---
license: apache-2.0
library_name: gguf
base_model: HuggingFaceBio/Carbon-8B
language:
- dna
tags:
- dna
- genomic
- llama.cpp
- gguf
- hybriddna
---

# Carbon-8B GGUF

GGUF (bf16) conversion of [HuggingFaceBio/Carbon-8B](https://huggingface.co/HuggingFaceBio/Carbon-8B) for use with [llama.cpp](https://github.com/ggml-org/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](https://github.com/ggml-org/llama.cpp/pull/23410), so any build from `master` after that works:

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

## Files

| File | Quant | Size |
|---|---|---|
| `carbon-8b-bf16.gguf` | bf16 (lossless from source) | 16 GB |

## Usage

### Download

```bash
hf download HuggingFaceBio/Carbon-8B-GGUF carbon-8b-bf16.gguf --local-dir .
```

### Basic DNA completion

```bash
./build/bin/llama-completion -m carbon-8b-bf16.gguf \
    -p '<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG' \
    -n 64 --temp 0 -no-cnv
```

### Metadata-conditioned generation

```bash
./build/bin/llama-completion -m carbon-8b-bf16.gguf \
    -p '<vertebrate_mammalian><protein_coding_region><dna>ATGCGCTAG' \
    -n 64 --temp 0 -no-cnv
```

### Speculative decoding with Carbon-500M draft (~2x speedup)

The 500M shares the HybridDNA vocab, so it's a near-ideal draft. Measured ~2.1x speedup at temp=0 with 87% accept rate on DNA prompts. Grab the draft GGUF first:

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

Then run the standalone tool with `--model-draft`:

```bash
./build/bin/llama-speculative \
    -m  carbon-8b-bf16.gguf \
    -md carbon-500m-bf16.gguf \
    -p '<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG' \
    -n 256 --temp 0
```

Or serve the 8B with the 500M draft (`llama-server` accepts the same `-md` flag):

```bash
./build/bin/llama-server \
    -m  carbon-8b-bf16.gguf \
    -md carbon-500m-bf16.gguf \
    --draft-max 16 --draft-min 1 \
    --port 8080
```

```bash
curl -s http://localhost:8080/completion -d '{
  "prompt": "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG",
  "n_predict": 256,
  "temperature": 0
}' | jq -r .content
```

### Likelihood scoring

The source card's Python `score()` function computes mean log-prob per DNA token. In llama.cpp the closest tools are `llama-perplexity` for corpus-level perplexity (`perplexity = exp(-mean_logprob)`):

```bash
# one prompt per line in dna_corpus.txt, each wrapped in <dna>...</dna>
./build/bin/llama-perplexity -m carbon-8b-bf16.gguf -f dna_corpus.txt --ppl-stride 0
```

Or `llama-server` with `logprobs` for per-token log-probabilities:

```bash
./build/bin/llama-server -m carbon-8b-bf16.gguf --port 8080 &
curl -s http://localhost:8080/completion -d '{
  "prompt": "<dna>GGGCTATAAAGGCCATCGATCGATCGATCGATCGATCGATCG</dna>",
  "n_predict": 0,
  "n_probs": 1
}' | jq '.completion_probabilities'
```

### Long context with YaRN (65k tokens ≈ 393 kbp)

Mirrors the source card's `rope_scaling = {type: yarn, factor: 4.0, original_max_position_embeddings: 32768}`:

```bash
./build/bin/llama-completion -m carbon-8b-bf16.gguf \
    -c 65536 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 \
    -p '<dna>...' -n 64 --temp 0 -no-cnv
```

### Base-pair-level generation (FNS branch) — not supported

The `revision="fns"` example from the source card needs custom modeling code (factorized nucleotide supervision head), which only the Python transformers path can load. llama.cpp can't run that branch.

## See also

- Source weights: [HuggingFaceBio/Carbon-8B](https://huggingface.co/HuggingFaceBio/Carbon-8B)
- Other GGUF variants: [500M](https://huggingface.co/HuggingFaceBio/Carbon-500M-GGUF) · [3B](https://huggingface.co/HuggingFaceBio/Carbon-3B-GGUF) · [8B](https://huggingface.co/HuggingFaceBio/Carbon-8B-GGUF)

## License

Apache-2.0, inherited from the source model.