Instructions to use pankajpandey-dev/Carbon-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pankajpandey-dev/Carbon-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pankajpandey-dev/Carbon-3B-GGUF",
	filename="carbon-3b-Q2_K.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 pankajpandey-dev/Carbon-3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pankajpandey-dev/Carbon-3B-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 pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pankajpandey-dev/Carbon-3B-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 pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pankajpandey-dev/Carbon-3B-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 pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use pankajpandey-dev/Carbon-3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pankajpandey-dev/Carbon-3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pankajpandey-dev/Carbon-3B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M

Ollama
How to use pankajpandey-dev/Carbon-3B-GGUF with Ollama:
```
ollama run hf.co/pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use pankajpandey-dev/Carbon-3B-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 pankajpandey-dev/Carbon-3B-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 pankajpandey-dev/Carbon-3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pankajpandey-dev/Carbon-3B-GGUF to start chatting

Docker Model Runner
How to use pankajpandey-dev/Carbon-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M
```

Lemonade

How to use pankajpandey-dev/Carbon-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pankajpandey-dev/Carbon-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Carbon-3B-GGUF-Q4_K_M

List all available models

lemonade list

Carbon-3B-GGUF

GGUF quantizations of HuggingFaceBio/Carbon-3B — a generative DNA foundation model — for efficient inference with llama.cpp.

Carbon-3B is a 3B-parameter autoregressive genomic model that uses a hybrid tokenizer (6-mer chunks for DNA inside <dna> tags, BPE for English text). It matches the performance of Evo2-7B while running over 250× faster at inference.

📦 Available Quantizations

File	Quant Method	Size	Use Case
carbon-3b-Q2_K.gguf	Q2_K	1.4 GB	Smallest — for very constrained edge devices
carbon-3b-Q3_K_M.gguf	Q3_K_M	1.8 GB	Small with reasonable quality
carbon-3b-Q4_K_M.gguf	Q4_K_M	2.1 GB	⭐ Recommended — best size/quality balance
carbon-3b-Q5_K_M.gguf	Q5_K_M	2.4 GB	Higher quality
carbon-3b-Q6_K.gguf	Q6_K	2.7 GB	Very close to f16 quality
carbon-3b-Q8_0.gguf	Q8_0	3.5 GB	Highest quality, near-lossless

⚙️ Requirements

You must use a recent build of llama.cpp that includes PR #23410 — this added support for Carbon's custom HybridDNATokenizer. Any build from master after that PR works.

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

🚀 Usage

Download a quant

huggingface-cli download pankajpandey-dev/Carbon-3B-GGUF carbon-3b-Q4_K_M.gguf --local-dir .

DNA sequence continuation

Wrap DNA sequences in <dna> tags so the tokenizer switches into 6-mer mode:

./build/bin/llama-completion -m carbon-3b-Q4_K_M.gguf \
    -p '<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG' \
    -n 64 --temp 0 -no-cnv

Conditional generation with metadata tags

./build/bin/llama-completion -m carbon-3b-Q4_K_M.gguf \
    -p '<vertebrate_mammalian><protein_coding_region><dna>ATGCGCTAG' \
    -n 64 --temp 0 -no-cnv

Long-context generation (up to 131k 6-mer tokens ≈ 786 kbp)

./build/bin/llama-completion -m carbon-3b-Q4_K_M.gguf \
    -c 65536 --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 \
    -p '<dna>...' -n 64 --temp 0 -no-cnv

⚠️ Important Notes

DNA tag is mandatory. Raw DNA without the <dna> tag will be BPE-tokenized as English text, producing meaningless results.
6-mer constraint. DNA sequences must contain only [ATCG] characters. Any other letters trigger an <oov> token.
fns revision is not supported. This GGUF is converted from the main revision only. The fns branch uses a factorized nucleotide supervision head that requires the Python transformers path.
Carbon-500M (GGUF here) can be used as a draft model for speculative decoding to speed up Carbon-3B inference at no quality loss.

📊 Quantization Method

These quants were produced from the original bf16 HuggingFaceBio/Carbon-3B safetensors using convert_hf_to_gguf.py followed by llama-quantize from llama.cpp (build b9330). Standard K-quants and Q8_0 were used — no imatrix calibration was applied, as standard text calibration corpora are not appropriate for DNA-pretrained models.

📜 License

Apache 2.0 — inherited from the source model.

🙏 Credits

Original model: HuggingFaceBio/Carbon-3B — full credit to the Hugging Face biology team for designing, training, and open-sourcing Carbon.
GGUF conversion & quantization: @pankajpandey-dev
Reference: Original GGUF release HuggingFaceBio/Carbon-3B-GGUF (bf16 only). This repository complements it with smaller K-quants for CPU/edge inference.

🔗 Related

Part of my GGUF Quantizations collection — making open models accessible across hardware tiers.

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for pankajpandey-dev/Carbon-3B-GGUF

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Collection including pankajpandey-dev/Carbon-3B-GGUF

GGUF Quantizations

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