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README.md

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+---
+license: apache-2.0
+task_categories:
+- question-answering
+tags:
+- xbridge
+- llama
+- eval-cache
+---
+
+# XBridge Evaluation Caches (Llama-3.1-8B sender)
+
+Precomputed evaluation caches for the [XBridge](https://github.com/WooseongYang/XBridge) repo.
+Each file stores, for a fixed set of eval items per task, the sender's
+(Llama-3.1-8B-Instruct) last-layer hidden states `H_S` and attention-sorted
+context token IDs — the inputs the Latent Enrichment Bridge (LEB) module
+queries at inference time. This is a cache of a Llama forward pass, not a
+copy of the underlying QA datasets themselves.
+
+| File | Task | Source dataset | Eval items |
+|---|---|---|---|
+| `countries_task_split.pt` | countries | local (`dataloader/data/countries.jsonl`) | 100 |
+| `tipsheets_task_split.pt` | tipsheets | local (`dataloader/data/tipsheets.jsonl`) | 200 |
+| `twowikimqa_task_split.pt` | twowikimqa | `Xnhyacinth/LongBench` (`2wikimqa`, test) | 100 |
+| `multifieldqa_en_task_split.pt` | multifieldqa_en | `Xnhyacinth/LongBench` (`multifieldqa_en`, test) | 75 |
+| `hotpotqa_attnselect.pt` | hotpotqa | `hotpot_qa` (`distractor`, train, seed=42 shuffle, first 200) | 200 |
+
+## Usage
+
+Download into `precomputed/` at the repo root:
+```bash
+huggingface-cli download wyangw/xbridge-eval-cache --repo-type dataset --local-dir precomputed
+```
+
+Then run Option A from the [repo README](https://github.com/WooseongYang/XBridge#reproduction).
+
+## Regenerating from scratch
+
+Each file can also be rebuilt from public sources using the scripts in the repo
+(`scripts/precompute_task_split.py`, `scripts/precompute_tipsheets_split.py`,
+`scripts/precompute_hotpotqa_eval.py`) — requires `meta-llama/Llama-3.1-8B-Instruct`
+access (gated on HF) and a GPU. See the repo README for exact commands.