Instructions to use vandijklab/pythia-160m-c2s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vandijklab/pythia-160m-c2s with Transformers:

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

pipe = pipeline("text-generation", model="vandijklab/pythia-160m-c2s")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vandijklab/pythia-160m-c2s")
model = AutoModelForCausalLM.from_pretrained("vandijklab/pythia-160m-c2s", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vandijklab/pythia-160m-c2s with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vandijklab/pythia-160m-c2s"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vandijklab/pythia-160m-c2s",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vandijklab/pythia-160m-c2s

SGLang

How to use vandijklab/pythia-160m-c2s 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/pythia-160m-c2s" \
    --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/pythia-160m-c2s",
		"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/pythia-160m-c2s" \
        --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/pythia-160m-c2s",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vandijklab/pythia-160m-c2s with Docker Model Runner:
```
docker model run hf.co/vandijklab/pythia-160m-c2s
```

SyedA5688 commited on Oct 31, 2025

Commit

9a85d18

verified ·

1 Parent(s): 2393d6b

Updated GitHub code base license description in README

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@@ -1,5 +1,5 @@
 ---
-license: cc0-1.0
 datasets:
 - vandijklab/immune-c2s
 language:
@@ -22,7 +22,7 @@ using 8 A100 40GB GPUs for approximately 20 hours on the following tasks:
 3. cell type prediction
 ## Cell2Sentence Links:
-GitHub: <https://github.com/vandijklab/cell2sentence-ft> (Note: Codebase has CC BY-NC-ND 4.0 license. Only weights shared on Hugging Face are CC0 1.0)
 Paper: <https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3>
 ## Pythia Links:

 ---
+license: cc-by-4.0
 datasets:
 - vandijklab/immune-c2s
 language:
 3. cell type prediction
 ## Cell2Sentence Links:
+GitHub: <https://github.com/vandijklab/cell2sentence-ft> (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: