Instructions to use HuggingFaceBio/Carbon-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceBio/Carbon-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceBio/Carbon-3B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-3B") model = AutoModelForCausalLM.from_pretrained("HuggingFaceBio/Carbon-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HuggingFaceBio/Carbon-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceBio/Carbon-3B" # 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-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceBio/Carbon-3B
- SGLang
How to use HuggingFaceBio/Carbon-3B 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-3B" \ --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-3B", "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-3B" \ --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-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceBio/Carbon-3B with Docker Model Runner:
docker model run hf.co/HuggingFaceBio/Carbon-3B
Fix links to the tech report and Carbon evals
Browse filesThe Carbon GitHub repo moved its code into a `carbon/` directory, so the `evaluation/` links 404 and `tech-report.pdf` is gone. This points the eval links at `carbon/evaluation/` and the tech report at bioRxiv. It also fixes the Carbon-8B and Carbon-500M links, which pointed at the org page.
π€ Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
README.md
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@@ -39,8 +39,8 @@ A generative DNA foundation model from the **Carbon** family.
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**Carbon-3B** is a 3B-parameter decoder-only autoregressive genomic foundation model trained on DNA and RNA sequences, with a primary focus on eukaryotes. It has a native context length of **32,768 6-mer tokens (β 197k DNA base pairs)** and extends to **65,536 tokens (β 393 kbp)** at inference time via YaRN. Carbon-3B is designed to be both strong and efficient: on generative tasks (sequence recovery), variant-effect prediction, and motif-perturbation discrimination, it matches the capability of substantially larger single-nucleotide baselines such as Evo2-7B while running several times faster.
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Carbon-3B is the **flagship** model of the Carbon family. We also release [**Carbon-8B**](https://huggingface.co/HuggingFaceBio/) for users who need additional capability at higher inference cost, and [**Carbon-500M**](https://huggingface.co/HuggingFaceBio/) β a small generative model intended for speculative decoding alongside Carbon-3B (or Carbon-8B).
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- Technical report: https://
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- Demo: https://huggingface.co/spaces/HuggingFaceBio/carbon-demo
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### Key features
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return logp.mean().item()
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```
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For batched scoring with attention masking and full reproducible evaluation pipelines (sequence recovery, ClinVar / BRCA2 / TraitGym VEP, triplet-expansion / synonymous codon replacement, Genomic-NIAH), use the official scripts in the [Carbon evaluation directory](https://github.com/huggingface/carbon/tree/main/evaluation) β see [`perturbation_tasks.py`](https://github.com/huggingface/carbon/blob/main/evaluation/perturbation_tasks.py) for the canonical `score_hf` implementation and [`README.md`](https://github.com/huggingface/carbon/blob/main/evaluation/README.md) for run instructions across all tasks.
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### Long context: extending to 65,536 tokens (β 393 kbp) with YaRN
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## Evaluation
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All evaluations are zero-shot and use the [public Carbon evaluation pipeline](https://github.com/huggingface/carbon/tree/main/evaluation). The suite covers seven tasks across four capability families:
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| Family | Task | Metric |
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**Carbon-3B** is a 3B-parameter decoder-only autoregressive genomic foundation model trained on DNA and RNA sequences, with a primary focus on eukaryotes. It has a native context length of **32,768 6-mer tokens (β 197k DNA base pairs)** and extends to **65,536 tokens (β 393 kbp)** at inference time via YaRN. Carbon-3B is designed to be both strong and efficient: on generative tasks (sequence recovery), variant-effect prediction, and motif-perturbation discrimination, it matches the capability of substantially larger single-nucleotide baselines such as Evo2-7B while running several times faster.
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Carbon-3B is the **flagship** model of the Carbon family. We also release [**Carbon-8B**](https://huggingface.co/HuggingFaceBio/Carbon-8B) for users who need additional capability at higher inference cost, and [**Carbon-500M**](https://huggingface.co/HuggingFaceBio/Carbon-500M) β a small generative model intended for speculative decoding alongside Carbon-3B (or Carbon-8B).
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- Technical report: https://www.biorxiv.org/content/10.64898/2026.05.22.727119v1
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- Demo: https://huggingface.co/spaces/HuggingFaceBio/carbon-demo
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### Key features
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return logp.mean().item()
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```
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For batched scoring with attention masking and full reproducible evaluation pipelines (sequence recovery, ClinVar / BRCA2 / TraitGym VEP, triplet-expansion / synonymous codon replacement, Genomic-NIAH), use the official scripts in the [Carbon evaluation directory](https://github.com/huggingface/carbon/tree/main/carbon/evaluation) β see [`perturbation_tasks.py`](https://github.com/huggingface/carbon/blob/main/carbon/evaluation/perturbation_tasks.py) for the canonical `score_hf` implementation and [`README.md`](https://github.com/huggingface/carbon/blob/main/carbon/evaluation/README.md) for run instructions across all tasks.
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### Long context: extending to 65,536 tokens (β 393 kbp) with YaRN
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## Evaluation
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All evaluations are zero-shot and use the [public Carbon evaluation pipeline](https://github.com/huggingface/carbon/tree/main/carbon/evaluation). The suite covers seven tasks across four capability families:
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| Family | Task | Metric |
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