Ollama reranking models

Ollama reranking models. Ollama Version 0. Exploring different prompts and text summarization methods to help determine document relevance Jan 9, 2024 · Ollama is a great option when it comes to running local models. 2 model from Mistral. Change BOT_TOPIC to reflect your Bot's name. Note: While we support self hosted LLMs, you will get significantly better responses with a more powerful model like GPT-4. This operation is performed using torch. Go to https://ollama. One such model is codellama, which is specifically trained to assist with programming tasks. The Modelfile RAG is a way to enhance the capabilities of LLMs by combining their powerful language understanding with targeted retrieval of relevant information from external sources often with using embeddings in vector databases, leading to more accurate, trustworthy, and versatile AI-powered applications Ollama provides a way to run large language models (LLMs) locally. You can easily switch between different models depending on your needs. Jul 4, 2024 · 1. What is Re-Ranking ? It is basically a 2 Stage RAG:-Stage 1 — Keyword Search; Stage-2 — Semantic Top K If you don’t want to run the model on your laptop, alternatively you could use their cloud version in which case you will have to modify the code in this blog to use the right API keys and packages. Run Llama 3. . Copy Models: Duplicate existing models for further experimentation with ollama cp. Model quantization is a technique that involves reducing the precision of a model’s weights (e. Pgai is open source under the PostgreSQL License and is available for you to use in your AI projects today. ai/library. Here’s a breakdown of what you’ll need: an LLM: we’ve chosen 2 types of LLMs, namely TinyLlama1. Go ahead and download and install Ollama. Remove Unwanted Models: Free up space by deleting models using ollama rm. Go to System. BM25, Cohere Rerank, etc. , Llama 2 for language tasks, Code Llama for coding assistance). Multimodal Ollama Cookbook Multi-Modal LLM using OpenAI GPT-4V model for image reasoning Multi-Modal LLM using Replicate LlaVa, Fuyu 8B, MiniGPT4 models for image reasoning Semi-structured Image Retrieval Multi-Tenancy Multi-Tenancy Multi-Tenancy RAG with LlamaIndex Ollama is an advanced AI tool that allows users to easily set up and run large language models locally (in CPU and GPU modes). x 已經支持同時調用embedding和LLM model 不知道，未來Langchain-Chatchat項目是否可以全面支持ollama的LLM以及embedding model？ Jun 3, 2024 · Create Models: Craft new models from scratch using the ollama create command. Reranking is relatively close to embeddings and there are models for both embed/rerank like bge-m3 Mar 12, 2024 · Setting the stage for offline RAG. If neither of these are sufficient, you can manually specify the context length by using hte "contextLength" property in your model in config. @pamelafox made their first Oct 14, 2023 · Pulling Models - Much like Docker’s pull command, Ollama provides a command to fetch models from a registry, streamlining the process of obtaining the desired models for local development and testing. This article will describe a cool trick you can use to improve retrieval performance in your RAG pipelines. Selecting your model on Ollama is as easy as a few clicks: i. For this example, we'll assume we have a set of documents related to various With a standard size of 137 million parameters, the model enables fast inference while delivering better performance than our small model. 1 "Summarize this file: $(cat README. 30 Feb 17, 2024 · 「Ollama」の日本語表示が改善されたとのことなので、「Elyza-7B」で試してみました。 1. That is fine-tuning the embedding model (for embedding) and the cross May 8, 2024 · # Run llama3 LLM locally ollama run llama3 # Run Microsoft's Phi-3 Mini small language model locally ollama run phi3:mini # Run Microsoft's Phi-3 Medium small language model locally ollama run phi3:medium # Run Mistral LLM locally ollama run mistral # Run Google's Gemma LLM locally ollama run gemma:2b # 2B parameter model ollama run gemma:7b Aug 1, 2024 · Figure 18 shows a simple Ollama use case for the chat and autocomplete, but you can also add models for embeddings and reranking. • Designing an intelligent agent that supports self-RAG and exploring a function calling mechanism to enhance Ollama's response generation in automotive-specific scenarios. jpg or . - ollama/ollama Mar 17, 2024 · Below is an illustrated method for deploying Ollama with Docker, highlighting my experience running the Llama2 model on this platform. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. This post explores how to create a custom model using Ollama and build a ChatGPT like interface for users to interact with the model. Mar 7, 2024 · Ollama communicates via pop-up messages. pip install ollama chromadb pandas matplotlib Step 1: Data Preparation. Dependencies: Install the necessary Python libraries. Mar 5. Nov 3, 2023 · How do we know which embedding model fits our data best? Or which reranker boosts our results the most? In this blog post, we’ll use the Retrieval Evaluation module from LlamaIndex to swiftly determine the best combination of embedding and reranker models. You can run the model using the ollama run command to pull and start interacting with the model directly. 🐍 Native Python Function Calling Tool: Enhance your LLMs with built-in code editor support in the tools workspace. Possible Implementation. yaml profile and run the private-GPT server. nomic-embed-text. These files are not removed using ollama rm if there are other models that use the same files. basically I run ollama run choose "weather is 16 degrees outside" and it gives me ollama run weather "weather is 16 degrees Multimodal Ollama Cookbook Multi-Modal LLM using OpenAI GPT-4V model for image reasoning Multi-Modal LLM using Replicate LlaVa, Fuyu 8B, MiniGPT4 models for image reasoning Semi-structured Image Retrieval Multi-Tenancy Multi-Tenancy Multi-Tenancy RAG with LlamaIndex Get up and running with large language models. Mar 21, 2024 · How the score is calculated using late interaction: Dot Product: It computes the dot product between the query embeddings and document embeddings. Ollama Embedding Models¶ While you can use any of the ollama models including LLMs to generate embeddings. And if not Get up and running with large language models. These models are designed to cater to a variety of needs, with some specialized in coding tasks. md中，3. Open WebUI Version v0. transpose(1, 2) (transposed to align dimensions $ ollama run llama3. Different models can share files. This is working as expected but I'm a noob and I'm not sure this is the best way to do this. Figure 18: Advanced configuration options in the Continue setup file. Feb 16, 2024 · 1-first of all uninstall ollama (if you already installed) 2-then follow this: Open Windows Settings. Which rag embedding model do you use that can handle multi-lingual documents, I have not overridden this setting in open-webui, so I am using the default embedded model that open-webui uses. Toggles automatic update of the reranking model. mxbai-embed-large. Click on New And create a variable called OLLAMA_MODELS pointing to where you want to store the models(set path for store Jun 28, 2024 · Pgai uses Python and PL/Python to interact with Ollama model APIs within your PostgreSQL database. Improved performance of ollama pull and ollama push on slower connections; Fixed issue where setting OLLAMA_NUM_PARALLEL would cause models to be reloaded on lower VRAM systems; Ollama on Linux is now distributed as a tar. Even, you can This project is an experimental sandbox for testing out ideas related to running local Large Language Models (LLMs) with Ollama to perform Retrieval-Augmented Generation (RAG) for answering questions based on sample PDFs. Introduction. To use a vision model with ollama run, reference . Consider using models optimized for speed: Mistral 7B; Phi-2; TinyLlama; These models offer a good balance between performance and Open WebUI is an extensible, feature-rich, and user-friendly self-hosted WebUI designed to operate entirely offline. ” ii. While most tools treat a model as solely the weights, Ollama takes a more comprehensive approach by incorporating the system Prompt and template. New Contributors. Let's get started by installing the required To deploy Ollama and pull models using IPEX-LLM, please refer to this guide. Customize and create your own. 3 通过 In ollama hub we provide the following set of models: jina-embeddings-v2-base-en: 137 million parameters (you are here). Go to the Advanced tab. Question: What types of models are supported by OLLAMA? Answer: OLLAMA supports a wide range of large language models, including GPT-2, GPT-3, and various HuggingFace models. com 2. png files using file paths: May 5, 2024 · In this article, I’ll share how I’ve enhanced my experience using my own private version of ChatGPT to ask about documents. 1 Table of contents Setup Call chat with a list of messages Streaming JSON Mode Structured Outputs Ollama - Gemma OpenAI OpenAI JSON Mode vs. In Ollama, a model consists of multiple layers, each serving a distinct purpose analogous to docker's layers. Hugging Face is a machine learning platform that's home to nearly 500,000 open source models. May 13, 2024 · Ollama Open WebUI、Dify を利用する場合は、pdf や text ドキュメントを読み込む事ができます。 Open WebUI の場合. I hosted few models on ollama on a machine having rtx 4090 gpu. 117. If you don’t… Especially this last part is quite important. Ollama is a powerful tool that simplifies the process of creating, running, and managing large language models (LLMs). Continue offers several reranking options: cohere, voyage, llm, hugginface-tei, and free-trial, which can be configured in config. 3版本 想咨询3问题： 1，README. ollama. For each model family, there are typically foundational models of different sizes and instruction-tuned variants. Voyage AI Voyage AI offers the best reranking model for code with their rerank-lite-1 model. Somet We would like to show you a description here but the site won’t allow us. In this blog post, we’re going to look at how to download a GGUF model from Hugging Face and run it locally. Apr 19, 2024 · I'm not sure about Rerankers but Ollama started supporting text embeddings as of 0. When you venture beyond basic image descriptions with Ollama Vision's LLaVA models, you unlock a realm of advanced capabilities such as object detection and text recognition within images. md)" Ollama is a lightweight, extensible framework for building and running language models on the local machine. 34. Their library offers a dozen different models, and Ollama is very easy to install. Smaller models generally run faster but may have lower capabilities. I am using Ollama for my projects and it&#39;s been great. Using the open source AI code assistant Choosing the Right Model to Speed Up Ollama. , float32 –> int8) leading to a reduction in computational costs. Despite impressive performance, current zero-shot relevance ranking with LLMs heavily relies on human prompt engineering. Update the OLLAMA_MODEL_NAME setting, select an appropriate model from ollama library. This model is an embedding model, meaning it can only be used to generate May 13, 2024 · The reranking model can be trained on a large dataset of questions and documents and is able to capture the relevance of a document to a question better than normal embedding models. /Modelfile List Local Models: List all models installed on your machine: ollama list Pull a Model: Pull a model from the Ollama library: ollama pull llama3 Delete a Model: Remove a model from your machine: ollama rm llama3 Copy a Model: Copy a model Apr 10, 2024 · Throughout the blog, I will be using Langchain, which is a framework designed to simplify the creation of applications using large language models, and Ollama, which provides a simple API for The next step is to invoke Langchain to instantiate Ollama (with the model of your choice), and construct the prompt template. Apr 29, 2024 · LangChain provides the language models, while OLLAMA offers the platform to run them locally. Expected Behavior: [Describe what you expected to happen. May 17, 2024 · Create a Model: Use ollama create with a Modelfile to create a model: ollama create mymodel -f . To interact with your locally hosted LLM, you can use the command line directly or via an API. For this guide I’m going to use the Mistral 7B Instruct v0. 1B and Zephyr-7B-gemma-v0. In this article, we will take a closer look at some of the shortcomings of the prompting methods and explore the latest efforts to train ranking-aware LLMs. This model, often trained on a large dataset of query-document pairs with New reranker model: release cross-encoder models BAAI/bge-reranker-base and BAAI/bge-reranker-large, which are more powerful than embedding model. Apr 1, 2024 · Coming soon RAG with Ollama - a primer on how to build a simple RAG app with Ollama and Chroma April 1, 2024 Amikos Tech LTD, 2024 (core ChromaDB contributors) Multimodal Ollama Cookbook Multi-Modal LLM using OpenAI GPT-4V model for image reasoning Multi-Modal LLM using Replicate LlaVa, Fuyu 8B, MiniGPT4 models for image reasoning Semi-structured Image Retrieval Multi-Tenancy Multi-Tenancy Multi-Tenancy RAG with LlamaIndex Feb 29, 2024 · 2. a unified embedding model to support diverse retrieval augmentation needs for LLMs: See README: BAAI/bge-reranker-large: Chinese and English: Inference Fine-tune: a cross-encoder model which is more accurate but less efficient [2] BAAI/bge-reranker-base: Chinese and English: Inference Fine-tune: a cross-encoder model which is more accurate but based on the subject mistral can choose the best model and gives me the command to run so I can run it through the model I want. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. For example, if model A uses blob A, B and model B uses blob A, C, removing model A will only remove blob B. ollama ollama 保证最新版（部署时的版本: 0. You can also read more in their README. It works by retrieving relevant information from a wide range of sources such as local and remote documents, web content, and even multimedia sources like YouTube videos. まずは、より高性能な embedding モデルを取得します。 ollama pull mxbai-embed-large. If you have changed the default IP:PORT when starting Ollama, please update OLLAMA_BASE_URL. RAG itself is not a fast technology. The usage of the cl. 124. After obtaining an API key from here, you can configure like this: Get up and running with large language models. Select About Select Advanced System Settings. ] Environment. Listing Available Models - Ollama incorporates a command for listing all available models in the registry, providing a clear overview of their Oct 18, 2023 · One cool thing about GGUF models is that it’s super easy to get them running on your own machine using Ollama. jina-embeddings-v2-base-de: German-English Bilingual embeddings. ) What the optimal values of embedding top-k and reranking top-n are for the two stage pipeline, accounting for latency, cost, and performance. Ollama での Llama2 の実行 はじめに、「Ollama」で「Llama2」を試してみます。 (1 . ] Actual Behavior: [Describe what actually happened. You’re welcome to pull a different model if you prefer, just switch everything from now on for your own model. In ollama hub we provide the following set of models: jina-embeddings-v2-small-en: 33 million parameters Jun 25, 2024 · 本机发布了ollama，运行有llama3:8b,nomic-embed-tet:latest和qwen:7b模型 Langchain-Chatchat使用0. Ollama (if applicable): 0. ⬆️ GGUF File Multimodal Ollama Cookbook Multi-Modal LLM using OpenAI GPT-4V model for image reasoning Multi-Modal LLM using Replicate LlaVa, Fuyu 8B, MiniGPT4 models for image reasoning Semi-structured Image Retrieval Multi-Tenancy Multi-Tenancy Multi-Tenancy RAG with LlamaIndex The Layers of a Model. To pull the model use the following command: Multimodal Ollama Cookbook Multi-Modal LLM using OpenAI GPT-4V model for image reasoning Multi-Modal LLM using Replicate LlaVa, Fuyu 8B, MiniGPT4 models for image reasoning Semi-structured Image Retrieval Multi-Tenancy Multi-Tenancy Multi-Tenancy RAG with LlamaIndex Type: str (enum: (empty for local model), ollama, openai) Options: (empty) - Uses a local model for embeddings. Select Environment Variables. safetensors fo Get up and running with large language models. 1. To demonstrate the RAG system, we will use a sample dataset of text documents. Jul 18, 2024 · Reranking is currently very common techniques used along with embeddings in RAG systems. Apr 8, 2024 · Embedding models are available in Ollama, making it easy to generate vector embeddings for use in search and retrieval augmented generation (RAG) applications. May 12, 2024 · The reranking process involves using a separate model to evaluate the relevance of each retrieved document to the query. Apr 28, 2024 · By now, you are probably already familiar with the Retrieval-Augmented Generation (RAG system, a framework used in NLP applications. Here's a short list of some currently available models: snowflake-arctic-embed. Oct 24, 2023 · The user’s prompt and any relevant information from the vector database are supplied to the language model (“augmentation”). I try to use bge-reranker-v2-m3、mxbai-rerank-large-v1，model. ColBERT is one of the fastest reranking models available and reduces this point of friction. 3 stand out the best: OpenAI — with a hitrate of 90% on the bge reranker large model May 3, 2024 · こんにちは、AIBridge Labのこばです🦙 無料で使えるオープンソースの最強LLM「Llama3」について、前回の記事ではその概要についてお伝えしました。 今回は、実践編ということでOllamaを使ってLlama3をカスタマイズする方法を初心者向けに解説します！ 一緒に、自分だけのAIモデルを作ってみ May 9, 2024 · copy the BAAI/bge-reranker-v2-minicpm-layerwise in to Reranking Model. Jul 15, 2024 · Langchain-Chatchat readme提到，能調用ollama的模型，不包括embedding model 現在ollama 0. Ollama local dashboard (type the url in your webbrowser): Models in roadmap: InRanker; Why sleeker models are preferred ? Reranking is the final leg of larger retrieval pipelines, idea is to avoid any extra overhead especially for user-facing scenarios. Navigate to Models: Once logged into Ollama, locate the section or tab labeled “Models” or “Choose Model. unsqueeze(0) (unsqueeze is used to add a batch dimension) and document_embeddings. We will use Ollama to run the open source Mistral-7b model locally. Operating System: wsl2 win11 docker for desktop. Apr 14, 2024 · #ollama #llm #rag #chatollama #rerank #cohere推荐一个目前全网价格最实惠的合租平台，ChatGPT，MidJourney，奈飞，迪士尼，苹果TV等热门软件应有尽有 - https://dub Feb 1, 2024 · Fortunately, there are techniques available to make running these models locally feasible, such as model quantization. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. Create new models or modify and adjust existing models through model files to cope with some special application scenarios. DSPy is the framework for solving advanced tasks with language models and retrieval models. As reranking again needs to call a reranking model, additional latency is introduced. To that end models with really small footprint that doesn't need any specialised hardware and yet offer competitive performance are chosen. Open WebUI Version: v0. Exploring Rerankers such as Cross Encoders, Colbert v2 & FlashRank. Create and add custom characters/agents, customize chat elements, and import models effortlessly through Open WebUI Community integration. Chroma provides a convenient wrapper around Ollama's embedding API. It is recommended to use a single GPU for inference. Mar 27, 2024 · 👍 17 RobinBially, yuliyantsvetkov, cedarice, trengrj, adwidianjaya, falmanna, nightosong, wzulfikar, DevinDon, iflyhere, and 7 more reacted with thumbs up emoji May 17, 2023 · How our LLM reranking implementation compares to other reranking methods (e. 26 and even released a blog post about Embedding models. #rag #llm #groq #cohere #langchain #ollama #reranking In this video, we're diving into the creation of a cool retrieval-augmented generation (RAG) app. Comparing LangChain and LlamaIndex with 4 tasks. 1, Phi 3, Mistral, Gemma 2, and other models. 2. Select Your Model: Choose the model that aligns with your objectives (e. It is a hit or a miss with translation. With Ollama, users can leverage powerful language models such as Llama 2 and even customize and create their own models. gz file, which contains the ollama binary along with required libraries. Ollama is an application for Mac, Windows, and Linux that makes it easy to locally run open-source models, including Llama3. The layers of a model include: May 23, 2024 · Cross-Encoders Reranking Cross-Encoders Reranking On this page Ollama Ollama Ollama Running Chroma Authorization Model with OpenFGA • Developing an advanced RAG system based on the Langchain framework, introducing reranking models and BM25 retrievers to build an efficient context compression pipeline. I’m interested in running the Gemma 2B model from the Gemma family of lightweight models from Google DeepMind. It supports various LLM runners, including Ollama and OpenAI-compatible APIs. Running Models. matmul(), which calculates the matrix multiplication between query_embeddings. 7B, 13B and a new 34B model: ollama run llava:7b; ollama run llava:13b; ollama run llava:34b; Usage CLI. Setup. 1 Ollama - Llama 3. We generally recommend using specialized models like nomic-embed-text for text embeddings. The article also describes several strategies to build effective and efficient LLM Jan 1, 2024 · One of the standout features of ollama is its library of models trained on different data, which can be found at https://ollama. Continue can then be configured to use the "ollama" provider: Mar 11, 2024 · 2. All the LLM calls introduce latency. jina-embeddings-v2-base-es: Spanish-English Bilingual embeddings. ; an embedding model: we will LangChain4j Documentation 2024. cpp, but in RAG, I hope to run a rerank model to improve the accuracy of recall. ai. g. ai/ and download the installer. Then, follow the same steps outlined in the Using Ollama section to create a settings-ollama. user_session is to mostly maintain the separation of user contexts and histories, which just for the purposes of running a quick demo, is not strictly required. Model selection significantly impacts Ollama's performance. Retrieval Augmented Generation (RAG) is a a cutting-edge technology that enhances the conversational capabilities of chatbots by incorporating context from diverse sources. Ollama Usage. Start by downloading Ollama and pulling a model such as Llama 2 or Mistral: ollama pull llama2 Usage cURL May 3, 2024 · I also tried to delete those files manually, but again those are KBs in size not GB as the real models. You can run many models such as LLama3, Mistral, CodeLlama and many others on your machine, with full CPU and GPU support. Start building more private AI applications with open-source models using pgai and Ollama today. Ollama 「Ollama」はLLMをローカルで簡単に実行できるアプリケーションです。 Ollama Get up and running with large language models, locally. However, you Jun 3, 2024 · As part of the LLM deployment series, this article focuses on implementing Llama 3 with Ollama. Browser (if applicable): chrome 🔄 Seamless Integration: Copy any ollama run {model:tag} CLI command directly from a model's page on Ollama library and paste it into the model dropdown to easily select and pull models. Once Ollama is set up, you can open your cmd (command line) on Windows and pull some models locally. Jun 20, 2024 · Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR) across various modules, such as reranking. In this article, we aim to guide readers through constructing an May 23, 2024 · Ollama: Download and install Ollama from the official website. The latter models are specifically trained for embeddings and are more Multimodal Ollama Cookbook Multi-Modal LLM using OpenAI GPT-4V model for image reasoning Multi-Modal LLM using Replicate LlaVa, Fuyu 8B, MiniGPT4 models for image reasoning Semi-structured Image Retrieval Multi-Tenancy Multi-Tenancy Multi-Tenancy RAG with LlamaIndex Oct 22, 2023 · Aside from managing and running models locally, Ollama can also generate custom models using a Modelfile configuration file that defines the model’s behavior. 次にドキュメントの設定をします。embedding モデルを指定します。 Jan 8, 2024 · Step 1: Download Ollama and pull a model. Apr 16, 2024 · 1. Ollama - Llama 3. Refer to Model Configs for how to set the environment variables for your particular deployment. For Ollama, the context length is determined automatically by asking Ollama. However, when using some AI app platform, like dify, build RAG app, rerank is nessesary. Get up and running with large language models. Please pay special attention, only enter the IP (domain) and PORT here, without appending a URI. Download the app from the website, and it will walk you through setup in a couple of minutes. Ollama helps with running LLMs locally on your laptop. 🗂️ Create Ollama Modelfile: To create a model file for Ollama, navagate to the Admin Panel > Settings > Models > Create a model menu. This tutorial will guide you through the steps to import a new model from Hugging Face and create a custom Ollama model. Bring Your Own Mar 21, 2024 · Based on these readings, we can observe that amongst the given models, based on different reranking models. 48），部署参考官方文档。 ollama pull qwen2:7b(根据自己的需求拉取大模型) ollama pull An Ollama Modelfile is a configuration file that defines and manages models on the Ollama platform. Ollama is a powerful tool that allows users to run open-source large language models (LLMs) on their Feb 8, 2024 · Ollama now has built-in compatibility with the OpenAI Chat Completions API, making it possible to use more tooling and applications with Ollama locally. It's possible for Ollama to support rerank models. May 17, 2023 · Improving RAG with Query expansion & reranking models. Ming. Also there are models where same model instance can be used for both embeddings and reranking - that is great resource optimisation. Function Calling for Data Extraction OpenLLM OpenRouter OpenVINO LLMs Optimum Intel LLMs optimized with IPEX backend Dec 26, 2023 · The previous article did a deep dive into the prompting-based pointwise, pairwise, and listwise techniques that directly use LLMs to perform reranking. CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. Feb 2, 2024 · These models are available in three parameter sizes. The retrieved text is then combined with a The rerank model cannot be converted to the ollama-supported format through llama. # run ollama with docker # use directory called `data` in Apr 22, 2024 · Ollama allows you to run locally open-source large language models, such as Llama 2: Ollama bundles model weights, configuration, and data into a single package. Selecting Efficient Models for Ollama. 1, Mistral, Gemma 2, and other large language models. Pull Pre-Trained Models: Access models from the Ollama library with ollama pull. Built with Docusaurus. For command-line interaction, Ollama provides the `ollama run <name-of-model 🛠️ Model Builder: Easily create Ollama models via the Web UI. The language model uses the information from the database to answer the user’s prompt (“generation”). json. Get up and running with Llama 3. Existing automatic prompt engineering algorithms primarily focus on language modeling and classification tasks, leaving the domain of IR Advanced Usage and Examples for LLaVA Models in Ollama Vision. qbvr fjhjc bkjo fmba aad lsvpthot xrqf tffplq chdq uvvi