Publish PRTS inference checkpoint under Apache-2.0

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README.md

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+---
+license: apache-2.0
+tags:
+- biology
+- rna-seq
+- tabular-classification
+- tabular-regression
+- survival-analysis
+- in-context-learning
+datasets:
+- hanzi-bulk-collab/bulk-datasets-private
+---
+
+# PRTS
+
+PRTS (Prior-trained RNA-seq Task Solver) predicts binary, multiclass, continuous and survival endpoints from bulk transcriptomes in context. A labelled cohort provides the context for predictions on new samples.
+
+This repository provides the evaluated checkpoint for **In-context learning predicts clinical endpoints from bulk transcriptomes across diseases and platforms**, by Han Wang, Lennart Purucker, Huafeng Liu, Ziyin Luo, Junfeng Hao, Chao Zhu and Chunjie Tian.
+
+- [Code and applications](https://github.com/Hanziwww/PRTS)
+- [PreBULK, RealBulk and benchmark data](https://huggingface.co/datasets/hanzi-bulk-collab/bulk-datasets-private)
+- Architecture: 219,595,334 parameters in 471 tensors; four cell blocks and twenty row blocks.
+- File: `prts.pt`, a PyTorch checkpoint containing `config.model` and the `model` state dictionary.
+- Export: model configuration and all parameter tensors are unchanged from the author-supplied checkpoint. Training-run bookkeeping and machine-specific paths are omitted from this inference export.
+- [Checkpoint size and SHA-256](checkpoint_metadata.json)
+
+## Load the model
+
+```bash
+pip install "git+https://github.com/Hanziwww/PRTS.git#subdirectory=prts" huggingface_hub
+```
+
+```python
+from huggingface_hub import hf_hub_download
+from prts import load
+
+checkpoint = hf_hub_download("HanZiwww/PRTS", "prts.pt")
+model = load(checkpoint, device="cpu")  # use "cuda" for a CUDA GPU
+```
+
+See the [inference documentation](https://github.com/Hanziwww/PRTS/tree/main/prts) for expression scaling, task construction and prediction outputs. The default Thinking preset averages four passes; the Off preset is also available.
+
+## Scope and limitations
+
+The model was evaluated on retrospective public transcriptomic cohorts. Accuracy varies with endpoint, cohort, platform and the reference samples supplied as context. Cross-cohort and unseen-site evaluation should be considered when assessing transfer to a new study. This repository supplies a research model; it is not a validated clinical diagnostic device.
+
+## Licence
+
+The checkpoint and author-owned code are available under Apache License 2.0. Original datasets and third-party code retain their own terms.

checkpoint_metadata.json

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+{
+  "file": "prts.pt",
+  "sha256": "a189221c588e2404efe97e91e3cd5d0aa08b47de9633823fa17df20d02bfaa4c",
+  "bytes": 878530652,
+  "tensors": 471,
+  "parameters": 219595334,
+  "format": "PyTorch checkpoint with config.model and model state_dict",
+  "export": "Inference-only export; model configuration and all tensors match the author-supplied checkpoint exactly.",
+  "license": "Apache-2.0"
+}

config.json

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+{
+  "architecture": "cell_row",
+  "cell_layers": 4,
+  "summary_tokens": 8,
+  "summary_dim": 128,
+  "summary_heads": 4,
+  "row_heads": 16,
+  "row_checkpoint": false,
+  "embed_dim": 192,
+  "n_heads": 6,
+  "n_layers": 24,
+  "ff_factor": 2,
+  "dropout": 0.0,
+  "cell_encoder": "rabel",
+  "rabel_centers": 16,
+  "col_id_dim": 32,
+  "col_id_scale": 0.1,
+  "max_classes": 64,
+  "block_order": "SNF",
+  "feature_mode": "isab",
+  "n_inducing": 128,
+  "n_row_summary": 4,
+  "n_feature_summary": 8,
+  "dense_group_size": 3,
+  "sample_qassmax": true,
+  "feature_qassmax": true,
+  "attnres": true,
+  "attnres_stride": 4,
+  "cls_head": "hybrid",
+  "decoder_heads": 6,
+  "decoder_head_dim": 64,
+  "reg_bins": 512,
+  "reg_range": 4.0,
+  "reg_tail_scale": 1.0,
+  "surv_bins": 64,
+  "loss_version": 2,
+  "preprocessing_version": 2,
+  "reg_target_transform": "context_zscore",
+  "reg_head": "quantile",
+  "reg_quantiles": 999,
+  "reg_loss": "pinball_crps",
+  "surv_head": "lognormal_mixture",
+  "surv_components": 8,
+  "survival_supervision": "latent",
+  "survival_support_policy": "explicit_task_metadata",
+  "checkpoint_segments": 0,
+  "checkpoint_min_cells": 0,
+  "checkpoint_attention_min_cells": 0,
+  "checkpoint_attention_max_cells": 0,
+  "chunk_cells": 1048576,
+  "checkpoint_chunks": true
+}

prts.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a189221c588e2404efe97e91e3cd5d0aa08b47de9633823fa17df20d02bfaa4c
+size 878530652