CellHermes v1.0
This is the CellHermes model, based on the LLaMA-3.1-8B-instruct architecture developed by Meta, fine-tuned using single-cell RNA sequencing (scRNA-seq) datasets from CellxGene and PPI network from BioGRID....
Model source
- Kind
- Text model
- File size
- Not recorded yet
- Base model
- meta-llama/Llama-3.1-8B-Instruct
- Version
- vRevision 8e2b0fa
- Updated
- Oct 4, 2026
- Publisher
- @EthanGao123
- Downloads
- 81
- Parameters
- 8B
- Tasks
- Text generation
- Source
- Hugging Face
Source description
Overview
This is the CellHermes model, based on the LLaMA-3.1-8B-instruct architecture developed by Meta, fine-tuned using single-cell RNA sequencing (scRNA-seq) datasets from CellxGene and PPI network from BioGRID. CellHermes is an innovative framework for adapting existing large language models (LLMs) to omics data by transforming various omics data into natural language. This transformation enables LLMs to leverage their powerful understanding and reasoning capabilities for various omics tasks, including perturbation prediction, cell fitness prediction, gene-disease association classification, etc.
Training Data
This model was trained on over 0.2 million human transcriptome instruction data and 0.2 million PPI network datasets from CellxGene and BioGRID. This dataset covers a broad range of cell types and conditions from multiple tissues in both human.
Tasks
This model is designed for:
- Omics data understanding.
CellHermes Links
- GitHub: https://github.com/theislab/CellHermes
- Paper:
LLaMa-3.1-8B-Instruct Links
- Paper: https://arxiv.org/abs/2407.21783
- Hugging Face: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
Sources
1 source
Sources
1 sourceVerified Oct 4
Source excerpts
3 excerpts
Source excerpts
3 excerpts--- license: llama3.1 language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: feature-extraction library_name: transformers tags: - biology - omics - bio-reasoning - scRNA-seq - PPI --- # Overview This is the CellHermes model, based on the LLaMA-3.1-8B-instruct architecture developed by Meta, fine-tuned using single-cell RNA sequencing (scRNA-seq) datasets from CellxGene and PPI network from BioGRID. CellHermes is an innovative framework for adapting existing large language models (LLMs) to omics data by transforming various omics data into natural language. This transformation enables LLMs to leverage their powerful understanding and reasoning capabilities for various omics tasks, including perturbation prediction, cell fitness prediction, gene-disease association classification, etc. # Training Data This model was trained on over 0.2 million human transcriptome instruction data and 0.2 million PPI network datasets from CellxGene and BioGRID. This dataset covers a broad range of cell types and conditions from multiple tissues in both human. # Tasks This model is designed for: - Omics data understanding. # CellHermes Links - GitHub: https://github.com/theislab/CellHermes - Paper: # LLaMa-3.1-8B-Instruct Links - Paper: https://arxiv.org/abs/2407.21783 - Hugging Face: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
Source context: 81 downloads · 4 likes · Pipeline feature-extraction · Library transformers · Repo EthanGao123/CellHermes-v1.0
Source context: 56 downloads · 2 likes · Pipeline feature-extraction · Library transformers · Repo EthanGao123/CellHermes-v1.0
