Instructions to use vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation")
model = AutoModelForCausalLM.from_pretrained("vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation

SGLang

How to use vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation with Docker Model Runner:
```
docker model run hf.co/vandijklab/C2S-Pythia-410m-cell-type-conditioned-cell-generation
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Overview

This is the C2S-Pythia-410m-cell-type-conditioned-cell-generation model, built on the Pythia-410m architecture developed by EleutherAI, fine-tuned using Cell2Sentence (C2S) on a comprehensive collection of single-cell RNA sequencing (scRNA-seq) datasets from CellxGene and the Human Cell Atlas. Cell2Sentence is a pioneering technique 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 is specifically trained for cell type-conditioned single-cell generation, enabling the generation of realistic single-cell profiles conditioned on specified cell types.

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 the top 200 genes per cell sentence.

Tasks

This model is designed for:

Cell type-conditioned single-cell generation: Generating single-cell profiles conditioned on specific cell types, allowing for the creation of synthetic cells that reflect the gene expression patterns of targeted cell types.