Drop the source and reproduction sections from the card

README.md

@@ -333,14 +333,11 @@ Every mid-training config has the same columns:
 | `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 `mid/` files are copied unchanged from [`HuggingFaceBio/FineDNA-Mid`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA-Mid)
-at revision `663538d6`.
-
 ## SFT
 
-Supervised fine-tuning conversations for a DNA language model: 204,023 rows (203,923 `train`, 100 `test`) drawn
-from 20 source datasets with the Carbon-X SFT mixture weights. `sft_mix` holds exactly the rows the SFT trainer builds
-from that mixture (seed 0), so training on its `train` split reproduces the mixture without any further weighting.
+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")`.
@@ -366,56 +363,6 @@ order, with no test split, e.g. `load_dataset("HuggingFaceBio/FineDNA", "sft_cha
 | `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 |
 
-### Sources
-
-Every source is the `_decontaminated_ngram_dna_homology` variant of the config below, `train` split. Each is shuffled
-with seed 0 and truncated to `int(rows × weight)`; the result is concatenated, shuffled with seed 0 and split into
-`train` and a 100-row `test` set with seed 0.
-
-| dataset | config | revision | rows | weight | rows used | domain |
-| --- | --- | --- | ---: | ---: | ---: | --- |
-| `HuggingFaceBio/sequence-recovery-qa` | `varlen` | `e09cae72` | 58,754 | 1.0 | 58,754 | `sequence_continuation` |
-| `HuggingFaceBio/sequence-recovery-qa` | `rewrite` | `e09cae72` | 24,730 | 0.1266728 | 3,132 | `dna_computation` |
-| `HuggingFaceBio/carbon-format-if` | `format_if_v2` | `390592d0` | 5,196 | 1.0 | 5,196 | `instruction_following` |
-| `HuggingFaceBio/basic-dna-sft-data-v3` | `all_diversify_30pct` | `72dc7e73` | 63,848 | 0.151 | 9,641 | `dna_computation` |
-| `HuggingFaceBio/carbon-sequence-contextfree-v8-regex-10k` | `final` | `5f17e938` | 10,000 | 0.3 | 3,000 | `sequence_analysis` |
-| `HuggingFaceBio/carbon-gemini-repair-sft` | `default` | `63cccfc7` | 11,907 | 0.3 | 3,572 | `sequence_analysis` |
-| `HuggingFaceBio/carbon-sequence-contextfree-v6-1000` | `final` | `e2792417` | 992 | 1.0 | 992 | `sequence_analysis` |
-| `HuggingFaceBio/carbon-bioSE-sft` | `final` | `31dc6d39` | 2,072 | 1.0 | 2,072 | `biology_qa` |
-| `HuggingFaceBio/carbon-biostars-sft-v2` | `final` | `5014a6b6` | 1,327 | 1.0 | 1,327 | `biology_qa` |
-| `HuggingFaceBio/wild-bio-prompts-luna-generated-verified-v1` | `final` | `35440c49` | 842 | 1.0 | 842 | `biology_qa` |
-| `HuggingFaceBio/seqqa2-luna-sft-structurednotags-dna8-clean-v3` | `final` | `38644f1f` | 853 | 1.0 | 853 | `dna_computation` |
-| `HuggingFaceBio/genomeqa-sft` | `enhancer_promoter_diversify_10pct` | `398703a5` | 10,559 | 0.4 | 4,223 | `sequence_classification` |
-| `HuggingFaceBio/genomeqa-sft` | `eukaryote_cls_diversify_10pct` | `398703a5` | 9,143 | 0.4 | 3,657 | `sequence_classification` |
-| `HuggingFaceBio/genomeqa-sft` | `tf_motif_diversify_10pct` | `398703a5` | 13,058 | 0.4 | 5,223 | `sequence_classification` |
-| `HuggingFaceBio/genomeqa-sft` | `tfbs_diversify_10pct` | `398703a5` | 8,262 | 0.4 | 3,304 | `sequence_classification` |
-| `HuggingFaceBio/vepqa-sft-draft-loubna` | `templated_reasoning_multiview_v2_all` | `76e39513` | 74,484 | 0.769 | 57,278 | `variant_effect_prediction` |
-| `HuggingFaceBio/nemotron-data-filtered-carbon` | `biology_en_max8704` | `50df980c` | 3,656 | 0.1 | 365 | `biology_qa` |
-| `HuggingFaceBio/nemotron-data-filtered-carbon` | `instruction_following_bio_en_max8704` | `50df980c` | 709 | 1.0 | 709 | `instruction_following` |
-| `HuggingFaceBio/easy-dna-chat-v2` | `all` | `53923d9a` | 11,502 | 0.7442 | 8,559 | `chat` |
-| `HuggingFaceBio/carbon-dna-skills` | `all` | `d703e0cd` | 39,156 | 0.8 | 31,324 | `dna_computation` |
-
-`carbon-format-if` rows reuse examples from basic-dna, seqqa2 and sequence-recovery `varlen` with a format instruction
-added, so they are labelled by purpose (`instruction_following`) rather than content.
-
-### Reproducing
-
-The `sft/` files are copied unchanged from [`HuggingFaceBio/FineDNA-SFT`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA-SFT)
-at revision `400d761b`, which was built with:
-
-```bash
-python sft/scripts/create_dataset.py --output_dataset HuggingFaceBio/FineDNA-SFT
-```
-
-[`sft/scripts/create_dataset.py`](sft/scripts/create_dataset.py) pins the full revision of every source above and needs
-read access to them (all are private). Its output matches the trainer's mixture loader row for row on both splits.
-
-The domain configs are then split from the mixture's `train` split at revision `60b6e0d5`:
-
-```bash
-python sft/scripts/create_subset.py --dataset HuggingFaceBio/FineDNA-SFT --revision 60b6e0d5c9af34bd97de521cc2cf5fb3e977771c
-```
-
 ## RL
 
 Verifiable prompts for reinforcement learning of a DNA language model: 98,433 rows in one `train` split, covering
@@ -455,29 +402,7 @@ every prompt (3,260).
 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.
 
-### Source
-
-| dataset | config | revision | rows |
-| --- | --- | --- | ---: |
-| `HuggingFaceBio/Rollouts-Carbon-14B-SFT-v36.52-step-000019119` | `blend_all_v0` | `205d8b22` | 98,433 |
-
-Only the six columns the trainer reads are kept, and `domain` is added from `task_group`. The rows are shuffled twice
-with seed 0, matching the trainer's mixture loader (once per source, once after concatenation).
-
-### Reproducing
-
-The `rl/` files are copied unchanged from [`HuggingFaceBio/FineDNA-RL`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA-RL)
-at revision `2315ddc7`, which was built with:
-
-```bash
-python rl/scripts/create_dataset.py --output_dataset HuggingFaceBio/FineDNA-RL
-```
-
-[`rl/scripts/create_dataset.py`](rl/scripts/create_dataset.py) pins the full source revision and needs read access to
-it (the source is private). Its output matches the trainer's mixture loader row for row.
-
 ## License
 
 Each mid-training config keeps the license and terms of its source: GENERator, OpenGenome2, FineWeb-Edu, DCLM,
-FinePDFs-Edu, PubMed Central, bioRxiv, Biostars, and Nemotron. The SFT and RL rows are built from the sources listed in
-their sections. Check the source dataset before use.
+FinePDFs-Edu, PubMed Central, bioRxiv, Biostars, and Nemotron. Check the source dataset before use.