Update README.md

README.md

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 library_name: transformers
-tags: []
+license: mit
+base_model:
+- KuanP/PULSAR-pbmc
 ---
 
-# Model Card for Model ID
+# Model Card for PULSAR-pbmc 
 
 <!-- Provide a quick summary of what the model is/does. -->
 
+**PULSAR** (Patient Understanding Leveraging Single-cell universAl Representation) is a multi-scale, multi-cellular foundation model for human peripheral blood mononuclear cells (PBMCs). It transforms a set of single-cell transcriptomes into an interpretable **donor-level embedding** that preserves single-cell resolution while capturing multicellular composition and coordination.
+
+This repo hosts the **aligned PBMC model** (`PULSAR-aligned`) used to produce donor embeddings aligned for disease classification. A base-model is also available (see **Model Sources**).
 
 
 ## Model Details
@@ -14,24 +19,20 @@ tags: []
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+PULSAR (Patient Understanding Leveraging Single-cell universAl Representation) is a hierarchical, multi-scale foundation model for PBMC scRNA-seq that converts unordered sets of single cells into a 512-d donor embedding while preserving single-cell resolution. It integrates molecular priors from ESM2 protein embeddings, cellular representations via Universal Cell Embeddings (UCE, 1,280-d), and a Multicellular Transformer encoder–decoder trained with a high-masking, Masked Cell Modeling objective. Pretraining proceeds in two stages: a pan-tissue CELLxGENE corpus (≈36.2M cells; 6,807 samples) followed by continual pretraining on blood (≈8.74M cells; 2,588 samples). The resulting donor embeddings support zero-shot and lightweight-head downstream tasks, including large-scale reference mapping for disease classification (state-of-the-art accuracy with strong external generalization), regression of plasma proteomics from transcriptomes, forecasting of future outcomes (e.g., RA conversion in ACPA+ individuals and influenza vaccine responsiveness), and individualized cytokine perturbation modeling across donor, cellular, and gene levels. A  “virtual instrument” conditions on cytokine protein embeddings to transform baseline donor states and, with the decoder and an optional UCE→expression head, generates perturbed cell distributions and gene programs. Attention over cells provides mechanistic interpretability, highlighting disease- and severity-relevant subsets and enriching for antigen-specific clonotypes in viral infection. PULSAR thus operationalizes the AI Virtual Cell vision by linking molecular, cellular, and multicellular organization into a unified, transferable representation for precision immunology.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Kuan Pang (Stanford University, kuanpang@stanford.edu)
+- **Model type:** Transformer
+- **License:** MIT
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:**: https://github.com/snap-stanford/PULSAR
+- **Paper:** [TBD]
+- **Demo:** [TBD]
+- **Aligned version:** https://huggingface.co/KuanP/PULSAR-pbmc
 
 ## Uses
 
@@ -39,134 +40,32 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+- Generate 512-d **donor embeddings** from PBMC scRNA-seq to:
+  - Perform **reference mapping/retrieval** (kNN) for disease phenotypes
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+The model might not work for tissue types other than PBMC, which also includes cell sorting samples.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
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-#### Metrics
+Stage-1 pretraining corpus: CZ CELLxGENE Census (LTS 2023-07-25), 36.2M cells, 6,807 samples across 53 tissues and 69 conditions.
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Stage-2 continual pretraining (blood focus): 8.736M cells, 2,588 blood/PBMC samples (balanced sexes; broad ages).
 
-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
 
-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
-
-[More Information Needed]
+More details can be found in the Paper and GitHub.
 
 ## Citation [optional]
 
@@ -178,22 +77,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **APA:**
 
-[More Information Needed]
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-## Model Card Contact
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 [More Information Needed]