README.md

metadata

license: apache-2.0
language:
  - en
task_categories:
  - text-generation
tags:
  - biology
  - genomics
  - dna
  - midtraining
  - sft
  - rl
pretty_name: FineDNA
configs:
  - config_name: mid_dna_eukaryote_fungi
    data_files:
      - split: train
        path: mid/dna_eukaryote_fungi/train-*.parquet
  - config_name: mid_dna_eukaryote_protozoa
    data_files:
      - split: train
        path: mid/dna_eukaryote_protozoa/train-*.parquet
  - config_name: mid_dna_eukaryote_plant
    data_files:
      - split: train
        path: mid/dna_eukaryote_plant/train-*.parquet
  - config_name: mid_dna_eukaryote_invertebrate
    data_files:
      - split: train
        path: mid/dna_eukaryote_invertebrate/train-*.parquet
  - config_name: mid_dna_eukaryote_vertebrate_mammalian
    data_files:
      - split: train
        path: mid/dna_eukaryote_vertebrate_mammalian/train-*.parquet
  - config_name: mid_dna_eukaryote_vertebrate_other
    data_files:
      - split: train
        path: mid/dna_eukaryote_vertebrate_other/train-*.parquet
  - config_name: mid_dna_mrna
    data_files:
      - split: train
        path: mid/dna_mrna/train-*.parquet
  - config_name: mid_dna_mrna_splice
    data_files:
      - split: train
        path: mid/dna_mrna_splice/train-*.parquet
  - config_name: mid_dna_prokaryote
    data_files:
      - split: train
        path: mid/dna_prokaryote/train-*.parquet
  - config_name: mid_bio_p90_biorxiv
    data_files:
      - split: train
        path: mid/bio_p90_biorxiv/train-*.parquet
      - split: validation
        path: mid/bio_p90_biorxiv/validation-*.parquet
  - config_name: mid_bio_p90_biostars
    data_files:
      - split: train
        path: mid/bio_p90_biostars/train-*.parquet
      - split: validation
        path: mid/bio_p90_biostars/validation-*.parquet
  - config_name: mid_bio_p90_finepdfs
    data_files:
      - split: train
        path: mid/bio_p90_finepdfs/train-*.parquet
      - split: validation
        path: mid/bio_p90_finepdfs/validation-*.parquet
  - config_name: mid_bio_p90_fineweb
    data_files:
      - split: train
        path: mid/bio_p90_fineweb/train-*.parquet
      - split: validation
        path: mid/bio_p90_fineweb/validation-*.parquet
  - config_name: mid_bio_p90_pubmed
    data_files:
      - split: train
        path: mid/bio_p90_pubmed/train-*.parquet
      - split: validation
        path: mid/bio_p90_pubmed/validation-*.parquet
  - config_name: mid_bio_rest
    data_files:
      - split: train
        path: mid/bio_rest/train-*.parquet
      - split: validation
        path: mid/bio_rest/validation-*.parquet
  - config_name: mid_bio_gemini_fim
    data_files:
      - split: train
        path: mid/bio_gemini_fim/train-*.parquet
  - config_name: mid_bio_gemini_nonfim
    data_files:
      - split: train
        path: mid/bio_gemini_nonfim/train-*.parquet
      - split: validation
        path: mid/bio_gemini_nonfim/validation-*.parquet
  - config_name: mid_english_fineweb_edu
    data_files:
      - split: train
        path: mid/english_fineweb_edu/train-*.parquet
  - config_name: mid_english_dclm
    data_files:
      - split: train
        path: mid/english_dclm/train-*.parquet
  - config_name: mid_english_finepdfs_edu
    data_files:
      - split: train
        path: mid/english_finepdfs_edu/train-*.parquet
  - config_name: mid_instruct_nemotron_chat_v3
    data_files:
      - split: train
        path: mid/instruct_nemotron_chat_v3/train-*.parquet
  - config_name: mid_instruct_nemotron_if_v3
    data_files:
      - split: train
        path: mid/instruct_nemotron_if_v3/train-*.parquet
  - config_name: mid_instruct_nemotron_if_v2
    data_files:
      - split: train
        path: mid/instruct_nemotron_if_v2/train-*.parquet
  - config_name: sft_mix
    data_files:
      - split: train
        path: sft/mix/train-*.parquet
      - split: test
        path: sft/mix/test-*.parquet
  - config_name: sft_sequence_continuation
    data_files:
      - split: train
        path: sft/sequence_continuation/train-*.parquet
  - config_name: sft_variant_effect_prediction
    data_files:
      - split: train
        path: sft/variant_effect_prediction/train-*.parquet
  - config_name: sft_dna_computation
    data_files:
      - split: train
        path: sft/dna_computation/train-*.parquet
  - config_name: sft_sequence_classification
    data_files:
      - split: train
        path: sft/sequence_classification/train-*.parquet
  - config_name: sft_chat
    data_files:
      - split: train
        path: sft/chat/train-*.parquet
  - config_name: sft_sequence_analysis
    data_files:
      - split: train
        path: sft/sequence_analysis/train-*.parquet
  - config_name: sft_instruction_following
    data_files:
      - split: train
        path: sft/instruction_following/train-*.parquet
  - config_name: sft_biology_qa
    data_files:
      - split: train
        path: sft/biology_qa/train-*.parquet
  - config_name: rl_mix
    data_files:
      - split: train
        path: rl/mix/train-*.parquet

FineDNA

FineDNA is the training data of Carbon-X, the model that taught Qwen3-14B to read DNA, for all three of its training stages. Each stage is a group of configs:

stage	configs	rows	starts from
Mid-training	mid_*, one per source (23)	514.30B tokens, of which the run read 302B	Qwen/Qwen3-14B
SFT	sft_mix, and sft_* for each of its 8 domains	203,923 train, 100 test	HuggingFaceBio/Carbon-X-Base
RL	rl_mix	98,433 train	HuggingFaceBio/Carbon-X-SFT

sft_mix and rl_mix are the rows the SFT and GRPO trainers used, in the order they used them. Mid-training has no single mix: Megatron samples the mid_* configs with the weights in the mix below.

from datasets import load_dataset

# one mid-training source; the mid-training configs total about 950 GB, so stream them
ds = load_dataset("HuggingFaceBio/FineDNA", "mid_dna_mrna", split="train", streaming=True)
# the SFT mixture, or one of its domains
ds = load_dataset("HuggingFaceBio/FineDNA", "sft_mix")
ds = load_dataset("HuggingFaceBio/FineDNA", "sft_chat", split="train")
# the RL blend
ds = load_dataset("HuggingFaceBio/FineDNA", "rl_mix", split="train")

The training recipes for every stage are in huggingface/carbon.

Mid-training

The mid_* configs hold every source of the 302B-token mid-training run, one config per source: genomes and transcripts, biology text that contains DNA, English web and PDF text, and instruction data. Their files are under mid/<source>/.

Every config holds all the data Carbon-X sampled from for that source. That is not the whole upstream dataset: the DNA, for example, is GENERator and OpenGenome2 after the filtering described below. The share of training column below is the weight each config gets, and those weights are the recipe: they decide how much of each config the run read, so some configs were read several times and some only in part. Tokenize the text column with the Carbon-X tokenizer, train with these weights, and you get the same data diet as Carbon-X.

Columns

Every mid-training config has the same columns:

column
text	the document, as it was tokenized
id	the source's document id, where it has one
source	the config name without mid_, or for biology text the original source (pubmed, biorxiv, …)
metadata	the source's other fields, as a JSON string

The mix

	unique tokens	share of training in Carbon-X
DNA	181.96B	55%
Biology text	15.75B	15%
English	310.22B	20%
Instruction	6.36B	10%
Total	514.30B	100%

Carbon-X trained on 302B of these 514.30B tokens. Long-DNA biology text was read about 7 times and instruction data about 5; of English, prokaryote and mRNA-splice the run read only a part.

config	documents	tokens	share of training (weight)	tokens read by Carbon-X	passes
mid_dna_eukaryote_fungi	6,607,490	1.88B	1.12%	3.37B	1.79
mid_dna_eukaryote_protozoa	1,257,499	366.8M	0.19%	581.3M	1.58
mid_dna_eukaryote_plant	9,429,321	5.55B	3.08%	9.30B	1.68
mid_dna_eukaryote_invertebrate	8,716,932	13.71B	7.58%	22.90B	1.67
mid_dna_eukaryote_vertebrate_mammalian	7,169,696	18.96B	10.32%	31.16B	1.64
mid_dna_eukaryote_vertebrate_other	12,420,543	29.56B	16.21%	48.95B	1.66
mid_dna_mrna	52,702,454	19.41B	8.80%	26.58B	1.37
mid_dna_mrna_splice	56,877,102	32.93B	2.20%	6.64B	0.20
mid_dna_prokaryote	17,408,059	59.60B	5.50%	16.61B	0.28
mid_bio_p90_biorxiv	7,262	99.1M	0.24%	710.3M	7.17
mid_bio_p90_biostars	2,036	3.1M	0.01%	25.4M	8.19
mid_bio_p90_finepdfs	51,241	1.96B	4.58%	13.84B	7.07
mid_bio_p90_fineweb	8,205	79.7M	0.19%	560.2M	7.03
mid_bio_p90_pubmed	68,330	854.2M	1.99%	6.01B	7.03
mid_bio_rest	914,391	12.16B	7.00%	21.14B	1.74
mid_bio_gemini_fim	56,866	262.8M	0.50%	1.51B	5.74
mid_bio_gemini_nonfim	72,343	333.4M	0.50%	1.51B	4.53
mid_english_fineweb_edu	96,541,686	99.35B	8.00%	24.16B	0.24
mid_english_dclm	88,340,007	111.98B	8.00%	24.16B	0.22
mid_english_finepdfs_edu	17,596,723	98.89B	4.00%	12.08B	0.12
mid_instruct_nemotron_chat_v3	562,376	2.25B	3.53%	10.67B	4.75
mid_instruct_nemotron_if_v3	249,748	632.0M	0.99%	3.00B	4.75
mid_instruct_nemotron_if_v2	1,997,510	3.48B	5.47%	16.53B	4.75

Tokens is the config's size with the Carbon-X tokenizer (estimated from a 2,000-document sample per config). Tokens read by Carbon-X is share x 302B. Passes is how many times over the run read the config: below 1, it read only that fraction of it.

The full training setup we used for Carbon-X are in the Carbon-X midtraining recipe.

What is in it

DNA. Eukaryote genomic windows from GENERator, up to 100 kbp, in six species groups. mRNA, mRNA with splice and promoter context, and prokaryote genomes (GTDB and IMG/PR) from OpenGenome2. The raw sequences are also in HuggingFaceBio/carbon-pretraining-corpus. Here they are formatted exactly as they were trained on:

• every sequence is cut to a multiple of 6 bp (the tokenizer reads 6-mers) and wrapped in <dna>…</dna>;
• eukaryote sequences carry metadata tags half the time, chosen at random: species group and gene type, species group only, or gene type only, e.g. <mammalian_species><protein_coding_region><dna>ATG…</dna>;
• mRNA and prokaryote sequences are uppercased, splice-junction markers (@) are removed, and mRNA longer than 100,000 bases is dropped.

Biology text. Web pages, papers and forum posts that contain DNA, plus synthetic annotations. The web and paper configs were built in two steps; the code is in mid/pipeline/.

1. Find documents that contain DNA: 1_regex_filter.py. We ran a regex filter over FineWeb-Edu, FinePDFs, PubMed Central, bioRxiv and Biostars. A document is kept when
   • it has a run of 15 or more nucleotide letters (A, T, C, G, U, N) using at least 3 different ones,
   • that run is not inside curly braces, which in scraped text is usually LaTeX, and
   • it mentions a molecular-biology keyword (dna, gene, primer, pcr, ...).
2. Split by DNA length: 2_p90_split.py, which finds DNA with tag_dna_sequences.py. Documents whose longest DNA sequence is at least 39 bp, the 90th percentile, form mid_bio_p90_*; everything else forms mid_bio_rest. Short runs are mostly primers and motifs, while the p90 documents carry real sequences. In our ablations, upweighting them improved DNA benchmarks.

config	what it is
mid_bio_p90_biorxiv, mid_bio_p90_biostars, mid_bio_p90_finepdfs, mid_bio_p90_fineweb, mid_bio_p90_pubmed	documents with long DNA sequences, one config per source
mid_bio_rest	the other regex hits from the same five sources, whose longest DNA sequence is under 39 bp
mid_bio_gemini_nonfim	genomic regions described by Gemini next to their sequence
mid_bio_gemini_fim	the same kind of documents in Qwen's fill-in-the-middle format: part of each document is moved to the end, and the model learns to fill it in

English. FineWeb-Edu (100BT sample), DCLM and FinePDFs-Edu, to keep the model's English.

Instruction. Nemotron Instruction-Following-Chat v3 (chat and instruction-following splits) and v2 (chat), rendered with the Qwen3 chat template, reasoning traces kept. Mid-training starts from an instruct model, and this keeps it able to follow instructions.

Biology configs also have a validation split: the documents held out of training to track loss.

SFT

Supervised fine-tuning conversations for a DNA language model: 204,023 rows (203,923 train, 100 test). sft_mix holds exactly the rows the SFT trainer built from the Carbon-X SFT mixture, so training on its train split reproduces the mixture without any further weighting.

Each domain also has its own config, sft_<domain>, holding the sft_mix train rows of that domain in the same order, with no test split, e.g. load_dataset("HuggingFaceBio/FineDNA", "sft_chat").

Columns

• messages: the conversation, as a list of {role, content} turns.
• chat_template_kwargs: {"tokenizer_kwargs": {"auto_dna_tags": <bool>}}, passed per example to apply_chat_template so the DNA tokenizer wraps sequences in <dna>...</dna>. It is false only for the sequence rewriting rows, whose answers are plain text. Keep it when selecting training columns.
• domain: the kind of task, one of the eight below.

Domains

config	train	test (in sft_mix)	what the rows teach
sft_sequence_continuation	58,727	27	predict the next 1–32 nucleotides after a 4–8 kb eukaryotic context
sft_variant_effect_prediction	57,254	24	decide whether a single-nucleotide variant is benign or pathogenic from its sequence window, with templated reasoning
sft_dna_computation	44,925	25	answers computed exactly from the sequence: counting, GC content, melting temperature, translation, restriction sites, base lookup, coordinates, edits, rewriting
sft_sequence_classification	16,396	11	multiple-choice functional genomics: promoter vs enhancer, species of origin, TF motifs, TF binding sites
sft_chat	8,553	6	multi-turn conversations on molecular-biology concepts, the assistant's identity, honesty about its limits, and pushing back on false claims
sft_sequence_analysis	7,561	3	free-text, mostly multi-turn discussion of a pasted sequence: primer checks, annotation, coding potential, troubleshooting
sft_instruction_following	5,904	1	answering in the format the user asks for, and following format constraints on biology prompts
sft_biology_qa	4,603	3	genetics, molecular biology and bioinformatics questions from Biology StackExchange, Biostars, real user chats and Nemotron chat data

RL

Verifiable prompts for reinforcement learning of a DNA language model: 98,433 rows in one train split, covering 80,406 distinct prompts. Every prompt has a gold answer or constraint set that a reward function checks in code. The blend was calibrated on rollouts from Carbon-X-SFT and excludes the prompts that model saw during SFT. It is meant for one epoch. The rows of rl_mix are in the order the GRPO trainer's mixture loader produces (seed 0), so training on it reproduces the blend exactly.

Some prompts appear more than once on purpose, for groups that learn slowly under RL: basic_dna repeats half of each task type (10,153 second copies), bioreason repeats 30% of each subgroup and label (4,614), and dna_skills repeats every prompt (3,260).

Columns

• prompt: the conversation to complete, as a list of {role, content} turns (2,983 rows open with a system turn).
• answer_text: the gold answer that most rewards compare against.
• task_group: which reward scores the row. Each task reward returns None for rows of other groups.
• task_type: the sub-task, used by the basic_dna and seqrecqa_position rewards (null elsewhere).
• verifier_metadata: the constraints an instruction-following reply must meet (if rows only).
• gold_answer_json: the exact answer the DNA-skills verifier checks (dna_skills rows only).
• domain: the kind of task. It uses the same labels as the SFT domains. Five of the eight occur here because the blend has no chat, sequence-analysis or sequence-continuation prompts.

Task groups

domain	task group	rows	what the prompts ask
dna_computation	basic_dna	30,461	short-sequence computations with code-checked answers: base counts, melting temperature, restriction fragments, CRISPR cut sites, translation, motif spacing
dna_computation	dna_skills	6,520	exact operations on longer sequences: locate a base or snippet, convert coordinates, compare, edit, translate, design
dna_computation	seqrecqa_position	6,000	localisation: the position of a base, 6-base block, variant block, 12-base span, single-base difference or snippet
variant_effect_prediction	bioreason	19,996	is a coding-sequence or non-SNV variant pathogenic or benign
variant_effect_prediction	vepqa	7,426	is a single-nucleotide variant pathogenic or benign, judged from its sequence window
sequence_classification	genomeqa	14,032	multiple-choice functional genomics: promoter vs enhancer, species of origin, TF motifs and binding sites, splice sites, histone marks
biology_qa	bio_mcq	9,998	four- and ten-option biology knowledge questions
instruction_following	if	4,000	prompts with verifiable formatting constraints

In total, dna_computation has 42,981 rows, variant_effect_prediction 27,422, sequence_classification 14,032, biology_qa 9,998 and instruction_following 4,000.

License

FineDNA is released under the Apache 2.0 license. The mid-training configs are built from third-party sources (GENERator, OpenGenome2, FineWeb-Edu, DCLM, FinePDFs-Edu, PubMed Central, bioRxiv, Biostars and Nemotron), which keep their own licenses and terms. Check the source dataset before use.

Citation

FineDNA was built for Carbon-X, a joint collaboration between the research teams at Hugging Face, Zhongguancun Academy, and the Telethon Institute of Genetics and Medicine/University of Naples “Federico II”. If you find it useful, please cite it as:

@misc{benallal2026finedna,
  title        = {FineDNA},
  author       = {Ben Allal, Loubna and Tunstall, Lewis and Aubakirova, Dana and Fiusco, Maurizio and Beeching, Edward and Rasul, Kashif and von Werra, Leandro and Li, Qiuyi and di Bernardo, Diego},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/HuggingFaceBio/FineDNA}}
}