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")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

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
```

loubnabnl HF Staff commited on 9 days ago

Commit

805e7ba

verified ·

1 Parent(s): 401b752

Update README.md

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@@ -7,7 +7,6 @@ tags:
   - dna
   - genomic
   - transformers
-  - draft-model
   - speculative-decoding
 ---
@@ -19,6 +18,8 @@ A small generative DNA model from the **Carbon** family.
 For the full design rationale, tokenizer specification, evaluation protocol, and usage notes (DNA tag wrapping, 6-mer constraints, scoring helpers), please refer to the **[Carbon-3B model card](https://huggingface.co/hf-carbon/Carbon-3B)** — this card focuses only on facts specific to Carbon-500M.
 ## Facts
 - **500M-parameter decoder-only autoregressive DNA model** (Llama-style architecture).

   - dna
   - genomic
   - transformers
   - speculative-decoding
 ---
 For the full design rationale, tokenizer specification, evaluation protocol, and usage notes (DNA tag wrapping, 6-mer constraints, scoring helpers), please refer to the **[Carbon-3B model card](https://huggingface.co/hf-carbon/Carbon-3B)** — this card focuses only on facts specific to Carbon-500M.
+> TODO: update teh tokenizer code
 ## Facts
 - **500M-parameter decoder-only autoregressive DNA model** (Llama-style architecture).