Instructions to use HuggingFaceBio/Carbon-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceBio/Carbon-500M with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceBio/Carbon-500M")

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

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-500M")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceBio/Carbon-500M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceBio/Carbon-500M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceBio/Carbon-500M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-500M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceBio/Carbon-500M

SGLang

How to use HuggingFaceBio/Carbon-500M 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-500M" \
    --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": "HuggingFaceBio/Carbon-500M",
		"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 "HuggingFaceBio/Carbon-500M" \
        --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": "HuggingFaceBio/Carbon-500M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceBio/Carbon-500M with Docker Model Runner:
```
docker model run hf.co/HuggingFaceBio/Carbon-500M
```

lewtun HF Staff Claude Opus 5.5 commited on 17 days ago

Commit

ada8a5e

verified ·

1 Parent(s): 9796b75

Point the tech report link at bioRxiv

Browse files

The Technical Report badge pointed at the PDF in the Carbon-3B repo, so this points it at the bioRxiv preprint like the other Carbon cards.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -13,7 +13,7 @@ tags:
 ![](figures/carbon-500m-banner.png)
 <p align="center">
-  <a href="https://huggingface.co/HuggingFaceBio/Carbon-3B/blob/main/tech-report.pdf"><b>Technical Report</b> 🧬</a>
 </p>
 # Carbon-500M

 ![](figures/carbon-500m-banner.png)
 <p align="center">
+  <a href="https://www.biorxiv.org/content/10.64898/2026.05.22.727119v1"><b>Technical Report</b> 🧬</a>
 </p>
 # Carbon-500M