Carbon-X: Hugging Face releases open 14.8B-parameter DNA language model
Hugging Face has released Carbon-X, an open model that analyzes DNA sequences in plain language and classifies mutations as benign or disease-associated. It averages 66.7% across five DNA benchmarks, close to GPT-6.1 Sol, at a fraction of the cost. It runs locally, so sensitive genetic data never leaves your machine.
What it's about
With Carbon-X, Hugging Face has released an open language model that understands DNA and natural language together. You paste raw DNA sequences into a regular chat and get answers in English: for example, whether a mutation is benign or disease-associated, which regions regulate gene activity, or which group of organisms a sequence belongs to. It was developed with experts from the Zhongguancun Academy, the Telethon Institute of Genetics and Medicine and the University of Naples Federico II. Weights are available on Hugging Face (HuggingFaceBio/Carbon-X), along with a GGUF version for llama.cpp.
Architecture and training
Carbon-X is a decoder-only language model with 14.8 billion parameters (the adjustable values learned during training): 40 layers, hidden size 5120, grouped-query attention with 40 query heads and 8 KV heads. The context window is 40,960 tokens. It starts from Qwen3-14B.
The distinctive feature is a hybrid tokenizer. Text uses the Qwen3 BPE vocabulary, while DNA is encoded in non-overlapping 6-mers, so one DNA token covers 6 base pairs. On top of that come 5,515 DNA tokens: 4,096 6-mers, 1,364 end pieces for lengths not divisible by 6, plus markers. DNA in prompts is detected automatically. There is no separate DNA encoder; text and DNA flow through a single stream.
Training comprised roughly 302B tokens of mid-training, more than half of it DNA (mammals, other vertebrates, invertebrates, plants, fungi, protists, prokaryotes), plus English, biology texts and dialogue examples. Supervised fine-tuning and reinforcement learning with verifiable rewards followed. According to Lewis Tunstall, viral sequences were deliberately left out to reduce misuse risks.
Benchmarks
All tests come from the team itself. Two of the five sets (BioReason, GenomeQA) are from the literature; Hugging Face built FrontierVEP and NextBase. Carbon-X was run without reasoning mode.
| Benchmark | Carbon-X | GPT-6.1 Sol | Opus 5.5 | Gemini 4 Argon |
|---|---|---|---|---|
| BioReason Coding | 74.9 | 71.0 | 70.3 | 6.5 |
| BioReason Non-SNV | 85.1 | 91.7 | 94.8 | 90.3 |
| GenomeQA | 63.6 | 67.2 | 62.2 | 63.1 |
| NextBase | 39.4 | 32.9 | 1.1 | 31.2 |
| FrontierVEP | 70.4 | 74.9 | 76.2 | 58.0 |
| Average | 66.7 | 67.5 | 60.9 | 49.8 |
Among open models (GLM-5.3: 39.8; Kimi K3: 50.4; DeepSeek V4.1 Flash: 47.1), Carbon-X leads, and it sits 27 points above its base model. It is weak on splice sites (29.8%) and histone modifications (34.6%).
Cost and local use
Hugging Face estimates that a problem costs about 1,000 times less with Carbon-X than with GPT-6.1 Sol (based on measured throughput per H200 hour at $5/hour). Analyzing the 2.4 million ClinVar variants of uncertain significance would cost about $300, versus $32,000 to $4 million with frontier models. Privacy is a key argument: when you run it locally, sequences stay on your own hardware. According to the Reddit post, large models also sometimes refuse routine genomics requests.
What you need to try it
- Uncompressed (bfloat16), the model needs a GPU with about 80 GB of memory.
- With the GGUF version and 4-bit quantization, roughly 10–12 GB of VRAM (video memory) should be enough. That is an estimate, not a vendor figure.
- The GGUF file runs with llama.cpp, in principle also on CPU or Apple Silicon, but slowly.
- Transformers version 5 or newer is required, along with
trust_remote_code=True. For serving, there is vLLM on a single GPU. - Plan for a few dozen GB of disk space (bfloat16 weights are about 30 GB).
Limitations
The model was trained on fragments of up to about 49,000 base pairs; longer inputs are accepted but untested. It can miscount nucleotides and get coordinates wrong, and without context it may invent the origin or function of a sequence. It is not clinically validated. It can continue DNA but isn't meant for generating new sequences, and quality drops on long fragments. The Model Card recommends leaving thinking mode off (temperature 0.6, top-p 0.95, top-k 20).
Our take
Carbon-X is a practical step for open biology AI: a comparatively small model that keeps pace with frontier models on some tasks and runs locally. It follows Carbon-A (1.2B parameters, gene search, 566 million putative genes across 22,617 species). Competitors include AlphaGenome and Evo 2 (Arc Institute with Nvidia), which can also generate sequences. A caveat: the benchmarks mostly come from the developer, and two of them were built in-house. The sources also offer no direct comparison with specialized non-language models. It is explicitly not suited for clinical decisions.
Sources
- r/LocalLLaMA reddit.com
- Wired – AI wired.com
- Google News (IT) news.google.com
- Google News (FR) news.google.com
- Google News (ES) news.google.com
- Google News (DE) news.google.com
- Google News (IN) news.google.com
- Hugging Face Trending huggingface.co
- Google News (IN) news.google.com
- Google News (US) news.google.com
- Bing News (de-DE) t3n.de
- Google News (TW) news.google.com
- Bing News (en-US) en.shiftdelete.net
Try it yourself: Carbon-X
Explained step by step, no prior knowledge needed – on your own computer or free in your browser.
What can I do with it?
You can paste DNA sequences straight into a chat and ask questions in English: Is this mutation benign or pathogenic? Which region regulates genes? Which group of organisms does the sequence belong to? The model explains its assessment and runs entirely locally.
Who is it worth it for?
Interesting for researchers, bioinformaticians and the curious who don't want to send sensitive sequences to cloud services. Not suitable for medical decisions.
What your computer needs
Which variant is right for me?
Many models come in several sizes or "quantizations." These are compressed versions: smaller and faster, but slightly less accurate. Rule of thumb: pick the largest variant whose memory requirement fits in your VRAM (video memory). The scripts below choose this for you automatically.
| Variant | Download | Needs approx. | Compatible graphics cards |
|---|---|---|---|
Q4_K_M Default |
9.6 GB | 12 GB | e.g. RTX 3060 12 GB, RTX 4070, RTX 5070, RX 7700 XT |
Q5_K_M |
11 GB | 14 GB | e.g. RTX 4060 Ti 16 GB, RTX 4080, RTX 5080, RX 7800 XT |
Q6_K |
13 GB | 15 GB | e.g. RTX 4060 Ti 16 GB, RTX 4080, RTX 5080, RX 7800 XT |
Q8_0 |
16 GB | 18 GB | e.g. RTX 3090, RTX 4090, Tesla P40 |
BF16 |
30 GB | 34 GB | e.g. 2× RTX 3090/4090 or RTX A6000 |
With an 80 GB GPU, use the bfloat16 weights (HuggingFaceBio/Carbon-X). For ordinary computers, use the GGUF version (HuggingFaceBio/Carbon-X-GGUF) with 4-bit quantization, roughly 10–12 GB of VRAM (estimate).
- Uncompressed: a GPU with about 80 GB of memory.
- GGUF runs with llama.cpp, also on CPU or Apple Silicon, but more slowly.
- Context window of 40,960 tokens; long contexts need extra memory.
- vLLM serving is possible on a single GPU.
- Plan for several tens of GB of free disk space.
How do I find out what my computer has?
- Windows: Press Ctrl + Shift + Esc → Task Manager → Performance tab → click GPU on the left. At the bottom you'll see Dedicated GPU memory (that's your VRAM). You'll find system memory under Memory.
- Mac: Apple menu at the top left → About This Mac. You'll see your chip (e.g. M2) and memory (e.g. 16 GB). On Apple chips, graphics and processor share this memory.
- Linux: Type
nvidia-smiin the terminal (shows your graphics card and its memory) andfree -hfor system memory.
Guide
Google Colab (in your browser, no installation)
Google Colab is a free service from Google. It gives you a computer with a graphics card in your browser for a few hours. All you need is a Google account. For this model: runs for free – The free Tesla T4 GPU with 15 GB is enough.
- Download the notebook. Click the button and save the file.
- Open Colab. Go to colab.research.google.com and sign in with your Google account.
- Upload the notebook. In the window that appears, click Upload on the left (or choose File → Upload notebook at the top) and select the file
colab.ipynb. - Turn on the GPU. In the top menu, go to Runtime → Change runtime type → under Hardware accelerator, select T4 GPU → Save.
- Start everything. Runtime → Run all (keyboard shortcut Ctrl + F9). If you see the warning “This notebook was not authored by Google,” click Run anyway.
- Wait. The cells run one after another. A green checkmark appears to the left of each cell when it's done. Installation and download take about 7 minutes here.
- Try it out. The result appears below the last cell. To test something else, change the text in the cell and run it again with Shift + Enter. The last cell starts a proper chat with an input field.
- Clean up. When you're done: Runtime → Disconnect and delete runtime. This saves your free quota.
Good to know: The free GPU is limited (usually a few hours per day). After about 90 minutes of inactivity, Colab disconnects, and you'll have to start everything over.
Windows 10 / 11
The easiest way is Ollama, a free program that launches AI models with a single command.
- Update your graphics driver (NVIDIA cards only): Install the NVIDIA app or the latest driver. For AMD cards, use the latest Adrenalin driver.
- Install Ollama. Download the file OllamaSetup.exe from ollama.com/download/windows, double-click it, and choose Install. Afterward, a small llama icon appears in the taskbar at the bottom right.
- Open a terminal window. Press the Windows key, type PowerShell and press Enter. A blue or black window opens.
- Start the model. Copy this command ("Copy" button), paste it into the window with a right-click, and press Enter:
The first time, Ollama downloads the model (9.6 GB (about 13 min at 100 Mbit/s)). After that, it's instant.ollama run hf.co/HuggingFaceBio/Carbon-X-GGUF:Q4_K_M - Start typing. Once
>>> Send a messageappears, type your question and press Enter. Type/byeto end the chat.
Not enough memory? Pick a smaller variant from the table above, e.g. ollama run hf.co/HuggingFaceBio/Carbon-X-GGUF:Q4_K_M.
Prefer automatic? A script that picks the right variant for you
- Download both files into the same folder: try_windows.ps1 demo.py
- Open the folder in File Explorer, right-click an empty spot and choose Open in Terminal.
- Paste this command and press Enter:
powershell -ExecutionPolicy Bypass -File try_windows.ps1
If needed, the script installs Ollama via winget, measures your VRAM (video memory) and downloads the matching variant.
macOS
Macs with an Apple chip (M1 to M5) are ideal. Older Intel Macs are very slow.
- Install Ollama. Download it from ollama.com/download/mac, open the file, and drag Ollama into your Applications folder. Launch Ollama once – a llama appears in the menu bar at the top.
- Open Terminal. Press Cmd + Space, type Terminal, and press Enter.
- Start the model. Paste the command (Cmd + V) and press Enter:
The first time, the model is downloaded (9.6 GB (about 13 min at 100 Mbit/s)).ollama run hf.co/HuggingFaceBio/Carbon-X-GGUF:Q4_K_M - Start typing. Type your question and press Enter. Quit with
/bye.
Linux
- Check your GPU.
nvidia-smishould show your card. If it doesn't, install the driver on Ubuntu and restart:sudo ubuntu-drivers install - Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh - Start the model:
Exit withollama run hf.co/HuggingFaceBio/Carbon-X-GGUF:Q4_K_M/bye. Useollama psto check whether the model is running on the GPU. - Or let it pick the right variant automatically:
bash try_linux.sh # mit Python/transformers statt Ollama: MODE=hf bash try_linux.sh
Try it
Reference: ATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTACTGCC Variant: ATGGTGCATCTGACTCCTGTGGAGAAGTCTGCCGTTACTGCC – Is this variant more likely benign or pathogenic? Explain your reasoning.What does this sequence encode? ATGGTGCATCTGACTCCTGAGGAGAAGTCTGCCGTTWhich group of organisms does this sequence belong to, and which regulatory elements does it contain? (paste sequence)If something goes wrong
Transformers version 5 or newer is required (pip install -U transformers).
The DNA tokenizer is custom code: set `trust_remote_code=True`.
Keep thinking mode off (enable_thinking=False), since that is how the model was trained.
Sequences containing N or other characters become <oov> in 6-mers; filter to A/C/G/T first.
Provide context (gene, strand, coordinates), otherwise the model may invent the origin or function.
“out of memory,” a crash, or your computer freezes
The model is too big for your VRAM (video memory). Pick a smaller variant from the table above and close other programs, especially games and browsers with lots of tabs.
It runs, but extremely slowly
Your CPU is probably doing the work instead of your graphics card. In Ollama, the PROCESSOR column of ollama ps shows how much runs on the GPU. If it says "100% CPU", update your graphics driver or pick a smaller variant that fits entirely into VRAM (video memory).
“ollama” or “python” is not found
Close the terminal window and open it again so it recognizes the newly installed program. For Python on Windows: reinstall it and check Add python.exe to PATH.
Windows: "Running scripts is disabled on this system"
Run the script with exactly the command above: powershell -ExecutionPolicy Bypass -File try_windows.ps1. This allows execution for this one time only.
Error message when loading the model (version, "trust_remote_code", unknown architecture)
Brand-new models often need the latest software version. With Ollama: update Ollama. With Python: pip install -U transformers in the activated environment. The Model Card usually lists the exact version.
Tokenstrom generates the scripts and notebook automatically. Please take a quick look before running them – they're plain, readable text. Mind the model's license: apache-2.0.
Ask the AI assistant
Questions about this article, installation or background? Our assistant knows this page and runs on our own machine, not in someone else's cloud.
The Assistant is an AI (Gemma 4) and can make mistakes – please check important details against the source. Questions are used only to generate an answer and are deleted after one hour. Please don't enter any personal data.
Comments
What do you think? Already tried it? Write it here, no sign-up needed.
Comments are public. Links aren't allowed, and we delete abusive posts. You can delete your own comment at any time in this browser. Learn more in the Privacy Policy.