Model overview
CodonFM predicts masked codons in mRNA sequences from codon-level context to enable variant effect interpretation and codon optimization as part of NVIDIA’s CodonFM Encodon family. For this family of models we have 4 models. The first set of 3 models are with randomly masked tokens with 80 million, 600 million and 1 Billion parameter. The fourth model is with 1 Billion parameters but is trained with codon frequency aware masking. Github 10/27/2025 via https://github.com/NVIDIA-Digital-Bio/CodonFM Hugging Face 10/27/2025 via: - Random Mask - https://huggingface.co/nvidia/NV-CodonFM-Encodon-1B-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-600M-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-80M-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-1B-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-600M-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-80M-v1 - Codon Frequency Aware Mask - https://huggingface.co/nvidia/NV-CodonFM-Encodon-Cdwt-1B-v1 - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-Cdwt-1B-v1 The NVIDIA CodonFM Encodon family features **Transformer-based architectures** tailored for codon-level sequence modeling in mRNA. Each model applies a **masked language modeling (MLM)** objective to predict masked codons from surrounding context of 2046 codons, enabling genome-scale codon optimization and synonymous variant interpretation.
Key features
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Limitations & considerations
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Public benchmarks & run configuration
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Sources & traceability
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