What is Carbon?
Carbon is a family of autoregressive genomic foundation models from Hugging Face Bio for DNA generation, sequence likelihood scoring, and reference-versus-alternate sequence comparison. It treats DNA as a language modeling problem, but uses a DNA-specific 6-mer tokenizer instead of ordinary text tokenization for bases.
ProteinIQ runs Carbon on canonical DNA sequences containing only A, C, G, and T. Ambiguity bases such as N, degenerate IUPAC codes, RNA U, and non-sequence annotations are not valid Carbon input.
Carbon is most useful when the question depends on learned genomic sequence context: extending a DNA sequence, ranking sequences by model likelihood, or asking whether an alternate sequence is more or less likely than a reference under the selected model. For deterministic sequence editing, use DNA mutator, DNA shuffle, or Reverse complement instead.
How to use Carbon online
Run Carbon online by pasting canonical DNA or uploading FASTA or text files, then choosing generation, scoring, or compare mode. ProteinIQ runs the selected Hugging Face Carbon checkpoint and returns generated DNA, likelihood scores, reference-versus-alternate deltas, spreadsheet data, and downloadable CSV or JSON files.
Carbon requires at least 6 bp because each DNA token represents one non-overlapping 6-mer. Sequences longer than the selected context window are scored or extended using the rightmost context segment. The maximum accepted sequence length is 786,432 bp.
Modes
In Compare mode, the reference and alternate can be entered in the dedicated settings fields. If those fields are empty, Carbon uses the first two submitted sequences.
Model choices
Carbon uses one DNA token per 6 bases. A 32,768-token context is therefore about 196,608 bp before accounting for model tags and generation budget.
YaRN long-context inference is available for Carbon-3B and Carbon-8B. Carbon-500M runs at its native 8,192-token context.
Settings
Understanding Carbon results
Generation results
Generation mode returns one row per input sequence.
The highlighted bases in the result view are the generated continuation, not the full submitted sequence. Downloaded generation jobs also include carbon-generated-sequences.fasta, where each record contains the context and generated continuation together.
Score results
Score mode reports log likelihoods for each sequence.
mean_logp is usually the most useful score for comparing sequences of similar length. It is not a calibrated biological effect size, binding score, pathogenicity probability, or expression measurement.
Compare results
Compare mode returns one row for the reference-versus-alternate pair.
For single-base or short edits in a fixed-length context, delta_mean_logp is the clearest comparison column. For insertions, deletions, or sequences of different length, inspect both mean and total deltas because length changes affect total log probability directly.
Downloaded files
How Carbon works
Carbon is a decoder-only Transformer model family trained on DNA and RNA sequence data. The key modeling choice is its hybrid tokenizer: English and metadata tokens use a text vocabulary, while DNA inside a <dna> block uses fixed non-overlapping 6-mers. ProteinIQ handles the DNA tag internally, so submitted DNA is tokenized in Carbon's DNA mode.
The 6-mer design improves efficiency because each model token represents 6 bp. It also creates practical constraints. Input must be canonical DNA, and the scored or generated context is aligned to 6-base boundaries. Carbon trims to the rightmost usable context when an input is longer than the selected window because autoregressive generation and scoring depend on the sequence immediately before the predicted bases.
Likelihood scoring follows the usual causal language model interpretation: the model estimates how probable each next DNA token or base is given the previous context. Higher log probability means the sequence is more expected under the selected Carbon checkpoint and context, not necessarily more functional in an experiment.
YaRN extends the rotary-position context used by the model. It is useful for long genomic contexts, but longer windows increase runtime and memory use, and very long extrapolated contexts can reduce retrieval quality. For short sequence scoring or generation, the native context is usually easier to interpret.
When to use Carbon vs alternatives
Carbon fits learned DNA sequence modeling tasks: generation, likelihood ranking, and zero-shot comparison of reference and alternate DNA sequences. It is not a multiple sequence alignment tool, variant annotation database, or wet-lab validation substitute.
Use AlphaGenome when the goal is variant effect prediction against genomic functional tracks rather than model likelihood. Use DNA to Protein Converter for translation, DNA to RNA converter for transcription, and Random DNA when a random control sequence is needed without learned genomic context.
