base on Run Generative AI models with simple C++/Python API and using OpenVINO Runtime # OpenVINO™ GenAI  OpenVINO™ GenAI is a library of the most popular Generative AI model pipelines, optimized execution methods, and samples that run on top of highly performant [OpenVINO Runtime](https://github.com/openvinotoolkit/openvino). This library is friendly to PC and laptop execution, and optimized for resource consumption. It requires no external dependencies to run generative models as it already includes all the core functionality (e.g. tokenization via openvino-tokenizers).  ## Getting Started Please follow the following blogs to setup your first hands-on experience with C++ and Python samples. * [How to Build OpenVINO™ GenAI APP in C++](https://medium.com/openvino-toolkit/how-to-build-openvino-genai-app-in-c-32dcbe42fa67) * [How to run Llama 3.2 locally with OpenVINO™](https://medium.com/openvino-toolkit/how-to-run-llama-3-2-locally-with-openvino-60a0f3674549) ## Supported Generative AI scenarios OpenVINO™ GenAI library provides very lightweight C++ and Python APIs to run following Generative Scenarios: - Text generation using Large Language Models. For example, chat with local LLaMa model - Image generation using Diffuser models, for example, generation using Stable Diffusion models - Speech recognition using Whisper family models - Text generation using Large Visual Models, for instance, Image analysis using LLaVa or miniCPM models family Library efficiently supports LoRA adapters for Text and Image generation scenarios: - Load multiple adapters per model - Select active adapters for every generation - Mix multiple adapters with coefficients via alpha blending All scenarios are run on top of OpenVINO Runtime that supports inference on CPU, GPU and NPU. See [here](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/system-requirements.html) for platform support matrix. ## Supported Generative AI optimization methods OpenVINO™ GenAI library provides a transparent way to use state-of-the-art generation optimizations: - Speculative decoding that employs two models of different sizes and uses the large model to periodically correct the results of the small model. See [here](https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/) for more detailed overview - KVCache token eviction algorithm that reduces the size of the KVCache by pruning less impacting tokens. Additionally, OpenVINO™ GenAI library implements a continuous batching approach to use OpenVINO within LLM serving. Continuous batching library could be used in LLM serving frameworks and supports the following features: - Prefix caching that caches fragments of previous generation requests and corresponding KVCache entries internally and uses them in case of repeated query. See [here](https://google.com) for more detailed overview Continuous batching functionality is used within OpenVINO Model Server (OVMS) to serve LLMs, see [here](https://docs.openvino.ai/2024/ovms_docs_llm_reference.html) for more details. ## Installing OpenVINO GenAI ```sh # Installing OpenVINO GenAI via pip pip install openvino-genai # Install optimum-intel to be able to download, convert and optimize LLMs from Hugging Face # Optimum is not required to run models, only to convert and compress pip install optimum-intel@git+https://github.com/huggingface/optimum-intel.git # (Optional) Install (TBD) to be able to download models from Model Scope ``` ## Performing text generation <details> For more examples check out our [Generative AI workflow](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html) ### Converting and compressing text generation model from Hugging Face library ```sh #(Basic) download and convert to OpenVINO TinyLlama-Chat-v1.0 model optimum-cli export openvino --model "TinyLlama/TinyLlama-1.1B-Chat-v1.0" --weight-format fp16 --trust-remote-code "TinyLlama-1.1B-Chat-v1.0" #(Recommended) download, convert to OpenVINO and compress to int4 TinyLlama-Chat-v1.0 model optimum-cli export openvino --model "TinyLlama/TinyLlama-1.1B-Chat-v1.0" --weight-format int4 --trust-remote-code "TinyLlama-1.1B-Chat-v1.0" ``` ### Run generation using LLMPipeline API in Python ```python import openvino_genai as ov_genai #Will run model on CPU, GPU or NPU are possible options pipe = ov_genai.LLMPipeline("./TinyLlama-1.1B-Chat-v1.0/", "CPU") print(pipe.generate("The Sun is yellow because", max_new_tokens=100)) ``` ### Run generation using LLMPipeline in C++ Code below requires installation of C++ compatible package (see [here](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html#archive-installation) for more details) ```cpp #include "openvino/genai/llm_pipeline.hpp" #include <iostream> int main(int argc, char* argv[]) { std::string models_path = argv[1]; ov::genai::LLMPipeline pipe(models_path, "CPU"); std::cout << pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(100)) << '\n'; } ``` ### Sample notebooks using this API See [here](https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+an+LLM-powered+Chatbot+using+OpenVINO+Generate+API) </details> ## Performing visual language text generation <details> For more examples check out our [Generative AI workflow](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html) ### Converting and compressing the model from Hugging Face library To convert the [OpenGVLab/InternVL2-1B](https://huggingface.co/OpenGVLab/InternVL2-1B) model, `timm` and `einops` are required: `pip install timm einops`. ```sh # Download and convert the OpenGVLab/InternVL2-1B model to OpenVINO with int4 weight-compression for the language model # Other components are compressed to int8 optimum-cli export openvino -m OpenGVLab/InternVL2-1B --trust-remote-code --weight-format int4 InternVL2-1B ``` ### Run generation using VLMPipeline API in Python See [Visual Language Chat](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/visual_language_chat) for a demo application. Run the following command to download a sample image: ```sh curl -O "https://storage.openvinotoolkit.org/test_data/images/dog.jpg" ``` ```python import numpy as np import openvino as ov import openvino_genai as ov_genai from PIL import Image # Choose GPU instead of CPU in the line below to run the model on Intel integrated or discrete GPU pipe = ov_genai.VLMPipeline("./InternVL2-1B", "CPU") image = Image.open("dog.jpg") image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.uint8) image_data = ov.Tensor(image_data) prompt = "Can you describe the image?" print(pipe.generate(prompt, image=image_data, max_new_tokens=100)) ``` ### Run generation using VLMPipeline in C++ Code below requires installation of C++ compatible package (see [here](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html#archive-installation) for more details). See [Visual Language Chat](https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/cpp/visual_language_chat) for a demo application. ```cpp #include "openvino/genai/visual_language/pipeline.hpp" #include "load_image.hpp" #include <iostream> int main(int argc, char* argv[]) { std::string models_path = argv[1]; ov::genai::VLMPipeline pipe(models_path, "CPU"); ov::Tensor rgb = utils::load_image(argv[2]); std::cout << pipe.generate( prompt, ov::genai::image(rgb), ov::genai::max_new_tokens(100) ) << '\n'; } ``` ### Sample notebooks using this API See [here](https://openvinotoolkit.github.io/openvino_notebooks/?search=Visual-language+assistant+with+MiniCPM-V2+and+OpenVINO) </details> ## Performing image generation <details> For more examples check out our [Generative AI workflow](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html) ### Converting and compressing image generation model from Hugging Face library ```sh #Download and convert to OpenVINO dreamlike-anime-1.0 model optimum-cli export openvino --model dreamlike-art/dreamlike-anime-1.0 --weight-format fp16 dreamlike_anime_1_0_ov/FP16 #You can also use INT8 hybrid quantization to further optimize the model and reduce inference latency optimum-cli export openvino --model dreamlike-art/dreamlike-anime-1.0 --weight-format int8 --dataset conceptual_captions dreamlike_anime_1_0_ov/INT8 ``` ### Run generation using Text2Image API in Python ```python import argparse from PIL import Image import openvino_genai device = 'CPU' # GPU can be used as well pipe = openvino_genai.Text2ImagePipeline("./dreamlike_anime_1_0_ov/INT8", device) image_tensor = pipe.generate("cyberpunk cityscape like Tokyo New York with tall buildings at dusk golden hour cinematic lighting") image = Image.fromarray(image_tensor.data[0]) image.save("image.bmp") ``` ### Run generation using Text2Image API in C++ Code below requires installation of C++ compatible package (see [here](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html#archive-installation) for additional setup details, or this blog for full instruction [How to Build OpenVINO™ GenAI APP in C++](https://medium.com/openvino-toolkit/how-to-build-openvino-genai-app-in-c-32dcbe42fa67) ```cpp #include "openvino/genai/image_generation/text2image_pipeline.hpp" #include "imwrite.hpp" int main(int argc, char* argv[]) { const std::string models_path = argv[1], prompt = argv[2]; const std::string device = "CPU"; // GPU can be used as well ov::genai::Text2ImagePipeline pipe(models_path, device); ov::Tensor image = pipe.generate(prompt); imwrite("image.bmp", image, true); } ``` ### Run generation using Image2Image API in Python ```python import argparse from PIL import Image import openvino_genai import openvino as ov device = 'CPU' # GPU can be used as well pipe = openvino_genai.Image2ImagePipeline("./dreamlike_anime_1_0_ov/INT8", device) image = Image.open("small_city.jpg") image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.uint8) image_data = ov.Tensor(image_data) image_tensor = pipe.generate( "cyberpunk cityscape like Tokyo New York with tall buildings at dusk golden hour cinematic lighting", image=image_data, strength=0.8 ) image = Image.fromarray(image_tensor.data[0]) image.save("image.bmp") ``` ### Run generation using Image2Image API in C++ Code below requires installation of C++ compatible package (see [here](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html#archive-installation) for additional setup details, or this blog for full instruction [How to Build OpenVINO™ GenAI APP in C++](https://medium.com/openvino-toolkit/how-to-build-openvino-genai-app-in-c-32dcbe42fa67) ```cpp #include "openvino/genai/image_generation/image2image_pipeline.hpp" #include "load_image.hpp" #include "imwrite.hpp" int main(int argc, char* argv[]) { const std::string models_path = argv[1], prompt = argv[2], image_path = argv[3]; const std::string device = "CPU"; // GPU can be used as well ov::Tensor image = utils::load_image(image_path); ov::genai::Image2ImagePipeline pipe(models_path, device); ov::Tensor generated_image = pipe.generate(prompt, image, ov::genai::strength(0.8f)); imwrite("image.bmp", generated_image, true); } ``` ### Run generation using Inpainting API in Python ```python import argparse from PIL import Image import openvino_genai import openvino as ov def read_image(path: str) -> openvino.Tensor: pic = Image.open(path).convert("RGB") image_data = np.array(pic.getdata()).reshape(1, pic.size[1], pic.size[0], 3).astype(np.uint8) return openvino.Tensor(image_data) device = 'CPU' # GPU can be used as well pipe = openvino_genai.InpaintingPipeline(args.model_dir, device) image = read_image("image.jpg") mask_image = read_image("mask.jpg") image_tensor = pipe.generate( "Face of a yellow cat, high resolution, sitting on a park bench", image=image, mask_image=mask_image ) image = Image.fromarray(image_tensor.data[0]) image.save("image.bmp") ``` ### Run generation using Inpainting API in C++ Code below requires installation of C++ compatible package (see [here](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-genai.html#archive-installation) for additional setup details, or this blog for full instruction [How to Build OpenVINO™ GenAI APP in C++](https://medium.com/openvino-toolkit/how-to-build-openvino-genai-app-in-c-32dcbe42fa67) ```cpp #include "openvino/genai/image_generation/inpainting_pipeline.hpp" #include "load_image.hpp" #include "imwrite.hpp" int main(int argc, char* argv[]) { const std::string models_path = argv[1], prompt = argv[2]; const std::string device = "CPU"; // GPU can be used as well ov::Tensor image = utils::load_image(argv[3]); ov::Tensor mask_image = utils::load_image(argv[4]); ov::genai::InpaintingPipeline pipe(models_path, device); ov::Tensor generated_image = pipe.generate(prompt, image, mask_image); imwrite("image.bmp", generated_image, true); } ``` ### Sample notebooks using this API See [here](https://openvinotoolkit.github.io/openvino_notebooks/?search=Text+to+Image+pipeline+and+OpenVINO+with+Generate+API) </details> ## Speech-to-text processing using Whisper Pipeline <details> For more examples check out our [Generative AI workflow](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html) NOTE: Whisper Pipeline requires preprocessing of audio input (to adjust sampling rate and normalize) ### Converting and compressing image generation model from Hugging Face library ```sh #Download and convert to OpenVINO whisper-base model optimum-cli export openvino --trust-remote-code --model openai/whisper-base whisper-base ``` ### Run generation using Whisper Pipeline API in Python NOTE: This sample is a simplified version of the full sample that is available [here](./samples/python/whisper_speech_recognition/whisper_speech_recognition.py) ```python import openvino_genai import librosa def read_wav(filepath): raw_speech, samplerate = librosa.load(filepath, sr=16000) return raw_speech.tolist() device = "CPU" # GPU can be used as well pipe = openvino_genai.WhisperPipeline("whisper-base", device) raw_speech = read_wav("sample.wav") print(pipe.generate(raw_speech)) ``` ### Run generation using Whisper Pipeline API in C++ NOTE: This sample is a simplified version of the full sample that is available [here](./samples/cpp/whisper_speech_recognition/whisper_speech_recognition.cpp) ```cpp #include <iostream> #include "audio_utils.hpp" #include "openvino/genai/whisper_pipeline.hpp" int main(int argc, char* argv[]) { std::filesystem::path models_path = argv[1]; std::string wav_file_path = argv[2]; std::string device = "CPU"; // GPU can be used as well ov::genai::WhisperPipeline pipeline(models_path, device); ov::genai::RawSpeechInput raw_speech = utils::audio::read_wav(wav_file_path); std::cout << pipeline.generate(raw_speech, ov::genai::max_new_tokens(100)) << '\n'; } ``` ### Sample notebooks using this API See [here](https://openvinotoolkit.github.io/openvino_notebooks/?search=Automatic+speech+recognition+using+Whisper+and+OpenVINO+with+Generate+API) </details> ## Additional materials - [List of supported models](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/docs/SUPPORTED_MODELS.md) (NOTE: models can work, but were not tried yet) - [OpenVINO Generative AI workflow](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html) - [Optimum-intel and OpenVINO](https://huggingface.co/docs/optimum/intel/openvino/export) ## License The OpenVINO™ GenAI repository is licensed under [Apache License Version 2.0](LICENSE). By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms. ", Assign "at most 3 tags" to the expected json: {"id":"12477","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"