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tr:nth-child(odd)]:bg-surface-sunken\">

Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	Not Applicable
Bias Metric (If Measured):	None
Measures taken to mitigate against unwanted bias:	Not Applicable

\n
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Model Overview

Description:

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.

An additional set of accelerated checkpoints also available for use.

This model is ready for commercial/non-commercial use.

License/Terms of Use

Governing Terms: Use of this model is governed by the NVIDIA Open Model License Agreement.

Deployment Geography:

Global

Use Case:

\n
Optimized Expression and Stability for mRNA design: To design mRNAs with codon usage patterns that enhance translation efficiency, protein yield, and transcript stability across specific cell types and tissues.
\n
\n
Variant Interpretation for pathogenicity: To identify and prioritize functional synonymous and missense variants in the context of diseases.
\n

Release Date:

Github 10/27/2025 via https://github.com/NVIDIA-Digital-Bio/CodonFM
\nHugging Face 10/27/2025 via:

Random Mask\n
\n
Codon Frequency Aware Mask\n
- https://huggingface.co/nvidia/NV-CodonFM-Encodon-Cdwt-1B-v1
- https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-Cdwt-1B-v1
\n

NGC 10/27/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/nv_codonfm_encodon

Model Architecture:

The NVIDIA CodonFM Encodon family features Transformer-based architectures tailored for codon-level sequence modeling in mRNA.\nEach 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.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name	Parameters
Encodon-80M	7.68 × 10⁷
Encodon-600M	6.09 × 10⁸
Encodon-1B	9.11 × 10⁸
Encodon-Cdwt-1B	9.11 × 10⁸

\n
\n

Input:

Input Type(s): Text (mRNA Sequence)
\nInput Format: fasta files converted to memmaps
\nInput Parameters: 1D
\nOther Properties Related to Input: mRNA sequence represented as a string of codons, of maximum length 2046. Longer sequences are automatically truncated to this length

Output:

Output Type(s): mRNA Sequence
\nOutput Format: Text
\nOutput Parameters: 2D
\nOther Properties Related to Output: Numeric 2D tensor with float-point values representing probabilities of a given codon at a give position within the sequence

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s):

PyTorch - 2.5.1

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper

Preferred/Supported Operating System(s):

Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

NV-CodonFM-Encodon-80M-v1
NV-CodonFM-Encodon-600M-v1
NV-CodonFM-Encodon-1B-v1
NV-CodonFM-Encodon-Cdwt-1B-v1
NV-CodonFM-Encodon-TE-80M-v1
NV-CodonFM-Encodon-TE-600M-v1
NV-CodonFM-Encodon-TE-1B-v1
NV-CodonFM-Encodon-TE-Cdwt-1B-v1

Training, Testing, and Evaluation Datasets:

Training Dataset:

Link: RefSeq Data from NCBI

Data Modality

Text (mRNA Sequencing data)

Properties: Coding sequences from the NCBI RefSeq database (release 2024-04) were used for training. A total of >130M non-viral protein-coding sequences from >22,000 species were included, comprising >2,000 eukaryotes. Sequences not divisible by three or containing ambiguous bases were removed. Taxonomy-level deduplication using MMSeqs eliminated redundant entries, and coding sequences from bacteria pathogenic to humans were excluded. The resulting dataset was partitioned into nine species groups: primates, archaea, bacteria, fungi, invertebrate, plant, protozoa, non-primate mammals, and non-mammal vertebrates. Sequences were clustered by similarity and then split into training and validation sets with stratification across groups to ensure balanced representation.

Encodon models use codon-level tokenization, processing input sequences of up to 2,046 codons. Each model was trained using a masked language modeling (MLM) objective, where randomly masked codons were predicted from their context. The Encodon pretraining dataset was sorted based on sequence taxonomy to maintain species balance, and sequence subsets could be resampled dynamically.

Non-Audio, Image, Text Training Data Size: NCBI RefSeq genomes FTP directory currently contains over 395,000 genomes totaling approximately 3.3 terabases (Tb)

Data Collection Method for all data Dataset:

Automatic/Sensors

Labeling Method by Dataset:

Not Applicable

Evaluation Datasets:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Link	Properties
ClinVar Variant Interpretation	This task involves classifying genetic variants from ClinVar, a publicly available database that aggregates information about the clinical significance of human genetic variants, into pathogenic or benign categories based on their coding sequence context
Denovo variant classification	This task uses variants from the Deciphering Developmental Disorders (DDD) and autism spectrum disorder (ASD) cohort study, which catalogs genetic mutations linked to rare pediatric and developmental diseases, to evaluate classification of pathogenic versus benign variants based on coding sequence context.
mRNA Translation Efficiency	This task predicts ribosome profiling signal intensity along coding sequences, evaluating how well models capture translation efficiency and codon-level regulation from sequence context.
Protein Abundance	This task predicts fluorescent protein expression levels (mRFP) from coding sequences, testing how accurately models capture codon-dependent effects on translation efficiency and protein abundance.

Data Collection Method for all data Dataset:

Human

Labeling Method by Dataset:

Not Applicable

Inference:

Acceleration Engine: None
\nTest Hardware: A100

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.

Users are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

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\n\nAn additional set of accelerated checkpoints also available for use.\n\nThis model is ready for commercial/non-commercial use.
\n\n\n## License/Terms of Use\n\nGoverning Terms: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## Deployment Geography:\nGlobal
\n\n## Use Case:
\n\n- Optimized Expression and Stability for mRNA design: To design mRNAs with codon usage patterns that enhance translation efficiency, protein yield, and transcript stability across specific cell types and tissues.\n\n- Variant Interpretation for pathogenicity: To identify and prioritize functional synonymous and missense variants in the context of diseases.
\n\n## Release Date:
\nGithub 10/27/2025 via https://github.com/NVIDIA-Digital-Bio/CodonFM
\nHugging Face 10/27/2025 via:\n- Random Mask\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-1B-v1\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-600M-v1\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-80M-v1\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-1B-v1\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-600M-v1\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-80M-v1\n- Codon Frequency Aware Mask\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-Cdwt-1B-v1\n - https://huggingface.co/nvidia/NV-CodonFM-Encodon-TE-Cdwt-1B-v1
\n\nNGC 10/27/2025 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/nv_codonfm_encodon
\n\n\n\n## Model Architecture:\nThe NVIDIA CodonFM Encodon family features **Transformer-based architectures** tailored for codon-level sequence modeling in mRNA.\nEach 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.\n\n| Model Name | Parameters |\n|-------------|-------------:|\n| Encodon-80M | 7.68 × 10⁷ |\n| Encodon-600M | 6.09 × 10⁸ |\n| Encodon-1B | 9.11 × 10⁸ |\n| Encodon-Cdwt-1B | 9.11 × 10⁸ |\n
\n\n## Input:
\n**Input Type(s):** Text (mRNA Sequence)
\n**Input Format:** fasta files converted to memmaps
\n**Input Parameters:** 1D
\n**Other Properties Related to Input:** mRNA sequence represented as a string of codons, of maximum length 2046. Longer sequences are automatically truncated to this length\n\n\n## Output:
\n**Output Type(s):** mRNA Sequence
\n**Output Format:** Text
\n**Output Parameters:** 2D
\n**Other Properties Related to Output:** Numeric 2D tensor with float-point values representing probabilities of a given codon at a give position within the sequence
\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
\n\n## Software Integration:\n**Runtime Engine(s):**\n* PyTorch - 2.5.1\n\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper\n\n\n**Preferred/Supported Operating System(s):**\n* Linux
\n\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
\n\n\n## Model Version(s):\n- NV-CodonFM-Encodon-80M-v1
\n- NV-CodonFM-Encodon-600M-v1
\n- NV-CodonFM-Encodon-1B-v1
\n- NV-CodonFM-Encodon-Cdwt-1B-v1
\n- NV-CodonFM-Encodon-TE-80M-v1
\n- NV-CodonFM-Encodon-TE-600M-v1
\n- NV-CodonFM-Encodon-TE-1B-v1
\n- NV-CodonFM-Encodon-TE-Cdwt-1B-v1
\n\n\n## Training, Testing, and Evaluation Datasets:\n\n# Training Dataset:\n\n**Link:** [RefSeq Data from NCBI](https://ftp.ncbi.nlm.nih.gov/genomes/refseq/)
\n\n**Data Modality**
\n* Text (mRNA Sequencing data)
\n\n**Properties:** Coding sequences from the NCBI RefSeq database (release 2024-04) were used for training. A total of >130M non-viral protein-coding sequences from >22,000 species were included, comprising >2,000 eukaryotes. Sequences not divisible by three or containing ambiguous bases were removed. Taxonomy-level deduplication using MMSeqs eliminated redundant entries, and coding sequences from bacteria pathogenic to humans were excluded. The resulting dataset was partitioned into nine species groups: primates, archaea, bacteria, fungi, invertebrate, plant, protozoa, non-primate mammals, and non-mammal vertebrates. Sequences were clustered by similarity and then split into training and validation sets with stratification across groups to ensure balanced representation.\n\nEncodon models use codon-level tokenization, processing input sequences of up to 2,046 codons. Each model was trained using a masked language modeling (MLM) objective, where randomly masked codons were predicted from their context. The Encodon pretraining dataset was sorted based on sequence taxonomy to maintain species balance, and sequence subsets could be resampled dynamically.
\n\n**Non-Audio, Image, Text Training Data Size:** NCBI RefSeq genomes FTP directory currently contains over 395,000 genomes totaling approximately 3.3 terabases (Tb)\n\n**Data Collection Method for all data Dataset:**\n* Automatic/Sensors
\n\n**Labeling Method by Dataset:**\n* Not Applicable
\n\n\n### Evaluation Datasets:\n\n| Link | Properties |\n|------|--------------------------------------------------|\n| [ClinVar Variant Interpretation](https://www.ncbi.nlm.nih.gov/clinvar/) | This task involves classifying genetic variants from ClinVar, a publicly available database that aggregates information about the clinical significance of human genetic variants, into pathogenic or benign categories based on their coding sequence context|\n| [Denovo variant classification](https://www.nature.com/articles/s41588-022-01148-2) | This task uses variants from the Deciphering Developmental Disorders (DDD) and autism spectrum disorder (ASD) cohort study, which catalogs genetic mutations linked to rare pediatric and developmental diseases, to evaluate classification of pathogenic versus benign variants based on coding sequence context.|\n| [mRNA Translation Efficiency](https://www.biorxiv.org/content/10.1101/2024.08.11.607362v2.full.pdf) | This task predicts ribosome profiling signal intensity along coding sequences, evaluating how well models capture translation efficiency and codon-level regulation from sequence context.|\n| [Protein Abundance](https://www.biorxiv.org/content/10.1101/2023.09.09.556981v1.full.pdf) | This task predicts fluorescent protein expression levels (mRFP) from coding sequences, testing how accurately models capture codon-dependent effects on translation efficiency and protein abundance. |\n\n**Data Collection Method for all data Dataset:**\n* Human
\n\n**Labeling Method by Dataset:**\n* Not Applicable
\n\n# Inference:\n**Acceleration Engine:** None
\n**Test Hardware:** A100
\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.\n\nUsers are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.
\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
","displayName","CodonFM Encodon",["D",1761595749360],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/models/nv_codonfm_encodon","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | Not Applicable\nBias Metric (If Measured): | None
\nMeasures taken to mitigate against unwanted bias: | Not Applicable
\n
","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | mRNA sequence prediction\nModel Type: | Transformer\nIntended Users: | Molecular and cellular biologists, Genomics researchers studying coding variants, Synthetic biology and mRNA therapeutics teams, Computational biologists working on codon optimization or protein expression.\nOutput: | Text
\nDescribe how the model works: | The model takes biological coding sequences as input, converts them into embeddings, and processes them through multiple transformer encoder layers featuring multi-head attention to capture context and pattern dependencies. Rotary positional encoding and feed-forward networks enhance the model’s ability to interpret the order and relationships of codons across the sequence for masked language modeling tasks. The final layers predict missing or masked codons, enabling the model to infer and reconstruct biologically relevant information from partial sequence data.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations & Mitigation: | The models were pre-trained on mRNA sequences. The model may not perform well if there is a difference from coding sequence type inputs and the training data. Any input sequence beyond 2046 codons requires truncation/windowing.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Significance Testing (-log10(p)), Classification (AUROC), Regression (Spearman r, r²)\nPotential Known Risks: | If the model does not work as intended, it may generate inaccurate predictions for biological sequences, leading to mischaracterization of codon relationships or masked inputs. Specifically, if the model inaccurately characterizes depth or other sequence properties, critical biological features may be distorted or missed, resulting in unreliable data interpretation or downstream analysis. Such failures could compromise clinical or research outcomes that depend on high-fidelity biological sequence understanding.\nLicensing: | Use of this model is governed by the (NVIDIA Open Model License Agreement)[https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/].","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | No\nWas consent obtained for any personal data used? | Not Applicable\n
How often is dataset reviewed? | During dataset creation, model training\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data.\nApplicable Privacy Policy | [Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/)
","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Genomics/Biotechnology\nDescribe the life critical impact (if present). | Not Applicable
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