Instructions to use HuggingFaceBio/Carbon-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceBio/Carbon-3B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HuggingFaceBio/Carbon-3B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -43,7 +43,7 @@ Carbon-3B is the **flagship** model of the Carbon family. We also release [**Car
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- **Native context: 32,768 tokens β 197 kbp.** Extendable to 65,536 tokens (β 393 kbp) at inference time using YaRN.
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- **Trained with a Cross-Entropy β Factorised Nucleotide Supervision (FNS) objective schedule** to bridge coarse tokenization and single-nucleotide resolution (see the Carbon technical report).
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- **Metadata-conditioned**: optional species-type and gene-type metadata tokens enable conditional generation.
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- **Efficient inference**:
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Across our zero-shot evaluation suite β sequence recovery, four variant-effect-prediction (VEP) benchmarks (ClinVar coding, ClinVar non-coding, BRCA2, TraitGym Mendelian), and two sequence-level perturbation tasks (TATA-box and synonymous codon) β Carbon-3B is competitive with Evo2-7B. It additionally works well on long context and retrieves needles reliably from up to β 393 kbp of distal context on the Genome-NIAH long-context benchmark, while remaining several times faster than Evo2-7B.
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- **Native context: 32,768 tokens β 197 kbp.** Extendable to 65,536 tokens (β 393 kbp) at inference time using YaRN.
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- **Trained with a Cross-Entropy β Factorised Nucleotide Supervision (FNS) objective schedule** to bridge coarse tokenization and single-nucleotide resolution (see the Carbon technical report).
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- **Metadata-conditioned**: optional species-type and gene-type metadata tokens enable conditional generation.
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- **Efficient inference**: compatible with vLLM and other inference engines. Can generate over 100,000 base-pairs per second on a single H100 GPU.
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Across our zero-shot evaluation suite β sequence recovery, four variant-effect-prediction (VEP) benchmarks (ClinVar coding, ClinVar non-coding, BRCA2, TraitGym Mendelian), and two sequence-level perturbation tasks (TATA-box and synonymous codon) β Carbon-3B is competitive with Evo2-7B. It additionally works well on long context and retrieves needles reliably from up to β 393 kbp of distal context on the Genome-NIAH long-context benchmark, while remaining several times faster than Evo2-7B.
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