Carbon-X is a 14.8B-parameter decoder-only language model trained jointly on DNA sequences and natural-language text. You give it raw DNA inside an ordinary chat conversation and it answers in English: it predicts whether mutations are benign or pathogenic, identifies regions that control gene activity, classifies sequences by organism group, predicts the next bases of a sequence, and explains its reasoning. Responses can contain both DNA and natural language. DNA and text share a single token stream in a single decoder. There is no separate DNA encoder. A hybrid tokenizer keeps a BPE vocabulary for text and encodes DNA as non-overlapping 6-mers, so one DNA token covers 6 base pairs. Across five DNA benchmarks, which cover variant-effect prediction, sequence understanding and next-base prediction, Carbon-X averages 66.7% with thinking disabled. That is 27 points above Qwen3-14B, the model it starts from, and ahead of every other open model we evaluated. It is close to GPT-6.1 Sol (67.5%) at about 1/1,800 of the inference cost per problem, and it leads every model we tested on coding-variant effects and next-base prediction (Evaluation). Carbon-X answers directly, without extended reasoning, so each problem costs few output tokens. We estimate that analysing the 2.4 million variants of uncertain significance in ClinVar would cost about $300 with Carbon-X, compared with $32,000 to $4 million with the frontier models we evaluated. The weights fit on a single 80 GB GPU in bfloat16, so researchers can keep sensitive or unpublished sequences on their own infrastructure. Key features 14.8B parameters: 40 layers, hidden size 5120, GQA (40 query heads, 8 KV heads), SwiGLU, RMSNorm with QK-norm. Hybrid tokenizer: the Qwen3 BPE vocabulary for text plus 5,515 DNA tokens. These are 4,096 6-mers, 1,364 tail tokens for sequences whose length is not a multiple of 6, and the , and markers. DNA in a prompt is detected and tagged automatically. Chat model: post-trained and evaluated with thinking disabled. Context window: 40,960 tokens. Training: 302B tokens of mid-training on DNA, biology text, English and instruction data, then supervised fine-tuning and reinforcement learning with verifiable rewards on DNA tasks. Efficient inference: serves with vLLM on a single GPU. https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF   submitted by   /u/jacek2023 [link]   [comments]