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 Settings

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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use HuggingFaceBio/Carbon-500M-GGUF with Ollama:
```
ollama run hf.co/HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M
```

Unsloth Studio

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:Q4_K_M
```

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:Q4_K_M

Run and chat with the model

lemonade run user.Carbon-500M-GGUF-Q4_K_M

List all available models

lemonade list

Carbon-500M GGUF

Running with llama.cpp

Carbon is a base DNA model (not instruction-tuned). Use the raw completion front-end so llama.cpp does not apply any chat templating:

llama-completion \
  -hf HuggingFaceBio/Carbon-500M-GGUF:Q8_0 \
  --prompt "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG" \
  -n 128 --temp 0 --no-display-prompt

If you use llama-cli instead, pass -no-cnv so it does not wrap the prompt in assistant-style markers (otherwise the model may emit annotation tags such as <protein_coding_region> instead of DNA).

Available files

Quant	File	Size
BF16	`carbon-500m-bf16.gguf`	982.2 MB
Q8_0	`carbon-500m-q8_0.gguf`	524.6 MB
Q6_K	`carbon-500m-q6_k.gguf`	443.2 MB
Q5_K_M	`carbon-500m-q5_k_m.gguf`	405.1 MB
Q4_K_M	`carbon-500m-q4_k_m.gguf`	369.2 MB

K-quants are produced with --token-embedding-type Q8_0 --output-tensor-type Q8_0 so the sensitive embedding/output tensors stay at Q8_0 precision (important for this model's tight DNA-vs-text logit margins).

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

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: 807

GGUF

Model size

0.5B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

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 • 7 items • Updated about 11 hours ago • 42

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Carbon-500M-GGUF