---
license: apache-2.0
datasets:
- tahoebio/Tahoe-100M
tags:
- biology
- single-cell
- RNA
- chemistry
- tahoebio
- pytorch
---
# Tahoe-x1

Tahoe-x1 is a family of perturbation-trained single-cell foundation models  developed by Tahoe Therapeutics.
For more details, see our blog post and [📄 preprint](http://www.tahoebio.ai/news/tahoe-x1).
In this repository, we provide pretrained weights for three model sizes:

- ~70M parameters (TahoeX1-70M)
- ~1B parameters (TahoeX1-1B)
- ~3B parameters (TahoeX1-3B)
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  <source media="(prefers-color-scheme: dark)" srcset="./assets/abstract_logo_dark_mode.png">
  <source media="(prefers-color-scheme: light)" srcset="./assets/abstract_logo_light_mode.png">
  <img src="./assets/abstract_logo_light_mode.png" alt="Abstract Logo">
</picture>

## Installation

To use the model, you must first install the `tahoe-x1` package from GitHub.

1. Clone the repository:
   ```bash
   git clone https://github.com/tahoebio/tahoe-x1
   cd tahoe-x1
   ```

2. Follow the installation steps for the docker or uv based insallation as described [here](https://github.com/tahoebio/tahoe-x1#installation) in the repository

Additional files (including vocabulary files and data for the included benchmarks) are hosted on S3 in a publicly accesible bucket (s3://tahoe-hackathon-data/MFM). These files will be automatically downloaded as needed.

## Quickstart
You can quickly load the model in this way:

```python
from tahoex.model import ComposerTX

model, vocab, model_config, collator_config = ComposerTX.from_hf(
    repo_id="tahoebio/Tahoe-x1",
    model_size="70m",  # or "1b", "3b".
    return_gene_embeddings=True,  # optional, default True
    use_chem_inf=False  # optional, default False
)
```





## License
We release the model weights and associated code under the Apache 2.0 license.