Instructions to use HuggingFaceBio/Carbon-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceBio/Carbon-X with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceBio/Carbon-X")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-X")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceBio/Carbon-X", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceBio/Carbon-X with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceBio/Carbon-X"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-X",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceBio/Carbon-X

SGLang

How to use HuggingFaceBio/Carbon-X 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 "HuggingFaceBio/Carbon-X" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-X",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "HuggingFaceBio/Carbon-X" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-X",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HuggingFaceBio/Carbon-X with Docker Model Runner:
```
docker model run hf.co/HuggingFaceBio/Carbon-X
```

A question on 6-mer tokens and indels, and a request from a 17-year-old who wants to help

by pop123ux - opened 8 days ago

Discussion

pop123ux

8 days ago

Hi Carbon team, congrats on the release!

The number that stopped me wasn’t a benchmark score. It was ~$300 to work through ClinVar’s 2.4 million variants of uncertain significance. Behind every one of those variants is a family waiting for an answer, and Carbon-X makes that answer something a normal hospital could afford.

First, a real question about the tokenizer. Carbon-X reads DNA as non-overlapping 6-mers. A substitution changes only one token, but an insertion or deletion shifts the frame, so every token after it changes. Did that show up as a weak spot on the Non-SNV / indel cases? Did the RLVR stage learn to work around it, or did you try anything at the tokenizer level (overlapping k-mers, frame-offset augmentation)?

A bit of context on why I’m asking: I’m a 17-year-old from Romania, aspiring to become an AI researcher, I won 🥇Gold in Life Sciences at ICYS 2026 with a bio-ML project, and I’d love to contribute to work like Carbon one day, because I love STEM AI work, and It's one of my deepest passions. If your team ever takes on research interns, I’d be very grateful to hear how to apply. I know I’m young. I’m asking anyway, because people who once took a chance on someone are the reason open science like Carbon exists.

Thanks for releasing Carbon in the open!

Alex · github.com/pop123-ux · getaiatlas.com · alexandrupp55@gmail.com

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