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README.md

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+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- robotics
+- text-embeddings
+- t5
+- wan2.2
+size_categories:
+- 1M<n<10M
+---
+
+# RoboTwin 2.0 3D — T5 Text Embedding Cache
+
+Precomputed [UMT5-XXL](https://huggingface.co/google/umt5-xxl) text embeddings for the
+**1,039,891 unique task prompts** of the RoboTwin 2.0 3D dataset
+([`flex-pi/robotwin_3d`](https://huggingface.co/datasets/flex-pi/robotwin_3d)), as consumed by
+Wan2.2-TI2V-5B / FastWAM.
+
+Precomputing these takes substantial GPU time; this cache lets you skip it.
+
+## Contents
+
+| Path | Description |
+|---|---|
+| `shards/shard_NNNNN.safetensors` | 520 shards, 2,000 prompts each (last one 1,891), ~2.10 GB per shard |
+| `manifest.txt` | The 1,039,891 prompt hashes, **sorted**, one per line — line `i` is row `i % 2000` of shard `i // 2000` |
+
+Each shard holds two tensors, row-aligned:
+
+| Tensor | Shape | Dtype |
+|---|---|---|
+| `contexts` | `[N, 128, 4096]` | `bfloat16` |
+| `masks` | `[N, 128]` | `bool` |
+
+The shard's `__metadata__["keys"]` is a JSON list of that shard's N hashes, in row order.
+
+The key for a prompt is `sha256(prompt.encode("utf-8")).hexdigest()`. Prompts themselves live in
+`meta/tasks.jsonl` of the main dataset.
+
+`masks` marks the valid tokens (mean 40.9 of 128). Values beyond the mask are **not** zero — they are
+the raw T5 outputs. Zero them yourself if your model does not apply the mask.
+
+## Usage
+
+Random access without downloading everything — resolve one prompt to its shard, fetch only that shard:
+
+```python
+import bisect, hashlib, json
+from huggingface_hub import hf_hub_download
+from safetensors import safe_open
+
+REPO = "flex-pi/robotwin_3d_text_embeds_cache"
+SHARD = 2000
+
+manifest = hf_hub_download(REPO, "manifest.txt", repo_type="dataset")
+keys = open(manifest).read().split()          # sorted
+
+def get(prompt):
+    h = hashlib.sha256(prompt.encode("utf-8")).hexdigest()
+    i = bisect.bisect_left(keys, h)
+    if i == len(keys) or keys[i] != h:
+        raise KeyError(prompt)
+    path = hf_hub_download(REPO, f"shards/shard_{i // SHARD:05d}.safetensors", repo_type="dataset")
+    with safe_open(path, framework="pt") as f:
+        row = i % SHARD
+        return f.get_slice("contexts")[row], f.get_slice("masks")[row]
+
+context, mask = get("Lift the medium-sized green bottle ensuring it remains upright.")
+print(context.shape, context.dtype, int(mask.sum()))   # (128, 4096) torch.bfloat16 49
+```
+
+`safe_open` + `get_slice` reads only the requested row, so this does not load the whole 2 GB shard
+into memory.
+
+### Rebuilding the original per-prompt `.pt` layout
+
+Some code expects `{cache_dir}/{sha256}.t5_len128.wan22ti2v5b.pt` holding `{"context", "mask"}`:
+
+```python
+import json, os, torch
+from safetensors import safe_open
+
+def unpack(shard_path, out_dir):
+    os.makedirs(out_dir, exist_ok=True)
+    with safe_open(shard_path, framework="pt") as f:
+        ks = json.loads(f.metadata()["keys"])
+        C, M = f.get_slice("contexts"), f.get_slice("masks")
+        for r, k in enumerate(ks):
+            torch.save({"context": C[r], "mask": M[r]},
+                       os.path.join(out_dir, f"{k}.t5_len128.wan22ti2v5b.pt"))
+```
+
+Note this expands to ~1 TB across 1,039,891 files.
+
+## Provenance
+
+Repacked byte-exactly from the original per-prompt `.pt` cache — tensors are bit-identical, verified
+by round-trip comparison against the source files. Encoder: UMT5-XXL, context length 128.