Upload README.md with huggingface_hub

README.md

@@ -102,3 +102,87 @@ configs:
   - split: train
     path: story/train-*
 ---
+
+# Qwen3.5-27B diverse-sampling — qualitative examples
+
+Side-by-side qualitative outputs from **`Qwen/Qwen3.5-27B`** under several
+prompting methods for diverse sampling, in two domains. One row per prompt; one
+column per method holding that method's list of outputs **for the same prompt**,
+so a row is a direct method-vs-method comparison.
+
+| config | rows (prompts) | methods | outputs per cell |
+|---|---|---|---|
+| `story` | 52 | naive, plan, idea, verbalized_k8, verbalized_k16, gacha | up to 128 stories |
+| `image` | 50 | naive, persona, plan, verbalized_k8, verbalized_k16, gacha | 128 images + expanded captions |
+
+These are the **complete** n=128 cells — not a benchmark, and not curated. No
+filtering, ranking, or cherry-picking: outputs appear in generation order.
+
+Two story cells are ragged rather than exactly 128: `verbalized_k8` (93–128 per
+prompt) and `verbalized_k16` (67–128), because the model returns fewer than `k`
+items in some list calls. Image `gacha` is missing a single render (6,399 of
+6,400).
+
+`persona` is present on `image` but **deliberately absent from `story`** — that
+cell was bad and was dropped.
+
+## Schema
+
+`story` and `image` are separate **configs** (their columns are shaped
+differently, so they cannot be splits of one dataset):
+
+```python
+from datasets import load_dataset
+story = load_dataset("scottgeng00/gacha_examples_test", "story")["train"]
+image = load_dataset("scottgeng00/gacha_examples_test", "image")["train"]
+
+row = story[0]
+row["prompt"]          # 'Write a story titled "It". You decide everything else about it.'
+row["gacha"]           # list[str] — the stories
+row["naive"][0]        # the same prompt, sampled IID
+
+row = image[0]
+row["prompt"]                # original caption the image must depict
+row["caption_instruction"]   # what the LLM was asked to expand ("Write a detailed image generation caption for: ...")
+row["gacha"][0]["image"]     # PIL.Image, 768x768
+row["gacha"][0]["caption"]   # the expanded caption that produced that image
+row["gacha"][0]["sample_idx"]
+```
+
+Shared columns: `prompt_id`, `prompt`, `category` (+ `tier` on story,
+`caption_instruction` on image).
+
+## Provenance
+
+- **Generator:** `Qwen/Qwen3.5-27B`, thinking ON, temperature 1.0, top_p 0.95,
+  top_k 20, min_p 0, presence_penalty 1.5.
+- **Prompts:** story = 52 open-ended creative-writing prompts; image = 50 OneIG
+  captions across 3 categories.
+- **Renderer:** `Qwen/Qwen-Image-2512`, 30 steps, `true_cfg_scale` 4.0,
+  1328×1328, captions anchored to the original caption. Downscaled to 768×768
+  (Lanczos) for this dataset; the 1328² originals are not included.
+
+## Methods
+
+- **naive** — n IID samples of the bare prompt.
+- **persona** — a distinct sampled persona (Nemotron-Personas-USA) per sample as
+  the system turn.
+- **plan** — intent-factored: a plan sampled hot (temperature 1.2), then executed
+  at normal temperature.
+- **idea** — k=64 numbered "creative angles" in one call, one execute call each.
+- **verbalized_k8 / verbalized_k16** — k responses with self-estimated
+  probabilities in one call, repeated to cover n.
+- **gacha** — per sample, compile a schema of decision points from the task, get
+  an options menu per question, roll each choice with a seeded external RNG, then
+  render the rolled brief in one fresh call.
+
+## Known quirk: `naive` image captions
+
+Asked to "write a detailed image generation caption for X", `Qwen3.5-27B`
+sampled directly answers with a **markdown menu of 2–4 alternative prompts**
+("### Option 1: The Cinematic Masterpiece…") in the large majority of cases,
+mean ~2,900 characters. That entire blob was passed to the renderer verbatim,
+exactly as it was in the run. So `naive` images are conditioned on a
+multi-option document rather than a single caption — worth knowing before
+reading the `naive` column as a clean IID image baseline. Other methods emit a
+single caption (mean 195–1,337 chars).