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XBridge Evaluation Caches (Llama-3.1-8B sender)
Precomputed evaluation caches for the 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:
huggingface-cli download wyangw/xbridge-eval-cache --repo-type dataset --local-dir precomputed
Then run Option A from the repo README.
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.
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