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- Alternatives
The best alternatives to LLM Explorer are Giskard, Sibyl AI, and Arbor. If these 3 options don't work for you, we've listed a few more alternatives below.
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PromotedBest alternatives to LLM Explorer
- Fast LLM System at scale 🛡️ Detect hallucinations & biases automatically 🔍 Enterprise LLM Evaluation Hub ☁️ Self-hosted / cloud 🤝 Integrated with 🤗, MLFlow, W&B
- The world's first spiritual AI for Neophytes, Adepts, and Holistic Practitioners trained on volumes of rare metaphysical knowledge and experience. Freemium Plan, AI-to-AI Conversations, Chat Visualization, Speech Enabled, Multilingual support in 99 Languages.
- Arbor is a platform that provides aggregated summaries by clustering, deduplicating, and summarizing content, sentence by sentence, from various sources on the same topic. Arbor's mission is to reindex the internet and save it from AI-generated SEO articles.
- Featuring a curated list of LLMOps companies, open-source LLM modules, educational resources, funding news, and much more. LLMOps.Space is a global community for LLM practitioners. Discover, learn, and participate - everything related to LLMs!
- Ludwig, open-sourced at Uber, makes it easy for any developer to build state-of-the-art ML models with its declarative interface. Ludwig v0.8 is the first low-code open-source framework optimized for efficiently building custom LLMs with your private data.
- Mold/Train multiple LLMs to automate ANYTHING you want and export as API, TelegramBot, or WhatsApp. Our LLM infrastructure is embedded with most of the web data in real time. Context and sources are not limited by context length.
- Eternity AI is a research project at IIT-Patna focused onbuilding a human-first language model capable of mimicking human behaviour via accessing real-time Internet, reducing hallucinations, and training on additional 100K+ behaviour parameters.
- ✨ Turn full websites into datasets for building custom LLMs with Webᵀ Crawl. Give us just 1️⃣ URL and let Webᵀ Crawl handle the rest. Quickly turn full websites & content (like PDFs, FAQ, etc.) into • Prompts for fine-tuning • Chunks for vector databases
- Qwen1.5-MoE-A2.7B is a small mixture-of-expert (MoE) model with only 2.7 billion activated parameters yet matches the performance of state-of-the-art 7B models like Mistral 7B and Qwen1.5-7B.
