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Model: vandijklab/C2S-Pythia-410m-diverse-single-and-multi-cell-tasks
Source: Original Platform

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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+base_model: EleutherAI/pythia-410m
+library_name: transformers
+tags:
+- biology
+- scRNAseq
+---
+
+
+# Overview
+This is the C2S-Pythia-410m-diverse-single-and-multi-cell-tasks model, based on the Pythia-410m architecture 
+developed by EleutherAI, fine-tuned using Cell2Sentence (C2S) on a wide array of single-cell RNA sequencing 
+(scRNA-seq) datasets from CellxGene and the Human Cell Atlas. Cell2Sentence is a cutting-edge method that 
+adapts large language models (LLMs) to single-cell biology by converting scRNA-seq data into 
+"cell sentences" — ordered sequences of gene names based on expression levels. This model has been trained 
+to perform a broad range of single- and multi-cell tasks, making it a versatile tool for various single-cell 
+and multi-cell analyses.
+
+# Training Data
+This model was trained on over 57 million human and mouse cells gathered from over 800 single-cell RNA sequencing 
+datasets from CellxGene and the Human Cell Atlas. This dataset covers a broad range of cell types and conditions
+from multiple tissues in both human and mouse.
+
+This model was trained with a variable number of genes per cell sentence, with a maximum context length of 8192 tokens.
+The context length of the default Pythia model was extended using rotary positional embeddings prior to C2S training.
+- Cells: For multi cell samples, each training sample contained between 5 and 20 cells, with the same number of genes for each of the cells in the same sample.
+- Genes: For single cell samples, each cell sentence contained between 100 and 2048 genes. For multi cell samples, each cell sentence per cell contained between 100 and 400 genes.
+
+# Tasks
+This model is designed for the following tasks:
+
+Single-Cell Tasks
+- Unconditional single-cell generation: Generate single cell sentences unconditionally.
+- Cell type prediction: Predict the cell type of a given single cell.
+- Cell type-conditioned generation: Generate a single cell sentence conditioned on a specific cell type.
+
+Multi-Cell Tasks
+- Unconditional multi-cell generation: Generate multiple cell sentences unconditionally.
+- Tissue prediction: Predict the tissue of origin for a group of cells.
+- Cell type prediction: Predict the cell type for each cell in a group of multiple cells.
+- Tissue-conditioned multi-cell generation: Generate multiple cell sentences conditioned on a specific tissue.
+- Cell type-conditioned multi-cell generation: Generate multiple cell sentences conditioned on the cell type of each individual cell.
+- Multi-cells to abstract: Generate a research paper abstract based on the provided multi-cell sentences.
+- Abstract to multi-cells: Generate multiple cell sentences based on a given research paper abstract.
+
+Gene Set Tasks
+- Gene set name to genes: Generate an alphabetical list of genes given a gene set name.
+- Genes to gene set name: Generate the name of a gene set given an alphabetical list of genes.
+
+# Cell2Sentence Links
+- GitHub: https://github.com/vandijklab/cell2sentence (Note: Codebase has Apache 2.0 license, weights shared on HuggingFace are CC-by-4.0)
+- Paper: https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3
+
+# Pythia Links
+- Paper: https://arxiv.org/pdf/2304.01373
+- Hugging Face: https://huggingface.co/EleutherAI/pythia-410m

config.json

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+  "architectures": [
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+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8192,
+  "model_type": "gpt_neox",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "rope_scaling": null,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 0.25,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.0",
+  "use_cache": false,
+  "use_parallel_residual": true,
+  "vocab_size": 50304
+}

configuration.json

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