Pratham Sahni on LinkedIn: #ollama #ollama #ollama #ai #generativeai #innovation #ollama #opensource… (2024)

Pratham Sahni

Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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🌟 Who knew playing with LLMs could be so easy? Recently, I came across Ollama and it's truly remarkable! This tool simplifies the process of experimenting with different large language models (LLMs) directly on your own computer.No crazy setup needed, so it's perfect for anyone curious about AI.What's fantastic about #ollama is its user-friendly nature. It makes trying out various LLMs locally incredibly easy, even for those new to the field of AI.I'm particularly impressed by Ollama's versatility. It supports a wide range of open source models, from Mistral 7B to Llama 2, making it a valuable resource for AI enthusiasts of all levels.Moreover, #ollama ensures enhanced security as LLMs run locally, keeping user data safe and private. 🔒Let's embrace the potential of Ollama and delve into the exciting world of AI together! 💫 Call to Action:Checkout #ollama website to get up and running https://ollama.com/Want to learn about more open source GenAI models Checkout My GitHub Repo https://lnkd.in/gwJTJ2b6#ai #generativeai #innovation #ollama #opensource #exploration #ml #llm

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  • Pratham Sahni

    Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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    Streamline the Dev Workflow with Docker!Are you tired of complex development environments that vary across machines? Docker is here to save the day! ♂️Docker is a powerful tool that packages application and its dependencies into a lightweight, portable container. This means we can run the app consistently on any machine with Docker installed, regardless of the underlying operating system. This consistency makes collaboration with the team a breeze!Here's what #docker brings to the table (and to the dev workflow): 🔹 Consistent Environments: Say goodbye to "it works on my machine" errors! Docker ensures everyone on the team has the identical environment. 🔹 Portability: Run apps anywhere with Docker! Docker containers can be easily shared and deployed across different environments. 🔹 Isolation: Docker containers isolate applications from each other, preventing conflicts and simplifying debugging. 🔹 Scalability: Easily scale the applications up or down by adding or removing containers.Ready to get started with #docker? Here are a few basic commands to get you going: 🔹 docker pull image_name - This command grabs the the image from the remote repo (particularly docker registry) to local machine. 🔹 docker run hello-world - This command will download and run a simple "Hello, World!" app to get you familiar with the Docker CLI. 🔹 docker ps - This command lists all the running Docker containers. 🔹 docker stop container_id - This command stops a running container.🔹docker images - This commands lists all the images that have been downloaded and are ready to use.-> #docker is a versatile tool that can be used for a variety of applications, including: 🔹 Microservices development: It is perfect for building and deploying microservices applications. 🔹 Continuous integration and continuous delivery (CI/CD): It can streamline the CI/CD pipeline by ensuring consistent builds and deployments. 🔹 DevOps: It is a valuable tool for DevOps teams who need to automate the deployment and management of applications.If looking to improve development workflow, #docker is a great option to consider. With its ease of use and wide range of benefits, Docker can help build, ship, and run applications faster and more efficiently.So, have you used Docker in your projects yet? Share your experiences in the comments below!#docker #containerization #developer #devworkflow

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  • Pratham Sahni

    Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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    Infrastructure as Code (IaC): Automate Your Way to IT NirvanaTired of spending hours manually provisioning and configuring servers? There's a better way!Infrastructure as Code (IaC) is like magic for IT admins. ✨ It allows you to define your infrastructure using code, automating the creation and management of your entire IT environment.Here's how IaC can be your game-changer: 🔹Efficiency Booster: Say goodbye to repetitive tasks! IaC automates everything, freeing you up for more strategic work. 🔹 Consistency Champion: Ensure all your environments are configured identically – no more room for human error. 🔹Repeatability Rockstar: Need to recreate a specific environment? IaC lets you do it with just a few clicks. 🔹 Collaboration Connoisseur: Share your IaC code with colleagues for seamless collaboration and knowledge sharing.Example in Action: ️Imagine you need to set up 10 web servers with the same configuration. Manually, this would take ages. ⏳ With IaC, you define the configuration once in code, then deploy it to all 10 servers in minutes! 🪄Terraform code for creating 10 Azure VMs resource "azurerm_linux_virtual_machine" "vm_tf" { count = 10 name = "example-vm-${count.index}" location = azurerm_resource_group.example.location resource_group_name = azurerm_resource_group. example. name network_interface_ids = [azurerm_network_interface.example[count.index].id] size = "Standard_B1s" source_image_reference { publisher = "Canonical" offer = "0001-com-ubuntu-server-jammy" sku = "22_04-lts-gen2" version = "latest" }}Ready to embrace the future of infrastructure management? IaC is the key!P.S. Have you tried IaC for managing your cloud resources? Share your experiences in the comments!#IaC #infrastructure #automation #devops #cloud #terraform

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  • Pratham Sahni

    Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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    New Open Source Model DBRX!🌟 Hey everyone, check out this awesome news! Say hello to DBRX, the latest gem in the world of AI from Databricks! 🚀DBRX is a transformer-based decoder-only large language model (LLM).Get ready to experience a whole new level of customization and performance that's rewriting the rulebook in language understanding, programming, math, and logic – it outshines GPT-3.5 and rivals Gemini 1.0!📈DBRX represents a significant advancement in AI technology, drawing upon extensive MegaBlocks research and leveraging a Mixture-of-Experts (MoE) model. This innovative approach allows DBRX to achieve remarkable efficiency, boasting 132 billion parameters while operating with only 36 billion.🌟DBRX uses rotary position encodings (RoPE), gated linear units (GLU), and grouped query attention (GQA). It has 16 experts and chooses 4, while Mixtral-8x7B and Grok-1 have 8 experts and choose 2 which improves model quality.And guess what? You can dive into this amazing model through Databricks' GitHub repository and Hugging Face, unlocking endless possibilities to supercharge your AI adventures! 💼https://lnkd.in/ggpp9V-xReady to take your AI journey to new heights? Let's explore the limitless potential of DBRX together!#genai #llm #opensource #innovation #databricks #dbrx

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  • Pratham Sahni

    Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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    Let's explore open-source language models (LLMs) Part 21. Codellama by Meta - Parameters: 7B, 13B, 34B, 70B - Description: Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and supports up to 100k tokens at inference time. The model is designed for general code synthesis and understanding. Codellama-70B is one of the top performing open source model for code generation out there. - Link: https://lnkd.in/gaV3w6qA2. Qwen1.5 by Alibaba Cloud - Parameters: 0.5B, 1.8B, 4B, 7B, 14B, 72B - Description: Qwen1.5 is a transformer-based decoder-only language model pretrained on a large amount of data. It has 32K context length for models of all sizes. It is Multilingual and outperforms GPT3.5 on some of the benchmarks. - Link: https://lnkd.in/gWGs7Su83. DeciCoder by Deci AI - Parameters: 1B, 6B - Description: DeciCoder is a decoder-only code completion model trained on the Python, Java, Javascript, Rust, C++, C, and C# subset of Starcoder Training Dataset. The model uses variable Grouped Query Attention and has a context window of 2k tokens. - Link: https://lnkd.in/g7cED3Gj4. SQLCoder and SQLCoder2 by Defog.ai (YC W23) - Parameters: 7B, 15B, 34B, 70B - Description: SQLCoders are series of state-of-the-art LLM for converting natural language questions to SQL queries. SQLCoder-70B-Alpha outperforms all generalist models (including GPT-4) on text to SQL. - Link: https://lnkd.in/gDDtFQ5U5. Codegen2 and Codegen2.5 by Salesforce - Parameters: 1B, 3.7B, 7B, 16B - Description: CodeGen2 is a family of autoregressive language models for program synthesis (generating executable code given English prompts) with support for more programming languages. - Link: https://lnkd.in/gRyMrSdg5. StableCode by Stability AI - Parameters: 3B - Description: StableCode decoder-only language model pre-trained on 1.3 trillion tokens of diverse textual and code datasets. StableCode is trained on 18 programming languages and demonstrates state-of-the-art performance on the MultiPL-E metrics across multiple programming languages. - Link: https://lnkd.in/gVEwHerb 7. CodeT5+ by Salesforce - Parameters: 110M, 220M, 770M, 2B, 6B, 16B - Description: CodeT5+ are series of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. encoder-only, decoder-only, and encoder-decoder) to support a wide range of code understanding and generation tasks. - Link: https://lnkd.in/g6qy8TWq8. Openchat3.5 by Openchat Community - Parameters: 7B - Description: Openchat-3.5-0106 is 7B parameter model that is on top on MT-bench and HumanEval, MMLU and other benchmarks. - Link: https://lnkd.in/gT5Ezf9T#opensource #ai #generativeai #largelanguagemodels

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  • Pratham Sahni

    Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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    Step into the fascinating world of cutting-edge language models! 👋Let's explore some top open-source language models (LLMs):1. Llama 2 by Meta - Parameters: 7B, 13B, 70B - Description: Llama 2 presents a collection of pretrained and fine-tuned LLMs ranging from 7 billion to 70 billion parameters.2. Falcon by Technology Innovation Institute - Parameters: 7B, 40B, 180B - Description: Falcon, a class of causal decoder-only, boasts a modern architecture optimized for inference, featuring multi-query attention and efficient attention support.3. Mistral by Mistral AI - Parameters: 7B - Description: Mistral-7B, a decoder-based LM released under Apache 2.0, marks Mistral AI's debut in the world of Large Language Models (LLMs).4. Mixtral 8x7B by Mistral AI - Parameters: Active 12B - Description: Mistral 8x7B is a high-quality sparse mixture of experts models (SMoE) with open weights. It follows a decoder-based LM approach and is licensed under Apache 2.0.5. DeciLM by Deci AI - Parameters: 7B - Description: DeciLM-7B, the fastest 7-billion parameter base LLM, redefines benchmarks for speed and accuracy. It employs variable Grouped-Query Attention (GQA) and is released under Apache 2.0.6. MPT by MosaicML (acquired by Databricks) - Parameters: 7B, 30B - Description: MPT models, open source and commercially usable LLMs pre-trained on 1T tokens, follow the GPT-style decoder-only transformer design.7. Vicuna by Lmsys - Parameters: 7B, 13B, 33B - Description: Vicuna, a chat assistant, is trained by fine-tuning Llama 2 on user-shared conversations from ShareGPT. It's an auto-regressive language model based on the transformer architecture.8. XGen by Salesforce - Parameters: 7B - Description: XGen-7B, designed to support longer context windows, is a 7B LLM trained on 8K input sequence length for up to 1.5T tokens. Ideal for tasks like text summarization, question answering, and code generation.9. BERT by Google - Parameters: 110M to 350M - Description: BERT, developed in 2018, is a transformer-based open-source model widely used in various NLP tasks, setting the stage for LLMs.10. BLOOM by BigScience - Parameters: 176B - Description: BLOOM a transformer-based LLM developed collaboratively by over 1,000 AI researchers, is trained on the ROOTS corpus. Fine-tuned for tasks like text summarization, question answering, and text generation.11. Flan-T5 by Google - Parameters: 80M to 11B - Description: FLAN-T5, an enhanced version of T5, excels in efficiency and is open source. Fine-tuned on diverse tasks, including instruction-based tasks.12. GPT-NeoX-20B by EleutherAI - Parameters: 20B - Description: GPT-NeoX is a autoregressive language model, stands out for powerful few-shot reasoning capabilities. Developed on the Pile dataset, it excels in various NLP tasks.Exciting times lie ahead in the realm of AI!#ai #generativeai #opensource #largelanguagemodels 🌐✨

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  • Pratham Sahni

    Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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    Thrilling Update ✨Amazon Web Services (AWS) has unveiled Amazon Q, an AI assistant tailored for AWS. Amazon Q is set to revolutionize the landscape, offering a game-changing shift. It holds the potential to reshape our strategies in handling cloud resources, simplifying the learning curve and providing a transparent insight into operations.The array of possibilities it unfolds is truly remarkable! 🌐 #AmazonQ #innovation #cloud #generativeai #aws #new #aiforcloud

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  • Pratham Sahni

    Ambitious Cloud Architect ☁️ | Web Developer 🌐 | Azure | AWS | GCP | GenAI | DU CS'23 | Building Innovative Cloud Solutions and Dynamic Web Applications

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    I am delighted to announce the successful completion of the "Prompt Engineering Course" and earned a certificate from Cognitive Class (initiative By IBM). This course has been an incredibly valuable equipping me with a comprehensive understanding of Prompt Engineering and I'm so glad to have taken this course.The course covered a wide range of techniques, including zero-shot, few-shot prompting, Persona Patterns, and the Chain of Thoughts (CoT) method. It was a truly immersive learning experience.Furthermore, I am really pumped up to apply the skills I've acquired by working with AI chatbots.If you have an interest in prompt engineering, I wholeheartedly recommend seizing this opportunity to enhance your skills and knowledge.#generatieveai #ai #promptengineering #cognitiveclass #ibm

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Pratham Sahni on LinkedIn: #ollama #ollama #ollama #ai #generativeai #innovation #ollama #opensource… (2024)
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