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HuggingFaceBio
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Carbon-8B-GGUF

GGUF
Upper Grand Valley Dani
genomic
llama.cpp
hybriddna
Model card Files
xet
 Community
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How to use from
llama.cpp
Install (macOS, Linux) 
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HuggingFaceBio/Carbon-8B-GGUF:
# Run inference directly in the terminal:
llama cli -hf HuggingFaceBio/Carbon-8B-GGUF:
Install from WinGet (Windows) 
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HuggingFaceBio/Carbon-8B-GGUF:
# Run inference directly in the terminal:
llama cli -hf HuggingFaceBio/Carbon-8B-GGUF:
Build from source code 
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf HuggingFaceBio/Carbon-8B-GGUF:
# Run inference directly in the terminal:
./build/bin/llama cli -hf HuggingFaceBio/Carbon-8B-GGUF:
Use Docker 
docker model run hf.co/HuggingFaceBio/Carbon-8B-GGUF:
Quick Links

Carbon-8B GGUF

Available files

Quant File Size
BF16 carbon-8b-bf16.gguf 15.4 GB
Q8_0 carbon-8b-q8_0.gguf 8.2 GB
Q6_K carbon-8b-q6_k.gguf 6.6 GB
Q5_K_M carbon-8b-q5_k_m.gguf 5.9 GB
Q4_K_M carbon-8b-q4_k_m.gguf 5.2 GB

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).

How to use

Click Use this model > llama.cpp on this page for a ready-made snippet. To install llama.cpp, follow llama.app or download a prebuilt binary from the releases page.

Carbon-8B is a base DNA model, not a chat model, so use text completion rather than the chat UI. Start the prompt with <dna>:

llama completion -hf HuggingFaceBio/Carbon-8B-GGUF:Q8_0 -p '<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG' -n 128 --temp 0

llama serve -hf HuggingFaceBio/Carbon-8B-GGUF:Q8_0 serves the same model over HTTP; send raw prompts to its /completion endpoint.

  • Metadata conditioning: prefix the prompt with tags, e.g. <vertebrate_mammalian><protein_coding_region><dna>ATGCGCTAG.
  • Speculative decoding: add -hfd HuggingFaceBio/Carbon-500M-GGUF:Q8_0 to use Carbon-500M, which shares the vocabulary, as a draft model (about 2x faster at temperature 0).
  • Long context: for up to 65k tokens (about 393 kbp), add -c 65536 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768.
  • Likelihoods: llama perplexity -hf HuggingFaceBio/Carbon-8B-GGUF:Q8_0 -f dna.txt gives corpus perplexity. For per-sequence log-likelihoods, use the Python score() from the source card.

See also

License

Apache-2.0, inherited from the source model.

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