Hugging Face有发布 Carbon-X，是一种开放的DNA语言模型，有14.8B参数。 Carbon-X是来自Hugging Face Bio的一款仅能解码的模型。 它在聊天时读取原始DNA，并以英语回答。 它预测变异是良性还是致病性，旗帜调控区域，将生物分类并预测下个基点。 重量在Apache 2.0下方的飞船，因此实验室可以在自己的GPU上保持敏感的序列。 Hugging Face与曾国藩学院和TIGEM/那不勒斯费德里克第二大学一起建造了它。
TL; DR 数据
- 大小：14.8B参数(ense, no MoE),40,960个上下文。
- 运行在：1 x 80 GB GPU在bfloat16中通过vLLM或变形器。 官方GGUF:Q8 0为15.8GB,Q4 K M为9.6GB.
- 性能：5个DNA 基准测试的平均值为66.7%，是每个已测试(自报)的开放模型中最好的。
- 最佳：在生物Reason编码方面占74.9%，领先于每个测试、开放或关闭的模型。
- 最坏的：29.8%的基因组QA系列问题。
什么是"碳-X"，它是如何工作的？
碳-X从Qwen3-14B开始。 DNA和文本在1个解码器中共享了1个令牌流。 没有单独的DNA编码器，不同于比奥瑞森等早期的工作。
关键结构块是混合活化活化剂。 它为文本保留了Qwen3的BPE词汇，并保留了新增 5,515个DNA符号。 其中包括4,096个不相重叠的6摩斯，1,364个尾代和3个标记。 1个DNA代号覆盖了6个碱基对。 6个或更多的A/C/G/T基的DNA运行自动被标记。
骨干为标准Qwen3堆。 它有40个地层，隐藏大小为5120,GQA有8个KV头。
碳-X是如何训练的？
培训分四个阶段进行，按样卡：
- 嵌入取暖：用变压器冷冻了约10B个活物，所以只有新的DNA嵌入体才能学习。
- 中训：302B令牌，序列长度为8,192个。 混合体为55%的原始DNA,20%的英语，15%的生物学文本和10%的指令数据。
- SFT:3个纪元，超过20个数据集子集，由域专家组成的确定核查员检查答复。
- RL：使用TRL和vLLM的同步GRPO在2发子弹中可核查的奖励。
根据发布文章，学习率是最困难的。 高比率学习了DNA却忘了英语。 一个较低的率，一个14B模型和一个长跑固定了取舍。 RL直接被应用到Qwen3-14B上，从未击败过FrontierVEP上50%的机会基线。
Carbon-X在DNA 基准测试上表现如何？
所有得分均由Hugging Face自报。 Carbon-X带着思维障碍运行。 竞争者尽了最大的努力
碳-X坐落于Qwen3-14B上方27分，GPT-6.1 Sol下方0.8分。 它导致编码-变量效应和下基预测。 边疆模型仍然在复杂的变种和单基变种上取胜。
拒绝决定了这些数字。 在下Base中，Opus 5.5从低努力的17.7%下降到了最大努力的1.1%，因为拒绝率上升了。 Gemini 4 Argon在FrontierVEP上从58%下降至38%，因为努力量增加。
Carbon-X还保留了一般技能：79.3%的IFEval快速级的严格精确度。
碳-X运行的成本是多少？
Hugging Face价格碳-X按H200计量的吞吐量，每小时5美元。 在此基础上，它报告每个问题的推论费用约为1,800 GPT-6.1 Sol。 将240万ClinVar的变体进行计算，其意义不确定，将花费约300美元。 同样的工作在前沿模型上花费了32 000至400万美元。
Carbon-X也作为子agent工作。 Kimi K3的DNA指数在咨询Carbon-X时从49.4%上升到了60.5%.
如何部署碳-X?
- vLLM:vllm服务于HuggingFaceBio/Carbon-X -- Trust-remote-code on 1×80GB GPU.
- 变形器：v5或后期，带有信任-remote code=True用于DNA活化剂。
- llama.cpp：官方的Carbon-X-GGUF repo需要llama.cpp b11742或更新。 古老的造型将DNA错误化
- Agents：回转船OpenAI型工具，是一款被检查的agent和编码agents的碳干能。
保持启用 thinking=虚假。 该模型经过了后期培训，并以这种方式进行了评估。
Carbon-X如何与其他DNA和一般模型进行比较？
关键外卖
- Carbon-X是一个14.8B的开放模型，读取了DNA和英语的答案。
- 平均在5个DNA 基准测试上为66.7%，比GPT-6.1 Sol低0.8分。
- 在BioReason编码(74.9%)和NextBase(39.4%)上，它引导每个测试模型。
- 它运行在1×80GB GPU上，或15.8GB Q8 0 GGUF上。
- Splice-site精度为29.8%，未经临床验证。
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Hugging Face has released Carbon-X, an open DNA language model with 14.8B parameters. Carbon-X is a decoder-only model from Hugging Face Bio. It reads raw DNA in a chat and answers in English. It predicts whether mutations are benign or pathogenic, flags regulatory regions, classifies organisms and predicts next bases. The weights ship under Apache 2.0, so labs can keep sensitive sequences on their own GPUs. Hugging Face built it with Zhongguancun Academy and TIGEM / University of Naples Federico II.
TL;DR
- Size: 14.8B parameters (dense, no MoE), 40,960-token context.
- Runs on: 1 x 80 GB GPU in bfloat16 via vLLM or Transformers. Official GGUF: Q8_0 is 15.8 GB, Q4_K_M is 9.6 GB.
- Performance: 66.7% average on 5 DNA benchmarks, best of every open model tested (self-reported).
- Best: 74.9% on BioReason Coding, ahead of every model tested, open or closed.
- Worst: 29.8% on GenomeQA splice-site questions.
What is Carbon-X and how does it work?
Carbon-X starts from Qwen3-14B. DNA and text share 1 token stream in 1 decoder. There is no separate DNA encoder, unlike earlier work such as BioReason.
The key structure piece is a hybrid tokenizer. It keeps Qwen3’s BPE vocabulary for text and adds 5,515 DNA tokens. These include 4,096 non-overlapping 6-mers, 1,364 tail tokens and 3 markers. One DNA token covers 6 base pairs. DNA runs of 6 or more A/C/G/T bases are tagged automatically.
The backbone is a standard Qwen3 stack. It has 40 layers, hidden size 5120 and GQA with 8 KV heads.
How was Carbon-X trained?
Training ran in four stages, per the model card:
- Embedding warmup: about 10B tokens with the transformer frozen, so only the new DNA embeddings learn.
- Mid-training: 302B tokens at 8,192 sequence length. The mix is 55% raw DNA, 20% English, 15% biology text and 10% instruction data.
- SFT: 3 epochs over 20 dataset subsets, with responses checked by deterministic verifiers built with domain experts.
- RL: asynchronous GRPO with verifiable rewards over 2 rounds, using TRL and vLLM.
As per the release post, learning rate was the hardest knob. High rates learned DNA but forgot English. A lower rate, a 14B model and a long run fixed the trade-off. RL applied directly to Qwen3-14B never beat the 50% chance baseline on FrontierVEP.
How does Carbon-X perform on DNA benchmarks?
All scores are self-reported by Hugging Face. Carbon-X ran with thinking disabled. Competitors ran at their best reasoning effort.
Carbon-X sits 27 points above Qwen3-14B and 0.8 points below GPT-6.1 Sol. It leads on coding-variant effects and next-base prediction. Frontier models still win on complex variants and single-base variants.
Refusals shape these numbers. On NextBase, Opus 5.5 falls from 17.7% at low effort to 1.1% at max effort as refusals rise. Gemini 4 Argon drops from 58% to 38% on FrontierVEP as effort increases.
Carbon-X also keeps general skills: 79.3% prompt-level strict accuracy on IFEval.
What does Carbon-X cost to run?
Hugging Face prices Carbon-X at measured throughput on an H200 at $5 per hour. On that basis, it reports about 1/1,800 of GPT-6.1 Sol’s inference cost per problem. Scoring the 2.4 million ClinVar variants of uncertain significance would cost about $300. The same job costs $32,000 to $4 million on frontier models.
Carbon-X also works as a sub-agent. Kimi K3’s DNA index rose from 49.4% to 60.5% when it consulted Carbon-X.
How do you deploy Carbon-X?
- vLLM: vllm serve HuggingFaceBio/Carbon-X --trust-remote-code on 1 x 80 GB GPU.
- Transformers: v5 or later, with trust_remote_code=True for the DNA tokenizer.
- llama.cpp: the official Carbon-X-GGUF repo needs llama.cpp b11742 or newer. Older builds tokenize DNA wrongly.
- Agents: the repo ships an OpenAI-style tool, an Inspect agent and a carbon-dna skill for coding agents.
Keep enable_thinking=False. The model was post-trained and evaluated that way.
How does Carbon-X compare with other DNA and general models?
Key Takeaways
- Carbon-X is a 14.8B open model that reads DNA and answers in English.
- It averages 66.7% on 5 DNA benchmarks, 0.8 points behind GPT-6.1 Sol.
- It leads every tested model on BioReason Coding (74.9%) and NextBase (39.4%).
- It runs on 1 x 80 GB GPU, or a 15.8 GB Q8_0 GGUF.
- Splice-site accuracy is 29.8%, and it is not clinically validated.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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