Update README.md

README.md

@@ -248,28 +248,15 @@ instruction data about 5; of English, prokaryote and mRNA-splice the run read on
 *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.
 
-### How to train on it
-
-1. Tokenize `text` with [`HuggingFaceBio/carbon-x`](https://huggingface.co/HuggingFaceBio/carbon-x). It detects DNA in
-   text and encodes it with its 6-mer DNA vocabulary. DNA configs already wrap sequences in `<dna>…</dna>`. To
-   tokenize a source straight from the Hub, read `hf://datasets/HuggingFaceBio/FineDNA/mid/<source>`.
-2. Sample configs by the *share of training* column. In Megatron, that is the weight of each `--data-path` prefix.
-   Within a config, split its weight across shards in proportion to their size, so every document is equally likely.
-3. Train for 302B tokens: 72,000 iterations at a global batch of 512 x 8,192 tokens, from the frozen-DNA-embedding
-   Qwen3-14B checkpoint (a short first stage that trains only the new DNA embeddings). The full recipe, including the vocabulary extension and that first stage, is in the
-   [Carbon-X midtraining recipe](https://github.com/huggingface/carbon/tree/main/carbon-x/training/midtraining).
-   Training longer with the same weights repeats the same data more often; for more unique DNA or English, add data
-   from the original datasets linked below.
+The full training setup we used for Carbon-X are in the
+[Carbon-X midtraining recipe](https://github.com/huggingface/carbon/tree/main/carbon-x/training/midtraining).
 
 ### What is in it
 
-**DNA (55%).** Eukaryote genomic windows from [GENERator](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote),
-up to 100 kbp, in six species groups weighted by the corpus's natural distribution. mRNA, mRNA with splice and promoter
-context, and prokaryote genomes (GTDB and IMG/PR) from [OpenGenome2](https://huggingface.co/datasets/arcinstitute/opengenome2).
-All DNA configs are complete: they hold every sequence the run could sample from. Carbon-X read eukaryote and mRNA
-more than once, and about 28% of prokaryote and
-20% of mRNA-splice.
-The raw sequences are also in [`HuggingFaceBio/carbon-pretraining-corpus`](https://huggingface.co/datasets/HuggingFaceBio/carbon-pretraining-corpus).
+**DNA.** Eukaryote genomic windows from [GENERator](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote),
+up to 100 kbp, in six species groups. mRNA, mRNA with splice and promoter context, and prokaryote genomes (GTDB and
+IMG/PR) from [OpenGenome2](https://huggingface.co/datasets/arcinstitute/opengenome2). The raw sequences are also in
+[`HuggingFaceBio/carbon-pretraining-corpus`](https://huggingface.co/datasets/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>`;
@@ -278,19 +265,23 @@ Here they are formatted exactly as they were trained on:
 - mRNA and prokaryote sequences are uppercased, splice-junction markers (`@`) are removed, and mRNA longer than
   100,000 bases is dropped.
 
-**Biology text (15%).** 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/`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/tree/main/mid/pipeline).
+**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/`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/tree/main/mid/pipeline).
 
-1. **Find documents that contain DNA:** [`1_regex_filter.py`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/blob/main/mid/pipeline/1_regex_filter.py). We ran a regex filter over
-   FineWeb-Edu, FinePDFs, PubMed Central, bioRxiv and Biostars. A document is kept when
+1. **Find documents that contain DNA:**
+   [`1_regex_filter.py`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/blob/main/mid/pipeline/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`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/blob/main/mid/pipeline/2_p90_split.py), which finds DNA with
-   [`tag_dna_sequences.py`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/blob/main/mid/pipeline/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; the
-   p90 documents carry real sequences. In our ablations, training on them instead of every regex hit improved DNA
-   benchmarks, so in the mix they get as much weight as `mid_bio_rest` and are read about seven times.
+2. **Split by DNA length:**
+   [`2_p90_split.py`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/blob/main/mid/pipeline/2_p90_split.py),
+   which finds DNA with
+   [`tag_dna_sequences.py`](https://huggingface.co/datasets/HuggingFaceBio/FineDNA/blob/main/mid/pipeline/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 |
 |---|---|
@@ -299,26 +290,15 @@ paper configs were built in two steps; the code is in [`mid/pipeline/`](https://
 | `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 (20%).** [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) (100BT sample),
+**English.** [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) (100BT sample),
 [DCLM](https://huggingface.co/datasets/HuggingFaceFW/dclm_100BT-shuffled) and
 [FinePDFs-Edu](https://huggingface.co/datasets/HuggingFaceFW/finepdfs_edu_100BT-shuffled), to keep the model's English.
 
-**Instruction (10%).** [Nemotron Instruction-Following-Chat v3](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3)
-(chat and instruction-following splits) and [v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-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.
-
-### How the mix matches the run
-
-Every config was rebuilt from its source and checked against the document counts in the training run's log: each
-holds everything the run sampled from for that source. How much of each the run read is set by the weights, as in the table above.
-Where the run read only part of a config, which documents Megatron happened to draw cannot be recovered, but training
-with the same weights draws the same amount from the same pool.
-
-Two pieces of data are included although they never reached the run, so that the pools are complete:
-1,720,988 documents of `mid_dna_eukaryote_invertebrate` and
-1,726,948 of `mid_english_dclm`, from tokenization shards that were missing when Carbon-X
-trained.
+**Instruction.** [Nemotron Instruction-Following-Chat v3](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3)
+(chat and instruction-following splits) and
+[v2](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-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.