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In the era of advanced language model applications, developers and data scientists are continually looking for efficient tools to build, deploy, and manage their projects. As large language models (LLMs) like GPT-4 gain popularity, more people are looking to leverage these powerful models in their own applications. However, working with LLM can be complex without the right tools.
That's why I've put together this list of five essential tools that can significantly improve the development and deployment of LLM-based applications. Whether you're just starting out or an experienced machine learning engineer, these tools will help you be more productive and create higher-quality LLM projects.
hugging face It is more than just an ai platform; is a comprehensive ecosystem for hosting models, datasets, and demos. It supports multiple frameworks that allow users to train, tune, evaluate, and generate content in multiple forms such as images, text, and audio. Combining a wide selection of models, community resources, and developer-friendly APIs into a single platform is why Hugging Face has become a go-to destination for many ai professionals and ML engineers.
Learn how to tune the Mistral ai 7B LLM using Hugging Face AutoTrain and push the model to the Hugging Face Hub.
ai/langchain” rel=”noopener” target=”_blank”>LangChain is a tool that uses a composability approach to create applications with LLM. It is widely used to develop context-aware applications by integrating different context sources with language models. Additionally, you can use a language model to reason about actions or responses based on the context provided. The LangChain ai team recently introduced LangSmith, a new tool that provides a unified development platform to increase the speed and efficiency of LLM application production.
If you are new to ai development, check out the LangChain cheat sheet to understand the Python API and other functionalities.
Quadrant is a Rust-based vector similarity database and search engine that provides a production-ready service with a simple API. It is designed to provide extended filtering support, making it ideal for applications that use neural networks or semantic-based matching. Qdrant's speed and reliability under high loads make it the best choice for turning embedded or neural network encoders into comprehensive applications for comparison, search, recommendation, and more. You can also try a fully managed Qdrant Cloud service, which includes a free tier, available for ease of use.
Read the 5 Best Vector Databases You Should Try in 2024 for other alternatives to Qdrant.
ml flow now includes support for LLM and offers implementation, evaluation and experiment monitoring solutions. It simplifies the integration of LLM capabilities into applications by introducing features such as MLflow Deployments Server for LLM, LLM Assessment, and Rapid Engineering UI. These tools help you navigate the complex LLM landscape, comparing key models, providers, and directions to find the best fit for your project.
Check out the list of 5 free courses to master MLOps.
vllm is a high-performance, memory-efficient inference and service engine for LLM. Known for its next-generation service performance and efficient key and value memory management, vLLM offers features such as continuous batch processing, optimized CUDA cores, and support for NVIDIA CUDA and AMD ROCm. Its flexibility and ease of use, including integration with popular Hugging Face models and various decoding algorithms, make it a valuable tool for LLM inference and servicing.
Each of these five tools brings unique strengths, whether in hosting, context awareness, search capabilities, implementation, or inference efficiency. By leveraging these tools, developers and data scientists can significantly streamline their workflows and elevate the quality of their LLM applications.
Get inspired and build ai-models” rel=”noopener” target=”_blank”>5 projects with generative ai models and open source tools.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master's degree in technology Management and a Bachelor's degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
