VenusGenome-1.05B
VenusGenome-1.05B is a 44-layer, width-1408 genome encoder with 1,048,191,308
parameters. It contextualizes ordered gene-level protein representations and
returns 1408-dimensional gene- and genome-level embeddings.
Usage
Install the inference-only release:
1git clone --branch release/inference-v1 \
2 https://github.com/zhangliang-sys/VenusGenome.git
3cd VenusGenome
4pip install -e '.[proteins]'
1from venusgenome import VenusGenome
2
3model = VenusGenome.from_pretrained("1.05b", device="cuda")
4outputs, genome, preprocessing = model.encode_genome_files(
5 "genome.gff",
6 "genome.faa",
7 esm_device="cuda",
8)
This path matches GFF CDS features to FAA proteins, generates per-protein
ESM-2 esm2_t12_35M_UR50D representations, and runs VenusGenome. Precomputed
[n_genes, 480] protein embeddings can still be passed to encode_genome.
The model returns gene_level, mean_pool, and global_token arrays.
Genomes longer than 4096 genes are processed in contiguous chunks.
Checkpoint
This repository contains encoder-only BF16 safetensors from the mask-75%
pretraining run at step 40,000. Decoder and optimizer state are not included.
The architecture is recorded in config.json, and SHA256SUMS records the
weight checksum.
Limitations
The model does not consume raw nucleotide sequence directly. Its outputs
depend on upstream gene calling, genomic ordering, and ESM-2 protein
embeddings. Chunking removes attention across chunk boundaries. Evaluate
performance on the target organism distribution and downstream task.
The current repository has no declared open-source model-weight license.