Datasets:

HuggingFaceBio
/

FineDNA

@@ -171,8 +171,8 @@ Qwen3-14B to read DNA, for all three of its training stages. Each stage is a gro
 | stage | configs | size | starts from |
 |---|---|---:|---|
 | [Mid-training](#mid-training) | `mid_*`, one per source (23) | 514.30B tokens, of which the run read 302B | [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B) |
-| [SFT](#sft) | `sft_mix`, and `sft_*` for each of its 8 domains | 203,923 conversations | [`HuggingFaceBio/Carbon-X-Base`](https://huggingface.co/HuggingFaceBio/Carbon-X-Base) |
-| [RL](#rl) | `rl_mix` | 98,433 prompts | [`HuggingFaceBio/Carbon-X-SFT`](https://huggingface.co/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](#the-mix) below.
@@ -313,8 +313,9 @@ Biology configs also have a `validation` split: the documents held out of traini
 ## SFT
-Supervised fine-tuning conversations for a DNA language model: 203,923 rows. `sft_mix` holds exactly the rows the
-SFT trainer built from the Carbon-X SFT mixture, so training on it reproduces the mixture without any further weighting.
 Each domain also has its own config, `sft_<domain>`, holding the `sft_mix` rows of that domain in the same order, e.g.
 `load_dataset("HuggingFaceBio/FineDNA", "sft_chat")`.
@@ -329,20 +330,23 @@ Each domain also has its own config, `sft_<domain>`, holding the `sft_mix` rows
 ### Domains
-| config | rows | what the rows teach |
-| --- | ---: | --- |
-| `sft_biology_qa` | 4,603 | genetics, molecular biology and bioinformatics questions from Biology StackExchange, Biostars, real user chats and Nemotron chat data |
-| `sft_chat` | 8,553 | multi-turn conversations on molecular-biology concepts, the assistant's identity, honesty about its limits, and pushing back on false claims |
-| `sft_dna_computation` | 44,925 | answers computed exactly from the sequence: counting, GC content, melting temperature, translation, restriction sites, base lookup, coordinates, edits, rewriting |
-| `sft_instruction_following` | 5,904 | answering in the format the user asks for, and following format constraints on biology prompts |
-| `sft_sequence_analysis` | 7,561 | free-text, mostly multi-turn discussion of a pasted sequence: primer checks, annotation, coding potential, troubleshooting |
-| `sft_sequence_classification` | 16,396 | multiple-choice functional genomics: promoter vs enhancer, species of origin, TF motifs, TF binding sites |
-| `sft_sequence_continuation` | 58,727 | predict the next 1–32 nucleotides after a 4–8 kb eukaryotic context |
-| `sft_variant_effect_prediction` | 57,254 | decide whether a single-nucleotide variant is benign or pathogenic from its sequence window, with templated reasoning |
 ## RL
-Verifiable prompts for reinforcement learning of a DNA language model: 98,433 rows, 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
@@ -365,20 +369,23 @@ every prompt (3,260).
 ### 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

 | stage | configs | size | starts from |
 |---|---|---:|---|
 | [Mid-training](#mid-training) | `mid_*`, one per source (23) | 514.30B tokens, of which the run read 302B | [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B) |
+| [SFT](#sft) | `sft_mix`, and `sft_*` for each of its 8 domains | 203,923 conversations, 230.3M tokens | [`HuggingFaceBio/Carbon-X-Base`](https://huggingface.co/HuggingFaceBio/Carbon-X-Base) |
+| [RL](#rl) | `rl_mix` | 98,433 prompts, 61.1M tokens | [`HuggingFaceBio/Carbon-X-SFT`](https://huggingface.co/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](#the-mix) below.
 ## SFT
+Supervised fine-tuning conversations for a DNA language model: 203,923 rows and 230.3M tokens, 27.4M of them in
+assistant turns. `sft_mix` holds exactly the rows the SFT trainer built from the Carbon-X SFT mixture, so training on
+it reproduces the mixture without any further weighting.
 Each domain also has its own config, `sft_<domain>`, holding the `sft_mix` rows of that domain in the same order, e.g.
 `load_dataset("HuggingFaceBio/FineDNA", "sft_chat")`.
 ### Domains
+| config | rows | tokens | assistant tokens | what the rows teach |
+| --- | ---: | ---: | ---: | --- |
+| `sft_biology_qa` | 4,603 | 2.8M | 2.2M | genetics, molecular biology and bioinformatics questions from Biology StackExchange, Biostars, real user chats and Nemotron chat data |
+| `sft_chat` | 8,553 | 2.1M | 1.5M | multi-turn conversations on molecular-biology concepts, the assistant's identity, honesty about its limits, and pushing back on false claims |
+| `sft_dna_computation` | 44,925 | 19.1M | 7.0M | answers computed exactly from the sequence: counting, GC content, melting temperature, translation, restriction sites, base lookup, coordinates, edits, rewriting |
+| `sft_instruction_following` | 5,904 | 3.7M | 2.1M | answering in the format the user asks for, and following format constraints on biology prompts |
+| `sft_sequence_analysis` | 7,561 | 8.7M | 3.6M | free-text, mostly multi-turn discussion of a pasted sequence: primer checks, annotation, coding potential, troubleshooting |
+| `sft_sequence_classification` | 16,396 | 12.8M | 2.0M | multiple-choice functional genomics: promoter vs enhancer, species of origin, TF motifs, TF binding sites |
+| `sft_sequence_continuation` | 58,727 | 64.8M | 0.65M | predict the next 1–32 nucleotides after a 4–8 kb eukaryotic context |
+| `sft_variant_effect_prediction` | 57,254 | 116.2M | 8.2M | decide whether a single-nucleotide variant is benign or pathogenic from its sequence window, with templated reasoning |
+Tokens are counted with the Carbon-X tokenizer and chat template. Assistant tokens are the ones the SFT loss is
+computed on, and no conversation is longer than the 9,984-token training context.
 ## RL
+Verifiable prompts for reinforcement learning of a DNA language model: 98,433 rows and 61.1M prompt tokens, 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
 ### Task groups
+| domain | task group | rows | prompt tokens | what the prompts ask |
+| --- | --- | ---: | ---: | --- |
+| `dna_computation` | `basic_dna` | 30,461 | 2.4M | 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 | 1.1M | exact operations on longer sequences: locate a base or snippet, convert coordinates, compare, edit, translate, design |
+| `dna_computation` | `seqrecqa_position` | 6,000 | 3.1M | 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 | 29.0M | is a coding-sequence or non-SNV variant pathogenic or benign |
+| `variant_effect_prediction` | `vepqa` | 7,426 | 14.3M | is a single-nucleotide variant pathogenic or benign, judged from its sequence window |
+| `sequence_classification` | `genomeqa` | 14,032 | 7.1M | 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 | 3.3M | four- and ten-option biology knowledge questions |
+| `instruction_following` | `if` | 4,000 | 0.65M | 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.
+Prompt tokens are counted with the Carbon-X tokenizer and chat template, as the GRPO trainer renders the prompts
+(thinking disabled). Completions are generated during training, so they are not counted.
 ## License
 FineDNA is released under the Apache 2.0 license. The mid-training configs are built from third-party sources