The library offers a range of Macaw-LLM is composed of three main components: CLIP: Responsible for encoding images and video frames. Nov 9, 2023 · This will start a new container running the Python 3. Bloom is a new multi-lingual LLM (Large Language Model) from BigScience, a Hunggingface-hosted open collaboration with hundreds of researchers and institutions around the world. In the Colab menu bar, choose Runtime > Change Runtime Type and choose GPU under Hardware Accelerator. FloatTensor)), optional, returned when use_cache=True is passed or when config. conda create --name mlc-llm python=3. from llama_cpp import Llama. We’ll use the TextIteratorStreamer from the Transformers library to Launch LLaMA Board via CUDA_VISIBLE_DEVICES=0 python src/train_web. This repo contains a notebook and configuration scripts to get started with the basics of text generation using Bloom's 1. In this lab learning notebook, you will learn how to build the simplest Language Model (LLM) using Jupyter Notebook. Select one of the models you downloaded. %pip install ludwig --quiet. It may take time for downloading as it takes around 3. eos_token, return_tensors="pt") # concatenate new user input with chat history (if Apr 7, 2023 · Mike Young. This tutorial can easily be adapted to other LLMs. Whisper: Responsible for encoding audio data. py) and run: streamlit run llm_app. Dive into techniques, from chunking to clustering, and harness the power of LLMs like GPT-3. create function. Apr 7, 2023 12 min. The underlying technology of LLM is a type of neural architecture called a transformer. The llm crate exports llm-base and the model crates (e. n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Sep 7, 2022 · You also need to add the vocabulary file vocab. 4 Commits. LangChain is an open source AI abstraction library that makes it easy to integrate large language models (LLMs) like GPT-4/LLaMa 2 into applications. edited Jul 18, 2022. Specify the model ID and prompt to generate text main. This model is designed for general code Mar 12, 2024 · Top 5 open-source LLM backends, full table available here #3. Start the Server, this will run a locally hosted service (similar Feb 9, 2023 · There are no labels or anything in the data. ipynb. %pip uninstall -y tensorflow --quiet. The Inference API is free to use, and rate limited. BLOOM is an autoregressive LLM trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. You can append key phrases to the prompt, like 'Let's think step by step', or 'Let's solve the problem by splitting into steps', and so on. 9 image with the non-root user user. you can find data on how fine-tuning was done here . To evaluate the fine-tuned BLOOM model, the user can use the following Python code: # Evaluate the model on the test dataset test_accuracy = trainer . Discover how APIs act as crucial bridges, enabling seamless integration of sophisticated language understanding and generation Aug 14, 2023 · The BigScience Large Open-science Open-access Multilingual (BLOOM) language model is a transformer-based large language model (LLM). Linly LLM fine-tuning for Text Classification with AG's News Topics - Python API. To give some examples of how much VRAM it roughly takes to load a model in bfloat16: GPT3 requires 2 * 175 GB = 350 GB VRAM. 0 A large language model is a computer program that learns and generates human-like language using a transformer architecture trained on vast training data. Step 1: Install Requirements Create a requirements. chains import LLMChain chain = LLMChain(llm = llm, prompt = prompt) # Run the chain only specifying the input variable. chain. Causal here means that the text the model generates is based on the sequence of words that preceded it (this is called "unidirectional"). 🔨 LLM finetuning in 2-bit, 3-bit, 4-bit precision using the ModuLoRA algorithm; 🐍 Easy-to-use Python API for quantization, inference, and finetuning; 🤖 Modular support for multiple LLMs, quantizers, and optimization algorithms; 🤗 Share all your finetuned LLMs on the HuggingFace Hub Apr 12, 2023 · Using the cpp variant, you can run a Fast ChatGPT-like model locally on your laptop using an M2 Macbook Air with 4GB of weights, which most laptops today should be able to handle. Mathematically this is calculated using entropy. TensorRT-LLM contains components to create Python and C++ runtimes that execute those TensorRT engines. Jul 12, 2022 · BLOOM is the first multilingual Large Language Model (LLM) trained in complete transparency by the largest collaboration of AI researchers ever involved in a single research project. With these tools ready, you’re prepared to start May 20, 2023 · Task-agnostic compression: The compressed LLM should retain its original ability as a multi-task solver. May 13, 2023 · Step 6: Generate Text with the Trained LLM Once the model is trained, you can generate text by utilizing the openai. The Dockerfile and corresponding instructions are provided in a dedicated GitHub repo to reproduce MLC LLM performance for both single-GPU and multi-GPU, CUDA and ROCm. against the interface and get Aug 18, 2022 · Step-2: Building the model. You can then interact with the container using the terminal. - GitHub - ritun16/llm-text-summarization: A comprehensive guide and codebase for text summarization using Large Language Models (LLMs). For example, you can use 4 x AWS p3. BLOOM_local_example. conda activate mlc-llm. We will use Python and the nltk library to create a basic language model . 5-Turbo LLM model on Jul 12, 2022 · Bloom isn’t completely bias-free — no LLM is. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. (You can make it longer) May 31, 2023 · Introducing the Hugging Face LLM Inference Container for Amazon SageMaker. Set to 0 if no GPU acceleration is available on your system. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. BLOOM_api_example. The response is grounded in the reference. You will learn why graph databases are a reliable option for grounding Large Language Models (LLMs), using Neo4j to provide factual, reliable information to stop the LLM from giving false information, also known as hallucination Jun 5, 2023 · After your requested quotas are applied to your account, you can use the default Studio Python 3 (Data Science) image with a ml. Python API. cli. The integration of these models allows Macaw-LLM to process and analyze multi-modal data effectively. This is an example on how to deploy the open-source LLMs, like BLOOM to Amazon SageMaker for inference using the new Hugging Face LLM Inference Container. Open the provided URL in your web browser to interact with the GeekBot application. It provides information for anyone considering using the model or who is affected by the model. The LLM model used in this Aug 27, 2023 · BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. 👷 The LLM Engineer focuses on creating LLM-based applications and deploying them. We will deploy the 12B Pythia Open Assistant Model, an open-source Chat LLM trained with the Open Assistant dataset. The corresponding source code is available on GitHub, and it is highly inspired of the amrrs tutorial. Jan 6, 2024 · Save the code in a file (e. This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. model_path The instruction to load the dataset is given below by providing the name of the dataset of interest, which is tatsu-lab/alpaca: train_dataset = load_dataset ("tatsu-lab/alpaca", split ="train") print( train_dataset) OpenAI. Research [2] has shown that chain-of-thoughts prompting significantly boost the performance of LLMs. Uses. In this example, we'll use BLOOM to generate a creative story. e. 0 is a major rework of the previous version with a very clear goal in mind: making it possible to implement composable AI systems that are easy to use, customize Original model card: Meta's CodeLlama 34B Python. Jul 31, 2023 · In this video, you'll learn how to use the Llama 2 in Python. The code provided is structured to set up the environment, prepare the model, and generate text based on a given prompt. Top LLM Python Libraries. You will take a simple chat interface that repeats the user’s input and modify it to answer questions about movies via the Neo4j Recommendations Dataset Jan 12, 2024 · How to install and use BERT API in Python. Running Ollama [cmd] Ollama communicates via pop-up messages. 5 trillion tokens using TII's RefinedWeb dataset. BLOOM can be used for free through the Hugging Face ecosystem. bloom, gpt2 llama). Jun 6, 2023 · Scikit-LLM is a Python package that integrates large language models (LLMs) like OpenAI’s GPT-3 into the scikit-learn framework for text analysis tasks. Therefore, for now, let’s assume that the memory requirement for inference is equal to the memory requirement to load the model into the GPU VRAM. cpp and make sure you have set the correct environment variables for your OS. Further, the NeMo LLM Service also TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. txt file with the following content: taipy==3. This tutorial teaches:00:00 - Why the Hype BLOOM0 This hands-on programming tutorial with step by step implementation of Bloom Large Language Text Generation Model will guide you building your own text gener BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. Cannot retrieve latest commit at this time. LangChain is a powerful framework that simplifies the process of building advanced language model applications. 5 GB of data. Llama 2 model is an open-source LLM model from Meta and we'll interact with it like we'd do with ChatGPT (free version), only text based interaction. This app explores Bloom for Chain-of-Thought reasoning or C-o-T prompts. Model Summary. BLOOM – (BigScience Large Open-science Open-access Multilingual Language Model) is the new language model was developed over the last year by over 1,000 volunteer researchers as part of a project called BigScience, which was coordinated by the AI startup Hugging Face and funded by the French government. LangChain provides All you need to do is: 1) Download a llamafile from HuggingFace 2) Make the file executable 3) Run the file. Install MLC LLM Python package. , llm_app. First, we’ll cover OpenAI, LangChain, Hugging Face, Cohere, Pinecone, and ChatOpenAI. May 25, 2024 · pip install scikit-llm Support us 🤝. Code Llama. For the full list of available kernels, refer to Available Amazon SageMaker Kernels. We can see that the resulting data is in a dictionary of two keys: Features: containing the main columns of the data. Jul 28, 2023 · App 1: Step By Step With Bloom. encode(text + tokenizer. May 13, 2024 · A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. Check out the build instructions for Llama. Google Colab: If you are running this in a Google Colab notebook, be sure to Feb 10, 2024 · Select the double arrow on the left side of the UI to start hosting the LLM as a Service. This is a minimal viable product (MVP) designed to be as simple as possible while providing a complete and detailed implementation template and set of 🧩 LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks. Course Description. Classification using Fine-Tuning. Alternatively, we can use other available models. So, lets get started with the first example! How to Run the LLama2 Model from Meta. You switched accounts on another tab or window. Jul 12, 2022 · Today, we release BLOOM, the first multilingual LLM trained in complete transparency, to change this status quo — the result of the largest collaboration of AI researchers ever involved in a single research project. Both versions were trained with bfloat16 precision and AdamW. Click on the “Download” button for your operating system. Less training corpus: In this work, we use only 50k publicly available samples (alpaca) to post-train the LLM. Once Ollama is set up, you can open your cmd (command line) on Windows Jan 27, 2024 · Inference Script. In this hands-on course, you will use the knowledge obtained from the Neo4j & LLM Fundamentals course to create a Movie Recommendation Chatbot backed by a Neo4j database. Sep 6, 2023 · Falcon 180B sets a new state-of-the-art for open models. phi-1: phi-1: Dialog: English / A dataset generated by using the method in Textbooks Are All You Need. text = input(">> You:") # encode the input and add end of string token. Jan 30, 2024 · TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. evaluate ( test_dataset ) The test loss and accuracy will give the user an indication of how well the fine-tuned BLOOM model will perform on new data. 10. And you get to pick whether you want to surface the reasoning part — “Starting with 2 apples, then add 3, the result is 5” — to end users. You signed in with another tab or window. input_ids = tokenizer. - start_with Aug 16, 2022 · Today, we release BLOOM, the first multilingual LLM trained in complete transparency, to change this status quo — the result of the largest collaboration of AI researchers ever involved in a single research project. TensorRT-LLM is an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. Hence, if you’re familiar with scikit-learn, you’ll feel right at home with scikit-llm. ) 💬 Any questions? Ping us in our Discord! 🦙 Want to host Llama 2? Request access to its weights at the ♾️ Meta AI website and 🤗 Model Hub, generate an 🔑 access token, then add --token YOUR_TOKEN_HERE to the python -m petals. The Bloom LLM API stands out for its exceptional capabilities and versatile applications. Let’s get started! 👇. The only differences with the 40B version are that the number of layers and embedding dimensions are halved: Layers: 60. These files can be found in the model’s repository with the weights, see this repository for GPT2. cpp in a Docker container and interact with it via Dec 27, 2022 · The call to the Bloom model is in principle exactly the same as for other completion models: you throw a text and arguments like temperature, max_new_tokens, etc. You can also train your own tokenizer using transformers. 0. Notes from feedback sessions, training logs and any bugs that A comprehensive guide and codebase for text summarization using Large Language Models (LLMs). 2. Let's learn about a popular tool for working with LLMs! Apr 2, 2024 · This will help you to use any future open source LLM models with ease. For Windows users there is a Useful guide here. File: app. First, let's set up the Conda environment which we will be running this notebook in (not required if running in Google Colab). TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. " LLMs like GPT, Claude, and LLaMA are revolutionizing chatbots, content creation, and many more use-cases. n. run_server command. Yes it is possible. Key Features of Bloom LLM API. The answer is 5. The instructions below showcase how to use the multi-GPU feature in pure Python. Token Authorization - Using your HF token. Here we will use HuggingFace's API with google/flan-t5-xxl. If you don't have such data, then you should be looking into different types of models, probably retrieval-based chatbot techniques. 16xlarge instances, which provide 4 (instance) x 8 (GPU/instance) x 16 (GB/GPU) = 512 GB memory. Front-end UIs for connecting to LLM backends. Started by Huggingface under the umbrella of Big Science, over 1000 researchers contributed to its development. we use the prebuilt model of bloom from the Transformers library. This video will guide you through step-by-step process about how c . Efficient compression: 3 minutes for pruning and 3 hours for post-training. Consult the LLM plugins directory for plugins that provide access to remote and local models. py. use_cache=True) — Tuple of torch. Jan 5, 2024 · With NeMo LLM Service API users can invoke the services from within their application code. FloatTensor tuples of length config. Jul 16, 2022 · July 16, 2022. The pre-trained OPT model weights can be obtained from Metaseq, subject to their license. Jul 1, 2023 · Using LLMs, i. Perplexity: This is based on what the model estimates the probability of new data is. These models are trained on massive amounts Mar 20, 2023 · Starting with 2 apples, then add 3, the result is 5. Oct 19, 2023 · Using MLC LLM Docker. function. How to Use BLOOM. But even if these new LLMs are now open-source doesn’t mean that we can just download them and use them on our laptops. You can use it to deploy any supported open-source large language model of your choice. OpenAI API: Access powerful LLMs like GPT-3 through a paid API, unlocking past_key_values (tuple(tuple(torch. Run the installer and follow the setup instructions. You can conveniently and quickly try them out, and via an API that you can easily integrate into your applications. Step 0. You mentioned that you tried replacing OpenAI with "bloom-7b1" and "flan-t5-xl" in the code, but the llms fail to use the tools provided. At its core, Haystack 2. txt of your tokenizer inside Megatron-LM folder of your container. t3. Hi everyone, If you have enough compute you could fine tune BLOOM on any downstream task but you would need enough GPU RAM to store the model + gradient (optimizer state) which is quite costly. Neither use the big LLM models as both ChatGPT (GPT3. Test and evaluate, for free, over 150,000 publicly accessible machine learning models, or your own private models, via simple HTTP requests, with fast inference hosted on Hugging Face shared infrastructure. Mar 30, 2023 · Based on my understanding, the issue is about using langchain without the OpenAI API. OpenAI is one of the leading organizations in the world of AI and LLMs Aug 15, 2023 · It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output. Scikit-LLM is designed to work within the scikit-learn framework. But the hope is that by maintaining transparency around the training data, it’ll be easier for researchers to get to the root of Bloom’s Text Generation Inference (TGI) is an open-source toolkit for serving LLMs tackling challenges such as response time. g. This is the repository for the 34B Python specialist version in the Hugging Face Transformers format. 📚 Learn more (how to use multiple GPUs, start the server on boot, etc. As other researchers have established as well, LLMs are overall performing well in MT when certain conditions are met. 3B parameter pre-trained model. BigScience Workshop org Jul 18, 2022. Mar 9, 2023 · One of the better known large language models (LLMs) is BLOOM. Completion. Reload to refresh your session. BLOOM is available in the following versions: If we needed the name of the model, we can use. As such, it is able to May 17, 2023 · 2. To some NLP experts, it is also one of the most important, due to its architecture and multilingual characteristics (46 languages) and open-source nature. You signed out in another tab or window. CPP variant combines Facebook's LLaMA, Stanford Alpaca, alpaca-Lora, and the corresponding weights. 5 and GPT-4. Here are some of its key Mar 7, 2024 · Now you are ready torun Ollama and download some models :) 3. medium instance to run the notebook code snippets. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open Course Description. 3. In this course, you will learn how to integrate Neo4j with Generative AI models using Langchain. Large Language Models (LLMs) are foundational machine learning models that use deep learning algorithms to process and understand natural language. Let's make code for chatting with our AI using greedy search: # chatting 5 times with greedy search for step in range(5): # take user input. If you need an inference solution for production, check out Transparency is the backbone of BLOOM, a project where everyone can access the source code and the training data in order to run, study, and improve it. You will need at least 350GB GPU memory on your entire cluster to serve the OPT-175B model. If you think about it, when you create a chain combining 2 tools, you have to pass the output of the first tool to the second tool as input and that is why we are using Aug 8, 2019 · Even though the sentences feel slightly off (maybe because the Reuters dataset is mostly news), they are very coherent given the fact that we just created a model in 17 lines of Python code and a Apr 3, 2023 · DO you want to query your PDF and get your questions answered? Then this video is for you. Jul 21, 2022 · Jul 21, 2022. •. llm = Llama(. Bloom is a Large Language Model (LLM) which has been trained in an unusually collaborative and transparent way. BERT. (multiple GPUs are not supported yet) (multiple GPUs are not supported yet) Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU. Each of the entry in this dataset comprise a question, a response and a reference. A common tasks people fine-tune auto regressive models is Question Answering. You can support the project in the following ways: ⭐ Star Scikit-LLM on GitHub (click the star button in the top right corner) 💡 Provide your feedback or propose ideas in the issues section or Discord; 📰 Post about Scikit-LLM on LinkedIn or other platforms; 🔗 Check out our other projects: Dingo Jun 7, 2023 · The Falcon-7B has a smaller architecture that enables its fine-tuning on consumer hardware. cpp into a single file that can run on most computers any additional dependencies. run("colorful socks") If we want to use the output of this first LLM as the input for a second LLM, we can use a SimpleSequentialChain: The training process aims to minimize the loss. The tools discussed in Section 2 can handle basic queries using the pre-trained data of Jan 30, 2024 · TensorRT-LLM Overview. It offers a unified API for accessing and utilizing pre-trained models Serverless Inference API. Mar 11, 2024 · Haystack is an open-source Python framework for building production-ready LLM applications, with integrations to almost all major model providers and databases. The lower the perplexity, the better. Nov 9, 2022 · Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. json and merges table merges. 5/GPT4) and Bard are more trained to answer Jan 23, 2024 · In this tutorial we will create a simple chatbot web interface and deploy it using an open-source Python library called Taipy. From the open ai cookbook there is: Classification using embeddings. llamafiles bundle model weights and a specially-compiled version of llama. You can also run Llama. This has the Sep 30, 2023 · Introduction to LangChain. toml. With its 176 billion parameters, BLOOM is able to generate text in 46 natural languages and 13 programming languages. BLOOM LM - BigScience Large Open-science Open-access Multilingual Language Model is a transformer based LLM. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. # Set gpu_layers to the number of layers to offload to GPU. Llama 2 is an open source large language model created by Meta AI . Streamlit Setup. Set up a SageMaker session. Add llm to your project by listing it as a dependency in Cargo. It focuses on math and CS problems. Embedding dimensions: 4,544. This represents the longest single-epoch pretraining for an open model. 101. Let's set up your environment, so you can successfully run the ChatModule. , prompting, is the new programming. OpenAI. Run prompts from the command-line, store the results in SQLite, generate embeddings and more. To use the version of llm you see in the main branch of this repository, add it from GitHub (although keep in mind this is pre-release software): Dataset used by WebGLM, which is a QA system based on LLM and Internet. So depending on what you want your chatbot to say, you may want to do some fine-tuning Apr 25, 2023 · When we want to use it with our LLM, we can use an LLMChain as follows: from langchain. The SageMaker Python SDK automatically translates your existing workspace environment, and any associated data processing code and datasets, into an SageMaker training job that runs on the training platform. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines. Jul 12, 2023 · Adding the streaming capability: To keep the user engaged and show immediate responses, let’s add the streaming support. b. We recommend using a GPU runtime for this example. Import the necessary libraries: Streamlit for the web application, OpenAI language model (OpenAI), and tiktoken for token counting. Other users have suggested trying different models and prompt engineering to resolve the issue. Launch LLaMA Board via CUDA_VISIBLE_DEVICES=0 python src/train_web. Jul 30, 2022 · The COLAB implementation of BigScience BLOOM to experience for yourself. Embeddings can be easier with a smaller amount of data than Fine-Tuning but fine-tuning should work better once you have a lot of data. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages. We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. Sep 14, 2023 · With Amazon SageMaker, now you can run a SageMaker training job simply by annotating your Python code with @remote decorator. The Python code shows how to use Gradio to create a demo for a text-generation model trained using transformers. BLOOM is made to be experimented with. Bloom requires 2 * 176 GB = 352 GB VRAM. It is the largest openly available language model, with 180 billion parameters, and was trained on a massive 3. TGI powers inference solutions like Inference Endpoints and Hugging Chat, as well as multiple community projects. Bloom is based on the Megatron GPT model which is also designed to be a "causal" language model. Use the following code to set up your SageMaker Unlock the power of Large Language Models (LLMs) in your applications with our latest blog on "Serving LLM Application as an API Endpoint Using FastAPI in Python. This hands-on workshop shows how to use Langchain LLM application framework with Chroma embedding database to fine-tune an OpenAI GPT-3. These models can be flexibly adapted to solve almost any language processing task for your use cases. With its 176 billion parameters (larger than OpenAI’s GPT-3 and MetaAI’s OPT), BLOOM can generate text in 46 natural languages and dialects and 13 programming Sep 4, 2023 · This Python library, crafted by Hugging Face, offers a user-friendly and effective means to access and leverage these models. LLM (LLaMA/Vicuna/Bloom): The language model that encodes instructions and generates responses. BLOOM inference with 176 billion parameters can either be achieved on professional Feb 4, 2024 · Transformers: This core library provides pre-trained LLM models and tools for fine-tuning and using them for your tasks. 🧑‍🔬 The LLM Scientist focuses on building the best possible LLMs using the latest techniques. 🔒 Jan 21, 2024 · Now pip install llama-cpp-python or if you use poetry poetry add llama-cpp-python; Windows/Linux. Question-answering models require training data where you explicitly have a question and the answer to it. Dec 7, 2022 · 1. In this quick article, I’ll show you the best LLM Python libraries. You can find the model on the Hugging Face Hub ( base Apr 4, 2024 · Visit the LLM Studio website. nx et en au wk gp if iv op ku