collectors and enthusiasts alike. However, as with any classic car, maintenance and repairs can be costly, so it's important to do your research and make sure you're prepared for the investment before making a purchase." 383 | }, 384 | "metadata": {} 385 | } 386 | ] 387 | }, 388 | { 389 | "cell_type": "markdown", 390 | "source": [ 391 | "### Index Local Files\n", 392 | "\n", 393 | "Now that we've got our first chain running, let's talk about indexing and what we can do with it!\n", 394 | "\n", 395 | "For the purposes of this tutorial, we'll be using the word \"index\" to refer to a collection of documents organized in a way that is easy for LangChain to access them as a \"Retriever\".\n", 396 | "\n", 397 | "Let's check out the Retriever set-up! First, a new dependency!" 398 | ], 399 | "metadata": { 400 | "id": "Md5XYaAj_t51" 401 | } 402 | }, 403 | { 404 | "cell_type": "code", 405 | "source": [ 406 | "!pip install chromadb tiktoken nltk -q" 407 | ], 408 | "metadata": { 409 | "id": "7mkmPs3GAQMp" 410 | }, 411 | "execution_count": 34, 412 | "outputs": [] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "source": [ 417 | "import nltk \n", 418 | "nltk.download('punkt')" 419 | ], 420 | "metadata": { 421 | "colab": { 422 | "base_uri": "https://localhost:8080/" 423 | }, 424 | "id": "_dNp7hVQGLFn", 425 | "outputId": "42185adb-4e14-4dc7-fee9-6f5d320d6c35" 426 | }, 427 | "execution_count": 35, 428 | "outputs": [ 429 | { 430 | "output_type": "stream", 431 | "name": "stderr", 432 | "text": [ 433 | "[nltk_data] Downloading package punkt to /root/nltk_data...\n", 434 | "[nltk_data] Unzipping tokenizers/punkt.zip.\n" 435 | ] 436 | }, 437 | { 438 | "output_type": "execute_result", 439 | "data": { 440 | "text/plain": [ 441 | "True" 442 | ] 443 | }, 444 | "metadata": {}, 445 | "execution_count": 35 446 | } 447 | ] 448 | }, 449 | { 450 | "cell_type": "markdown", 451 | "source": [ 452 | "Before we can get started with our chain - we'll have to include some kind of text that we want to include as potential context. \n", 453 | "\n", 454 | "Let's use Douglas Adam's [The Hitch Hiker's Guide to the Galaxy](https://erki.lap.ee/failid/raamatud/guide1.txt) as our text file. " 455 | ], 456 | "metadata": { 457 | "id": "MFe3eeJTB37W" 458 | } 459 | }, 460 | { 461 | "cell_type": "code", 462 | "source": [ 463 | "%pwd" 464 | ], 465 | "metadata": { 466 | "colab": { 467 | "base_uri": "https://localhost:8080/", 468 | "height": 35 469 | }, 470 | "id": "vWU68TL2Acpe", 471 | "outputId": "b3f0ce64-a859-47e0-92d8-361d32414a01" 472 | }, 473 | "execution_count": 6, 474 | "outputs": [ 475 | { 476 | "output_type": "execute_result", 477 | "data": { 478 | "text/plain": [ 479 | "'/content'" 480 | ], 481 | "application/vnd.google.colaboratory.intrinsic+json": { 482 | "type": "string" 483 | } 484 | }, 485 | "metadata": {}, 486 | "execution_count": 6 487 | } 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "source": [ 493 | "!wget https://erki.lap.ee/failid/raamatud/guide1.txt" 494 | ], 495 | "metadata": { 496 | "colab": { 497 | "base_uri": "https://localhost:8080/" 498 | }, 499 | "id": "jdkQUoQICVwa", 500 | "outputId": "5d5f7253-f994-4919-aa81-3801641cca3b" 501 | }, 502 | "execution_count": 7, 503 | "outputs": [ 504 | { 505 | "output_type": "stream", 506 | "name": "stdout", 507 | "text": [ 508 | "--2023-04-26 14:48:29-- https://erki.lap.ee/failid/raamatud/guide1.txt\n", 509 | "Resolving erki.lap.ee (erki.lap.ee)... 185.158.177.102\n", 510 | "Connecting to erki.lap.ee (erki.lap.ee)|185.158.177.102|:443... connected.\n", 511 | "HTTP request sent, awaiting response... 200 OK\n", 512 | "Length: 291862 (285K) [text/plain]\n", 513 | "Saving to: ‘guide1.txt’\n", 514 | "\n", 515 | "guide1.txt 100%[===================>] 285.02K 665KB/s in 0.4s \n", 516 | "\n", 517 | "2023-04-26 14:48:30 (665 KB/s) - ‘guide1.txt’ saved [291862/291862]\n", 518 | "\n" 519 | ] 520 | } 521 | ] 522 | }, 523 | { 524 | "cell_type": "code", 525 | "source": [ 526 | "from langchain.document_loaders import TextLoader\n", 527 | "loader = TextLoader('guide1.txt', encoding='utf8')" 528 | ], 529 | "metadata": { 530 | "id": "W7zuITDYCaXo" 531 | }, 532 | "execution_count": 11, 533 | "outputs": [] 534 | }, 535 | { 536 | "cell_type": "markdown", 537 | "source": [ 538 | "Now we can set up our first Index!\n", 539 | "\n", 540 | "More detail can be found [here](https://python.langchain.com/en/latest/modules/indexes/getting_started.html) but we'll skip to a more functional implementation!" 541 | ], 542 | "metadata": { 543 | "id": "xha9YA4wA1-b" 544 | } 545 | }, 546 | { 547 | "cell_type": "code", 548 | "source": [ 549 | "from langchain.indexes import VectorstoreIndexCreator\n", 550 | "index = VectorstoreIndexCreator().from_loaders([loader])" 551 | ], 552 | "metadata": { 553 | "colab": { 554 | "base_uri": "https://localhost:8080/" 555 | }, 556 | "id": "JuuKSPgTB0Uz", 557 | "outputId": "2e914f3a-16df-4db9-9c1c-0c3c694f4831" 558 | }, 559 | "execution_count": 14, 560 | "outputs": [ 561 | { 562 | "output_type": "stream", 563 | "name": "stderr", 564 | "text": [ 565 | "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n" 566 | ] 567 | } 568 | ] 569 | }, 570 | { 571 | "cell_type": "markdown", 572 | "source": [ 573 | "Now that we have our Index set-up, we can query it straight away!" 574 | ], 575 | "metadata": { 576 | "id": "-nQNNB8NC-XP" 577 | } 578 | }, 579 | { 580 | "cell_type": "code", 581 | "source": [ 582 | "query = \"What are the importances of towels?\"\n", 583 | "index.query_with_sources(query)" 584 | ], 585 | "metadata": { 586 | "colab": { 587 | "base_uri": "https://localhost:8080/" 588 | }, 589 | "id": "s7vSgpomC9n9", 590 | "outputId": "82c4cb02-d54b-4e31-e5bc-d1502b9db26c" 591 | }, 592 | "execution_count": 18, 593 | "outputs": [ 594 | { 595 | "output_type": "execute_result", 596 | "data": { 597 | "text/plain": [ 598 | "{'question': 'What are the importances of towels?',\n", 599 | " 'answer': ' Towels are important because they have immense psychological value, can be used for warmth, protection, and distress signals, and can be used to indicate that a person is well-prepared for a journey.\\n',\n", 600 | " 'sources': 'guide1.txt'}" 601 | ] 602 | }, 603 | "metadata": {}, 604 | "execution_count": 18 605 | } 606 | ] 607 | }, 608 | { 609 | "cell_type": "markdown", 610 | "source": [ 611 | "### Putting it All Together\n", 612 | "\n", 613 | "Now that we have a simple idea of how we prompt, what a chain is, and what an index is - let's put it all together!" 614 | ], 615 | "metadata": { 616 | "id": "5F5tv4ULERDo" 617 | } 618 | }, 619 | { 620 | "cell_type": "code", 621 | "source": [ 622 | "from langchain.embeddings.openai import OpenAIEmbeddings\n", 623 | "from langchain.text_splitter import NLTKTextSplitter\n", 624 | "from langchain.vectorstores import Chroma\n", 625 | "from langchain.docstore.document import Document\n", 626 | "from langchain.prompts import PromptTemplate\n", 627 | "from langchain.indexes.vectorstore import VectorstoreIndexCreator\n", 628 | "\n", 629 | "with open(\"guide1.txt\") as f:\n", 630 | " hitchhikersguide = f.read()\n", 631 | "\n", 632 | "text_splitter = NLTKTextSplitter()\n", 633 | "texts = text_splitter.split_text(hitchhikersguide)\n", 634 | "\n", 635 | "embeddings = OpenAIEmbeddings()" 636 | ], 637 | "metadata": { 638 | "id": "uAHJHsksEx5H" 639 | }, 640 | "execution_count": 36, 641 | "outputs": [] 642 | }, 643 | { 644 | "cell_type": "code", 645 | "source": [ 646 | "docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": str(i)} for i in range(len(texts))]).as_retriever()" 647 | ], 648 | "metadata": { 649 | "colab": { 650 | "base_uri": "https://localhost:8080/" 651 | }, 652 | "id": "4Z796M-aE_lU", 653 | "outputId": "043daf5f-e3fa-456c-a581-cd7c17eb2a01" 654 | }, 655 | "execution_count": 37, 656 | "outputs": [ 657 | { 658 | "output_type": "stream", 659 | "name": "stderr", 660 | "text": [ 661 | "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n" 662 | ] 663 | } 664 | ] 665 | }, 666 | { 667 | "cell_type": "code", 668 | "source": [ 669 | "from langchain.chains.question_answering import load_qa_chain\n", 670 | "from langchain.llms import OpenAI\n", 671 | "\n", 672 | "chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")\n", 673 | "query = \"Why are towels important?\"\n", 674 | "docs = docsearch.get_relevant_documents(query)\n", 675 | "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)" 676 | ], 677 | "metadata": { 678 | "colab": { 679 | "base_uri": "https://localhost:8080/" 680 | }, 681 | "id": "SsEM-dR1FEvp", 682 | "outputId": "64126b63-e825-4226-b923-8cf304c7aba7" 683 | }, 684 | "execution_count": 40, 685 | "outputs": [ 686 | { 687 | "output_type": "execute_result", 688 | "data": { 689 | "text/plain": [ 690 | "{'output_text': '\\n\\nTowels are important because they have both practical and psychological value. In the context of the story, Arthur Dent uses a towel to protect himself from a bulldozer in his garden. Towels can also be used for warmth, as a distress signal, for hand-to-hand combat, and to ward off noxious fumes. They also have psychological value, as a strag (non-hitch hiker) will assume that a hitch hiker who has a towel with them is also in possession of other items such as a toothbrush, face flannel, soap, tin of biscuits, flask, compass, map, ball of string, gnat spray, wet weather gear, and a space suit. This gives the hitch hiker a sense of respect and admiration. Towels can also be used to protect oneself from the elements, such as when Arthur Dent used a towel to protect himself from a bulldozer in his garden. In addition, towels can be used to dry off after a swim, to mop up spills, and to provide a comfortable place to sit or lie down. Towels can also be used to protect oneself from the elements, such as when Arthur Dent used a towel to protect himself from a bulldozer in his garden. Furthermore'}" 691 | ] 692 | }, 693 | "metadata": {}, 694 | "execution_count": 40 695 | } 696 | ] 697 | } 698 | ] 699 | } -------------------------------------------------------------------------------- /MarketMail-AI/notebooks/✉️_MarketMail_AI_✉️_Fine_tuning_BLOOM.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "WE5GJ6s7y0Xo" 7 | }, 8 | "source": [ 9 | "# Fine-tune a BLOOM-based ad generation model using `peft`, `transformers` and `bitsandbytes`\n", 10 | "\n", 11 | "We can use the [MarketMail-AI dataset](https://huggingface.co/datasets/FourthBrainGenAI/MarketMail-AI) to fine-tune BLOOM to be able to generate marketing emails based off of a product and its description!" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "source": [ 17 | "### Overview of PEFT and LoRA:\n", 18 | "\n", 19 | "Based on some awesome new research [here](https://github.com/huggingface/peft), we can leverage techniques like PEFT and LoRA to train/fine-tune large models a lot more efficiently. \n", 20 | "\n", 21 | "It can't be explained much better than the overview given in the above link: \n", 22 | "\n", 23 | "```\n", 24 | "Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of\n", 25 | "pre-trained language models (PLMs) to various downstream applications without \n", 26 | "fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often \n", 27 | "prohibitively costly. In this regard, PEFT methods only fine-tune a small \n", 28 | "number of (extra) model parameters, thereby greatly decreasing the \n", 29 | "computational and storage costs. Recent State-of-the-Art PEFT techniques \n", 30 | "achieve performance comparable to that of full fine-tuning.\n", 31 | "```" 32 | ], 33 | "metadata": { 34 | "id": "gahXSeKIkc3N" 35 | } 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": { 40 | "id": "TfBzP8gWzkpv" 41 | }, 42 | "source": [ 43 | "### Install requirements\n", 44 | "\n", 45 | "First, run the cells below to install the requirements:" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 2, 51 | "metadata": { 52 | "colab": { 53 | "base_uri": "https://localhost:8080/" 54 | }, 55 | "id": "otj46qRbtpnd", 56 | "outputId": "b7fca22d-282f-47cd-be1f-06f8eb0c274f" 57 | }, 58 | "outputs": [ 59 | { 60 | "output_type": "stream", 61 | "name": "stdout", 62 | "text": [ 63 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.3/104.3 MB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 64 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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\u001b[36m0:00:00\u001b[0m\n", 76 | "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", 77 | " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", 78 | " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", 79 | " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" 80 | ] 81 | } 82 | ], 83 | "source": [ 84 | "!pip install -q bitsandbytes datasets accelerate loralib transformers\n", 85 | "!pip install -q git+https://github.com/huggingface/peft.git" 86 | ] 87 | }, 88 | { 89 | "cell_type": "markdown", 90 | "metadata": { 91 | "id": "FOtwYRI3zzXI" 92 | }, 93 | "source": [ 94 | "### Model loading\n", 95 | "\n", 96 | "Let's load the `bloom-1b7` model!\n", 97 | "\n", 98 | "We're also going to load the `bigscience/tokenizer` which is the tokenizer for all of the BLOOM models.\n", 99 | "\n", 100 | "This step will take some time, as we have to download the model weights which are ~3.44GB." 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 13, 106 | "metadata": { 107 | "id": "cg3fiQOvmI3Q" 108 | }, 109 | "outputs": [], 110 | "source": [ 111 | "import os\n", 112 | "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n", 113 | "import torch\n", 114 | "import torch.nn as nn\n", 115 | "import bitsandbytes as bnb\n", 116 | "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n", 117 | "\n", 118 | "model = AutoModelForCausalLM.from_pretrained(\n", 119 | " \"bigscience/bloom-1b7\", \n", 120 | " torch_dtype=torch.float16,\n", 121 | " load_in_8bit=True, \n", 122 | " device_map='auto',\n", 123 | ")\n", 124 | "\n", 125 | "tokenizer = AutoTokenizer.from_pretrained(\"bigscience/tokenizer\")" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": { 131 | "id": "9fTSZntA1iUG" 132 | }, 133 | "source": [ 134 | "### Post-processing on the model\n", 135 | "\n", 136 | "Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast the layer-norm in `float32` for stability. We also cast the output of the last layer in `float32` for the same reasons." 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 14, 142 | "metadata": { 143 | "id": "T-gy-LxM0yAi" 144 | }, 145 | "outputs": [], 146 | "source": [ 147 | "for param in model.parameters():\n", 148 | " param.requires_grad = False # freeze the model - train adapters later\n", 149 | " if param.ndim == 1:\n", 150 | " # cast the small parameters (e.g. layernorm) to fp32 for stability\n", 151 | " param.data = param.data.to(torch.float32)\n", 152 | "\n", 153 | "model.gradient_checkpointing_enable() # reduce number of stored activations\n", 154 | "model.enable_input_require_grads()\n", 155 | "\n", 156 | "class CastOutputToFloat(nn.Sequential):\n", 157 | " def forward(self, x): return super().forward(x).to(torch.float32)\n", 158 | "model.lm_head = CastOutputToFloat(model.lm_head)" 159 | ] 160 | }, 161 | { 162 | "cell_type": "markdown", 163 | "metadata": { 164 | "id": "KwOTr7B3NlM3" 165 | }, 166 | "source": [ 167 | "### Apply LoRA\n", 168 | "\n", 169 | "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`." 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 15, 175 | "metadata": { 176 | "id": "4W1j6lxaNnxC" 177 | }, 178 | "outputs": [], 179 | "source": [ 180 | "def print_trainable_parameters(model):\n", 181 | " \"\"\"\n", 182 | " Prints the number of trainable parameters in the model.\n", 183 | " \"\"\"\n", 184 | " trainable_params = 0\n", 185 | " all_param = 0\n", 186 | " for _, param in model.named_parameters():\n", 187 | " all_param += param.numel()\n", 188 | " if param.requires_grad:\n", 189 | " trainable_params += param.numel()\n", 190 | " print(\n", 191 | " f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n", 192 | " )" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 16, 198 | "metadata": { 199 | "colab": { 200 | "base_uri": "https://localhost:8080/" 201 | }, 202 | "id": "4iwHGzKBN6wk", 203 | "outputId": "9b2e2767-d012-445b-c229-c357c7fdd239" 204 | }, 205 | "outputs": [ 206 | { 207 | "output_type": "stream", 208 | "name": "stdout", 209 | "text": [ 210 | "trainable params: 3145728 || all params: 1725554688 || trainable%: 0.18230242262828822\n" 211 | ] 212 | } 213 | ], 214 | "source": [ 215 | "from peft import LoraConfig, get_peft_model \n", 216 | "\n", 217 | "config = LoraConfig(\n", 218 | " r=16,\n", 219 | " lora_alpha=32,\n", 220 | " target_modules=[\"query_key_value\"],\n", 221 | " lora_dropout=0.05,\n", 222 | " bias=\"none\",\n", 223 | " task_type=\"CAUSAL_LM\"\n", 224 | ")\n", 225 | "\n", 226 | "model = get_peft_model(model, config)\n", 227 | "print_trainable_parameters(model)" 228 | ] 229 | }, 230 | { 231 | "cell_type": "markdown", 232 | "metadata": { 233 | "id": "QdjWif4CVXR6" 234 | }, 235 | "source": [ 236 | "### Preprocessing" 237 | ] 238 | }, 239 | { 240 | "cell_type": "markdown", 241 | "source": [ 242 | "We can simply load our dataset from 🤗 Hugging Face with the `load_dataset` method!" 243 | ], 244 | "metadata": { 245 | "id": "OmB8KTexh4rf" 246 | } 247 | }, 248 | { 249 | "cell_type": "code", 250 | "execution_count": 17, 251 | "metadata": { 252 | "id": "AQ_HCYruWIHU" 253 | }, 254 | "outputs": [], 255 | "source": [ 256 | "import transformers\n", 257 | "from datasets import load_dataset\n", 258 | "\n", 259 | "dataset_name = \"FourthBrainGenAI/MarketMail-AI-Dataset\"\n", 260 | "product_name = \"product\"\n", 261 | "product_desc = \"description\"\n", 262 | "product_ad = \"marketing_email\"" 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "source": [ 268 | "dataset = load_dataset(dataset_name)\n", 269 | "print(dataset)\n", 270 | "print(dataset['train'][0])" 271 | ], 272 | "metadata": { 273 | "id": "Ap6OE6R_W2QZ", 274 | "outputId": "02fc8a36-c91e-491b-de9b-91e79bdb44f3", 275 | "colab": { 276 | "base_uri": "https://localhost:8080/", 277 | "height": 208, 278 | "referenced_widgets": [ 279 | "667bbd65f5104d939979ef6b406535c1", 280 | "2a8be04284fa44ac8381956d2419fe04", 281 | "81b66853b38b49a18c217b0d2e62b045", 282 | "fd2315ae643445669a335465e076bef7", 283 | "520b1a2e9ba24c91996635575542c3f0", 284 | "fa6c6f598015462fa89944ead445654b", 285 | "de840549c7f8433bb57840a74ed338b3", 286 | "612c10fcb5b140a28cc3085d42550e64", 287 | "4787102dfbe1458fa0a597742ea5e04c", 288 | "cba2f10389de40da8bda496a595427dc", 289 | "e57b72c010654fbb9d35f832b3cdbe39" 290 | ] 291 | } 292 | }, 293 | "execution_count": 32, 294 | "outputs": [ 295 | { 296 | "output_type": "stream", 297 | "name": "stderr", 298 | "text": [ 299 | "WARNING:datasets.builder:Found cached dataset parquet (/root/.cache/huggingface/datasets/FourthBrainGenAI___parquet/FourthBrainGenAI--MarketMail-AI-Dataset-a5c26d0083f22d98/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n" 300 | ] 301 | }, 302 | { 303 | "output_type": "display_data", 304 | "data": { 305 | "text/plain": [ 306 | " 0%| | 0/1 [00:00 str:\n", 361 | " prompt = f\"Below is a product and description, please write a marketing email for this product.\\n\\n### Product:\\n{product}\\n### Description:\\n{description}\\n\\n### Marketing Email:\\n{marketing_email}\"\n", 362 | " return prompt\n", 363 | "\n", 364 | "mapped_dataset = dataset.map(lambda samples: tokenizer(generate_prompt(samples['product'], samples['description'], samples['marketing_email'])))" 365 | ], 366 | "metadata": { 367 | "id": "MWZk1U-kXwZF", 368 | "outputId": "bf223ffd-d09a-48ae-fcdd-89c82a15a736", 369 | "colab": { 370 | "base_uri": "https://localhost:8080/", 371 | "height": 17, 372 | "referenced_widgets": [ 373 | "0672126a1aa44f339f85a8d40a4ffde5", 374 | "7aff62de0bb84d3d846b1f311298d7dd", 375 | "1489367efdb1438c83251ee94c27e604", 376 | "785468e813f84d9292cb9f0f27a05132", 377 | "365159fbdbda4cee819b872e29d9a5f7", 378 | "3c227deff8d64dc08788d927eb02d20b", 379 | "55dc4a1ebd6d4d849f81f799d3245043", 380 | "1146c98b526b4b1ca28b80c2bbb521ad", 381 | "d84c2136e65441cf95b2c6bcbc8991f8", 382 | "449d60a1bfaf43fda450b192639e1bcd", 383 | "dcf9f4788e50455badc3c3f697f89705" 384 | ] 385 | } 386 | }, 387 | "execution_count": 34, 388 | "outputs": [ 389 | { 390 | "output_type": "display_data", 391 | "data": { 392 | "text/plain": [ 393 | "Map: 0%| | 0/75 [00:00>\"\n", 473 | "\n", 474 | "model.push_to_hub(f\"{HUGGING_FACE_USER_NAME}/{model_name}\", use_auth_token=True)" 475 | ] 476 | }, 477 | { 478 | "cell_type": "markdown", 479 | "metadata": { 480 | "id": "S65GcxNGA9kz" 481 | }, 482 | "source": [ 483 | "## Load adapters from the Hub\n", 484 | "\n", 485 | "You can also directly load adapters from the Hub using the commands below:" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": null, 491 | "metadata": { 492 | "id": "hsD1VKqeA62Z" 493 | }, 494 | "outputs": [], 495 | "source": [ 496 | "import torch\n", 497 | "from peft import PeftModel, PeftConfig\n", 498 | "from transformers import AutoModelForCausalLM, AutoTokenizer\n", 499 | "\n", 500 | "peft_model_id = f\"{HUGGING_FACE_USER_NAME}/{model_name}\"\n", 501 | "config = PeftConfig.from_pretrained(peft_model_id)\n", 502 | "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')\n", 503 | "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n", 504 | "\n", 505 | "# Load the Lora model\n", 506 | "model = PeftModel.from_pretrained(model, peft_model_id)" 507 | ] 508 | }, 509 | { 510 | "cell_type": "markdown", 511 | "metadata": { 512 | "id": "MHYljmTjj5wX" 513 | }, 514 | "source": [ 515 | "## Inference\n", 516 | "\n", 517 | "You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`." 518 | ] 519 | }, 520 | { 521 | "cell_type": "markdown", 522 | "source": [ 523 | "### Take it for a spin!" 524 | ], 525 | "metadata": { 526 | "id": "WuppXQXWA27h" 527 | } 528 | }, 529 | { 530 | "cell_type": "code", 531 | "source": [ 532 | "from IPython.display import display, Markdown\n", 533 | "\n", 534 | "def make_inference(product, description):\n", 535 | " batch = tokenizer(f\"Below is a product and description, please write a marketing email for this product.\\n\\n### Product:\\n{product}\\n### Description:\\n{description}\\n\\n### Marketing Email:\\n\", return_tensors='pt')\n", 536 | "\n", 537 | " with torch.cuda.amp.autocast():\n", 538 | " output_tokens = model.generate(**batch, max_new_tokens=200)\n", 539 | "\n", 540 | " display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))" 541 | ], 542 | "metadata": { 543 | "id": "whuXcSPsluc5" 544 | }, 545 | "execution_count": null, 546 | "outputs": [] 547 | }, 548 | { 549 | "cell_type": "code", 550 | "source": [ 551 | "your_product_name_here = \"The Coolinator\"\n", 552 | "your_product_description_here = \"A personal cooling device to keep you from getting overheated on a hot summer's day!\"\n", 553 | "\n", 554 | "make_inference(your_product_name_here, your_product_description_here)" 555 | ], 556 | "metadata": { 557 | "id": "E5_d4r1Mm6ud" 558 | }, 559 | "execution_count": null, 560 | "outputs": [] 561 | }, 562 | { 563 | "cell_type": "markdown", 564 | "source": [ 565 | "### Example in Training Set" 566 | ], 567 | "metadata": { 568 | "id": "8jiRI6NUxoRu" 569 | } 570 | }, 571 | { 572 | "cell_type": "code", 573 | "source": [ 574 | "make_inference(\"SmartEyes\", \"Glasses with real-time translation\")" 575 | ], 576 | "metadata": { 577 | "id": "xcrFGC9Hxlno" 578 | }, 579 | "execution_count": null, 580 | "outputs": [] 581 | } 582 | ], 583 | "metadata": { 584 | "accelerator": "GPU", 585 | "colab": { 586 | "machine_shape": "hm", 587 | "provenance": [] 588 | }, 589 | "gpuClass": "premium", 590 | "kernelspec": { 591 | "display_name": "Python 3 (ipykernel)", 592 | "language": "python", 593 | "name": "python3" 594 | }, 595 | "language_info": 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