Rewrite model card: BART, MIT license, links to datasets/scripts/blog

README.md

@@ -1,13 +1,91 @@
 ---
+license: mit
 library_name: pytorch
+language:
+- en
 tags:
 - nanochat
 - language-model
+- pre-1930
+- historical
+- vintage
 ---
-# think.nano
 
-Durable model artifacts for lineage-aware nanochat experiments.
+# BART
 
-Each base experiment owns its tokenizer and base checkpoints. SFT runs are
-nested under their exact base parent, and post-training runs are nested under
-their exact SFT parent. Legacy paths are retained under `archive/pre-lineage-v1`.
+A language model whose knowledge ends in **1930**. BART is trained from scratch on pre-1930
+public-domain books — no modern text anywhere in the corpus — to study what a model learns when its
+entire world is historical.
+
+📝 [Read the write-up](https://www.unboundedlab.com/blog/bart) · 🌐 [Unbounded Labs](https://unboundedlab.com)
+
+## The model
+
+`experiments/Think.Unbounded-d32-v2mix-cont`
+
+| | |
+|---|---|
+| Parameters | 1,677,724,672 (~1.68B) |
+| Depth | 32 |
+| Context | 4,096 (SSSL window pattern) |
+| Vocab | 32,768 (`rustbpe`) |
+| Training tokens | 20.13B |
+| Precision | fp8 (tensorwise) |
+| Midtrain schedule | 0% → 21% → 45% |
+
+It branches from `Think.Unbounded-d32` at step 5500, carrying the optimizer state, and continues on
+the corrected token-based midtrain mixtures. The parent run used mixtures whose ratios had been
+computed by document count, which under-delivered midtrain content badly — the continuation is the
+repair.
+
+## What it's for
+
+BART exists to answer what a model trained *only* on historical text actually knows and how it
+writes. Because nothing after 1930 appears in training, it can be probed for period-authentic
+prose and for genuine historical reasoning without the usual contamination from modern sources —
+and it can be measured on whether modern concepts leak in at all. The companion benchmarks
+([vintage-core](https://huggingface.co/datasets/jbduran/vintage-core),
+[vintage-gsm8k](https://huggingface.co/datasets/jbduran/vintage-gsm8k),
+[history-event-reconstruction](https://huggingface.co/datasets/jbduran/history-event-reconstruction))
+are period-adapted so the model isn't penalized for lacking knowledge it was never given.
+
+This is a research artifact, not a deployable chat model — it ships as raw nanochat checkpoints.
+
+## Repository layout
+
+```
+experiments/    39 runs — each owns its tokenizer and base checkpoints
+evaluations/    vintage-core results across models
+archive/        legacy paths (pre-lineage-v1)
+```
+
+SFT runs nest under their exact base parent; post-training runs nest under their exact SFT parent.
+
+**Reading experiment names:** `d12`/`d24`/`d32` is depth · `r11`–`r30` is the target
+parameter-to-data ratio · `ctx4096`/`ctx8192` is sequence length · `sssl` is the window pattern ·
+`fulltok`/`randtok` is the tokenizer variant. The prefix tells you the corpus generation:
+`think-` = v1, `thinkcleaned-` = v2, `clean1930s-` = v3, `Think.Unbounded-` = v3 plus midtraining.
+
+## Training data
+
+| Stage | Dataset |
+|---|---|
+| Pretraining | [bart-dataset-v3](https://huggingface.co/datasets/jbduran/bart-dataset-v3) ← [v2](https://huggingface.co/datasets/jbduran/bart-dataset-v2) ← [v1](https://huggingface.co/datasets/jbduran/bart-dataset-v1) |
+| Midtraining | [bart-midtrain](https://huggingface.co/datasets/zachnorton03/bart-midtrain) |
+
+Pipelines: [bart-dataset-scripts](https://github.com/OwenVoorhees/bart-dataset-scripts) ·
+[bart-midtrain-scripts](https://github.com/OwenVoorhees/bart-midtrain-scripts)
+
+> **Note.** The `config.json` in each experiment records the repo and dataset names as they were at
+> training time (`jbduran/think.nano`, `think-dataset-clean-1930s`, `think-midtrain`). Those still
+> resolve — the Hub redirects renamed repos — but the midtrain mixture subfolders they point at
+> (`mixed/v2/ratio_21/data`) have since moved to `mixtures/v2-by-tokens/ratio_21/data`. The configs
+> are left as historical records of each run rather than rewritten.
+
+## License
+
+MIT
+
+---
+
+Built by [Unbounded Labs](https://unboundedlab.com).