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Alternatives to MACE Foundations

Terra by Broad
Terra by BroadTerra is an open-source, cloud-based platform for biomedical data analysis, secure data sharing, and global scientific collaboration. Built by the Broad Institute of MIT and Harvard.
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Phi AI (Microsoft)
Phi AI (Microsoft)Explore Microsoft Phi, a family of efficient open small language models (SLMs) for generative AI. Available on Azure AI Foundry for developers, enterprises, and researchers.
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FastChat
FastChatFastChat is an open-source platform for training, serving, and evaluating large language models. Powers Chatbot Arena with OpenAI-compatible APIs and a distributed multi-model serving system.
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MatterSim
MatterSimMatterSim is Microsoft's open-source deep learning atomistic model for simulating material properties across elements, temperatures, and pressures using M3GNet architecture.
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PrimateAI
PrimateAIPrimateAI is an open-source deep residual neural network by Illumina that scores the pathogenicity of missense variants using primate sequence data.
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Orb Materials Model
Orb Materials ModelOrbital Materials provides open-source AI foundation models for materials science and molecular research, including OrbMol and the MofasaDB dataset, hosted on Hugging Face.
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EleutherAI Open LLM
EleutherAI Open LLMEleutherAI is a nonprofit AI research collective that trains and releases open-source LLMs, advancing interpretability, alignment, and language modeling research.
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Jamba by AI21 Labs
Jamba by AI21 LabsJamba is AI21 Labs' family of open, hyper-efficient LLMs with a 256K context window and hybrid Mamba-Transformer architecture, designed for secure enterprise deployment.
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Gorilla LLM
Gorilla LLMGorilla is an open-source LLM from UC Berkeley that connects with massive APIs, enabling state-of-the-art function calling across Python, Java, JavaScript, and REST APIs. Includes BFCL benchmarking, RAFT fine-tuning, and the GoEX execution engine.
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HumanEval
HumanEvalHumanEval is OpenAI's open-source benchmark of 164 hand-written Python problems for evaluating large language models on code generation using functional correctness.
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