---
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.