Question: I need a platform that provides access to a large community of ML developers and researchers, as well as the latest open-source tools and resources.

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Hugging Face

If you want a platform that puts you in touch with a broader community of ML developers and researchers and gives you access to the latest open-source tools and resources, Hugging Face is a great option. This open-source, collaborative platform is designed to provide a full ecosystem for model collaboration, dataset exploration and application development. With more than 400,000 models, 150,000 applications and demos, and more than 100,000 public datasets, it lets you host models, datasets and applications for free. It also offers more advanced features like compute options, single sign-on (SSO) and SAML support, making it good for individuals and companies.

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LLM Explorer

Another resource is LLM Explorer, an all-in-one platform with a massive library of 35,809 open-source Large Language Models (LLMs) and Small Language Models (SLMs). You can explore and compare models based on a range of attributes and abilities, and the platform is updated with the latest developments in the field. It's good for AI enthusiasts, researchers and industry professionals who want to quickly find and use the best language models for their needs.

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Humanloop

For those trying to manage and optimize LLM applications, Humanloop is a collaborative platform that tries to solve pain points like suboptimal workflows and bad collaboration. It has a collaborative prompt management system, evaluation and monitoring suite, and customization tools to connect private data and fine-tune models. It's geared for product teams and developers, and Humanloop supports popular LLM providers and can be integrated with Python and TypeScript SDKs.

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Flowise

Last, Flowise is an open-source, low-code tool that lets developers build custom LLM orchestration flows and AI agents. With a graphical interface and more than 100 integrations, including Langchain and LlamaIndex, it makes it easier to build and integrate LLM apps. Flowise can be self-hosted on AWS, Azure and GCP, and it's a good option for building and iterating on AI solutions.

Additional AI Projects

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Langfuse

Debug, analyze, and experiment with large language models through tracing, prompt management, evaluation, analytics, and a playground for testing and optimization.

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Superpipe

Build, test, and deploy Large Language Model pipelines on your own infrastructure, optimizing results with multistep pipelines, dataset management, and experimentation tracking.

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Kolank

Access multiple Large Language Models through a single API and browser interface, with smart routing and resilience for high-quality results and cost savings.

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Vellum

Manage the full lifecycle of LLM-powered apps, from selecting prompts and models to deploying and iterating on them in production, with a suite of integrated tools.

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Klu

Streamline generative AI application development with collaborative prompt engineering, rapid iteration, and built-in analytics for optimized model fine-tuning.

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LLMStack

Build sophisticated AI applications by chaining multiple large language models, importing diverse data types, and leveraging no-code development.

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Meta Llama

Accessible and responsible AI development with open-source language models for various tasks, including programming, translation, and dialogue generation.

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HoneyHive

Collaborative LLMOps environment for testing, evaluating, and deploying GenAI applications, with features for observability, dataset management, and prompt optimization.

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LlamaIndex

Connects custom data sources to large language models, enabling easy integration into production-ready applications with support for 160+ data sources.

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Predibase

Fine-tune and serve large language models efficiently and cost-effectively, with features like quantization, low-rank adaptation, and memory-efficient distributed training.

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LastMile AI

Streamline generative AI application development with automated evaluators, debuggers, and expert support, enabling confident productionization and optimal performance.

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Abacus.AI

Build and deploy custom AI agents and systems at scale, leveraging generative AI and novel neural network techniques for automation and prediction.

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Airtrain AI

Experiment with 27+ large language models, fine-tune on your data, and compare results without coding, reducing costs by up to 90%.

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MLflow

Manage the full lifecycle of ML projects, from experimentation to production, with a single environment for tracking, visualizing, and deploying models.

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Keywords AI

Streamline AI application development with a unified platform offering scalable API endpoints, easy integration, and optimized tools for development and monitoring.

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Prem

Accelerate personalized Large Language Model deployment with a developer-friendly environment, fine-tuning, and on-premise control, ensuring data sovereignty and customization.

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ThirdAI

Run private, custom AI models on commodity hardware with sub-millisecond latency inference, no specialized hardware required, for various applications.

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Dify

Build and run generative AI apps with a graphical interface, custom agents, and advanced tools for secure, efficient, and autonomous AI development.

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Openlayer

Build and deploy high-quality AI models with robust testing, evaluation, and observability tools, ensuring reliable performance and trustworthiness in production.

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Dataloop

Unify data, models, and workflows in one environment, automating pipelines and incorporating human feedback to accelerate AI application development and improve quality.