Drop the SFT test split and the split counts from the card

README.md

@@ -123,8 +123,6 @@ configs:
   data_files:
   - split: train
     path: sft/mix/train-*.parquet
-  - split: test
-    path: sft/mix/test-*.parquet
 - config_name: sft_biology_qa
   data_files:
   - split: train
@@ -170,11 +168,11 @@ configs:
 FineDNA is the training data of [Carbon-X](https://huggingface.co/HuggingFaceBio/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 |
+| 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` |
-| [SFT](#sft) | `sft_mix`, and `sft_*` for each of its 8 domains | 203,923 `train`, 100 `test` | `HuggingFaceBio/Carbon-X-Base` |
-| [RL](#rl) | `rl_mix` | 98,433 `train` | `HuggingFaceBio/Carbon-X-SFT` |
+| [SFT](#sft) | `sft_mix`, and `sft_*` for each of its 8 domains | 203,923 conversations | `HuggingFaceBio/Carbon-X-Base` |
+| [RL](#rl) | `rl_mix` | 98,433 prompts | `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.
@@ -185,7 +183,7 @@ 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_mix", split="train")
 ds = load_dataset("HuggingFaceBio/FineDNA", "sft_chat", split="train")
 # the RL blend
 ds = load_dataset("HuggingFaceBio/FineDNA", "rl_mix", split="train")
@@ -315,12 +313,11 @@ Biology configs also have a `validation` split: the documents held out of traini
 
 ## 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.
+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` `train` rows of that domain in the same
-order, with no test split, e.g. `load_dataset("HuggingFaceBio/FineDNA", "sft_chat")`.
+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")`.
 
 ### Columns
 
@@ -332,20 +329,20 @@ order, with no test split, e.g. `load_dataset("HuggingFaceBio/FineDNA", "sft_cha
 
 ### Domains
 
-| config | train | test (in `sft_mix`) | what the rows teach |
-| --- | ---: | ---: | --- |
-| `sft_biology_qa` | 4,603 | 3 | genetics, molecular biology and bioinformatics questions from Biology StackExchange, Biostars, real user chats and Nemotron chat data |
-| `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_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_instruction_following` | 5,904 | 1 | answering in the format the user asks for, and following format constraints on biology prompts |
-| `sft_sequence_analysis` | 7,561 | 3 | free-text, mostly multi-turn discussion of a pasted sequence: primer checks, annotation, coding potential, troubleshooting |
-| `sft_sequence_classification` | 16,396 | 11 | multiple-choice functional genomics: promoter vs enhancer, species of origin, TF motifs, TF binding sites |
-| `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 |
+| 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 in one `train` split, covering
+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

sft/mix/test-00000-of-00001.parquet

@@ -1,3 +0,0 @@
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