Hugging Face 开源 DNA 模型 Carbon-X，基于 Qwen3-14B 中训练
Frontier labs are advancing the capabilities in AI for biology but preventing the good guys (scientists, academia, non-profits, health agencies,...) to use, stu
Hugging Face 联合创始人 Clément Delangue 宣布开源 DNA 模型 Carbon-X，通过在 300B tokens 的 DNA、英文与交错数据上对 Qwen3-14B 做中训练得到，并采用 BPE 处理英文、6-mer tokenizer 处理 DNA 的混合分词器。该模型在五个 DNA 理解与生成基准上超过其评估的所有开源语言模型，比 Qwen3-14B 提升 27 分，API 成本比 GPT 6.1 Sol 低 1,800 倍。模型以 Apache 2.0 协议发布在 Hugging Face。
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Hugging Face 联合创始人亲自说明 Carbon-X 的训练数据与评测结果，读者可据此了解开源 DNA 模型与闭源前沿模型的差距和成本。
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Frontier labs are advancing the capabilities in AI for biology but preventing the good guys (scientists, academia, non-profits, health agencies,...) to use, study and understand it with widespread but shallow guardrails that the bad guys are bypassing easily (blocked me to proofread this tweet for example 😅).
This will automatically create massive assymetry and risks in the coming years, just like in cybersecurity, especially now that frontier labs are building wet labs (HF cybersecurity scenario for biology when?).
This is why we created Carbon-X, the best open-source DNA model by midtraining Qwen3-14B on 300B tokens of DNA, English and interleaved data. We also used a hybrid tokenizer that tokenizes English with BPE and DNA with a 6-mer tokenizer.
As a result, Carbon-X outperforms all open language models we evaluated on five benchmarks of DNA understanding and generation. This is a 27-point improvement over Qwen3-14B, showing how much a language model can gain from DNA training. It is also API frontier level at 1,800x less cost than GPT 6.1 Sol!
We hope Carbon-X helps researchers explore DNA in new ways. Releasing it openly means anyone can study its capabilities, limitations, and risks. Let's make the AI biology frontier open-source to make it safe and impactful for the whole world (not just a few companies).
Apache 2.0 Model on @huggingface: https://t.co/RRc1VLSrGT