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

Libraries

How to use HuggingFaceBio/Carbon-500M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="HuggingFaceBio/Carbon-500M-GGUF",
	filename="carbon-500m-bf16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use HuggingFaceBio/Carbon-500M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HuggingFaceBio/Carbon-500M-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf HuggingFaceBio/Carbon-500M-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf HuggingFaceBio/Carbon-500M-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf HuggingFaceBio/Carbon-500M-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:BF16

Use Docker

docker model run hf.co/HuggingFaceBio/Carbon-500M-GGUF:BF16

LM Studio
Jan
Ollama
How to use HuggingFaceBio/Carbon-500M-GGUF with Ollama:
```
ollama run hf.co/HuggingFaceBio/Carbon-500M-GGUF:BF16
```

Unsloth Studio new

How to use HuggingFaceBio/Carbon-500M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HuggingFaceBio/Carbon-500M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HuggingFaceBio/Carbon-500M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HuggingFaceBio/Carbon-500M-GGUF to start chatting

Docker Model Runner
How to use HuggingFaceBio/Carbon-500M-GGUF with Docker Model Runner:
```
docker model run hf.co/HuggingFaceBio/Carbon-500M-GGUF:BF16
```

Lemonade

How to use HuggingFaceBio/Carbon-500M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HuggingFaceBio/Carbon-500M-GGUF:BF16

Run and chat with the model

lemonade run user.Carbon-500M-GGUF-BF16

List all available models

lemonade list

Carbon-500M GGUF

GGUF (bf16) conversion of HuggingFaceBio/Carbon-500M for use with llama.cpp.

Carbon is a hybrid DNA / English language model that switches between Qwen3-4B-Base byte-level BPE for natural text and fixed 6-mer chunking for DNA inside <dna>...</dna> tags.

Requires a recent llama.cpp

HybridDNATokenizer support was merged in ggml-org/llama.cpp#23410, so any build from master after that works:

git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build -j

Files

File	Quant	Size
`carbon-500m-bf16.gguf`	bf16 (lossless from source)	983 MB

Usage

Download

hf download HuggingFaceBio/Carbon-500M-GGUF carbon-500m-bf16.gguf --local-dir .

Basic DNA completion

./build/bin/llama-completion -m carbon-500m-bf16.gguf \
    -p '<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG' \
    -n 64 --temp 0 -no-cnv

As a draft model for speculative decoding

Carbon-500M shares the HybridDNA vocab with the larger models, so it makes an excellent draft model:

# 8B target + 500M draft -> ~2x speedup at temp=0
./build/bin/llama-speculative \
    -m  carbon-8b-bf16.gguf \
    -md carbon-500m-bf16.gguf \
    -p '<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG' \
    -n 256 --temp 0

License

Apache-2.0, inherited from the source model.

Downloads last month: -

GGUF

Model size

0.5B params

Architecture

llama

Hardware compatibility

16-bit

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Collection including HuggingFaceBio/Carbon-500M-GGUF

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Collection

Carbon 500M, 3B, 8B genomic models and GGUF variants for llama.cpp • 6 items • Updated about 18 hours ago • 27

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Carbon-500M-GGUF