HIV-1 Recombinant Classification
Attention-based Transformer encoder for HIV-1 recombinant/non-recombinant classification.
Model
- Representation: 3-mer tokenization
- Architecture: Transformer Encoder
- Transformer layers: 2
- Attention heads: 8
- Embedding dimension: 128
- Feed-forward dimension: 256
- Maximum input length: 4,395 3-mers
- Classes:
- Non-Recombinant
- Recombinant
Deployment Model
The deployment model uses post-training dynamic INT8 quantization of eligible Linear layers.
The model is intended for CPU-oriented inference.
The embedding and Transformer attention components are not fully quantized.
Source
The deployed model originates from:
Experiment 5 — Best Transformer
The quantized model was generated as part of:
Experiment 6 — Post-Training Dynamic INT8 Quantization
Reproducibility
Source FP32 checkpoint SHA256:
2a5641ef378603c6d9f2e98a1a4a29bc428e6774c92ffb80d39f1c90c01bbb83
Dynamic INT8 checkpoint SHA256:
69935da6113edfb1f6eded7ded6bb6b6219b173aa24325b0e26d8da43012b289
Train-only vocabulary SHA256:
1f626e456022bafa8547b925bdd03907fabcb1d3a8fc09353f008b63536ad0ae
Intended Use
This model is a research prototype for HIV-1 genomic surveillance and recombinant classification research.
It is not a clinical diagnostic tool and should not be used for patient diagnosis or clinical decision-making.
Files
model/Transformer_Encoder_best_DYNAMIC_INT8.pt- Deployment model
model/Transformer_Encoder_best.pt- Original FP32 source checkpoint
tokenizer/TRAIN_ONLY_3MER_VOCABULARY.csv- Training-only 3-mer vocabulary
metadata/model_config.json- Architecture and deployment metadata
metadata/file_manifest.json- File integrity manifest
Research Project
HIV-1 Subtype/Recombinant Classification
Attention-Based Deep Learning for HIV-1 Genomic Surveillance and Subtype Classification in Uganda.